def build_network(self):
# Building 'AlexNet'
# https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py
# https://github.com/DT42/squeezenet_demo
# https://github.com/yhenon/pysqueezenet/blob/master/squeezenet.py
print('[+] Building CNN')
self.network = input_data(shape = [None, SIZE_FACE, SIZE_FACE, 1])
self.network = conv_2d(self.network, 96, 11, strides = 4, activation = 'relu')
self.network = max_pool_2d(self.network, 3, strides = 2)
self.network = local_response_normalization(self.network)
self.network = conv_2d(self.network, 256, 5, activation = 'relu')
self.network = max_pool_2d(self.network, 3, strides = 2)
self.network = local_response_normalization(self.network)
self.network = conv_2d(self.network, 256, 3, activation = 'relu')
self.network = max_pool_2d(self.network, 3, strides = 2)
self.network = local_response_normalization(self.network)
self.network = fully_connected(self.network, 1024, activation = 'tanh')
self.network = dropout(self.network, 0.5)
self.network = fully_connected(self.network, 1024, activation = 'tanh')
self.network = dropout(self.network, 0.5)
self.network = fully_connected(self.network, len(EMOTIONS), activation = 'softmax')
self.network = regression(self.network,
optimizer = 'momentum',
loss = 'categorical_crossentropy')
self.model = tflearn.DNN(
self.network,
checkpoint_path = SAVE_DIRECTORY + '/alexnet_mood_recognition',
max_checkpoints = 1,
tensorboard_verbose = 2
)
self.load_model()
def build_network(self, input, output, is_svm= False, scope='R-CNN',is_training=True, keep_prob=0.5):
with tf.variable_scope(scope):
with slim.arg_scope([slim.fully_connected, slim.conv2d],
activation_fn=nn_ops.relu,
weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
weights_regularizer=slim.l2_regularizer(0.0005)):
net = slim.conv2d(input, 96, 11, stride=4, scope='conv_1')
net = slim.max_pool2d(net, 3, stride=2, scope='pool_2')
net = local_response_normalization(net)
net = slim.conv2d(net, 256, 5, scope='conv_3')
net = slim.max_pool2d(net, 3, stride=2, scope='pool_2')
net = local_response_normalization(net)
net = slim.conv2d(net, 384, 3, scope='conv_4')
net = slim.conv2d(net, 384, 3, scope='conv_5')
net = slim.conv2d(net, 256, 3, scope='conv_6')
net = slim.max_pool2d(net, 3, stride=2, scope='pool_7')
net = local_response_normalization(net)
net = slim.flatten(net, scope='flat_32')
net = slim.fully_connected(net, 4096, activation_fn=self.tanh(), scope='fc_8')
net = slim.dropout(net, keep_prob=keep_prob,is_training=is_training, scope='dropout9')
net = slim.fully_connected(net, 4096, activation_fn=self.tanh(), scope='fc_10')
if is_svm:
return net
net = slim.dropout(net, keep_prob=keep_prob, is_training=is_training, scope='dropout11')
net = slim.fully_connected(net, output, activation_fn=self.softmax(), scope='fc_11')
return net
def alexnet():
X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))
# Building 'AlexNet'
network = input_data(shape=[None, 227, 227, 3])
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 17, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
# Training
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=2)
model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True,
show_metric=True, batch_size=64, snapshot_step=200,
snapshot_epoch=False, run_id='alexnet')
def _model1():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 32, 3, strides = 4, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, strides = 2, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, len(Y[0]), activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target')
model = tflearn.DNN(network, tensorboard_verbose=3)
model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest),
snapshot_step=500, show_metric=True, batch_size=batchNum, shuffle=True, run_id=_id + 'artClassification')
if modelStore: model.save(_id + '-model.tflearn')
def cnn():
X, Y, testX, testY = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
# Building convolutional network
network = input_data(shape=[None, 28, 28, 1], name='input')
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit({'input': X}, {'target': Y}, n_epoch=20,
validation_set=({'input': testX}, {'target': testY}),
snapshot_step=100, show_metric=True, run_id='cnn_demo')
def alexnet(width, height, lr, output=3):
network = input_data(shape=[None, width, height, 1], name='input')
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
def do_cnn_doc2vec_2d(trainX, testX, trainY, testY):
print "CNN and doc2vec 2d"
trainX = trainX.reshape([-1, max_features, max_document_length, 1])
testX = testX.reshape([-1, max_features, max_document_length, 1])
# Building convolutional network
network = input_data(shape=[None, max_features, max_document_length, 1], name='input')
network = conv_2d(network, 16, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit({'input': trainX}, {'target': trainY}, n_epoch=20,
validation_set=({'input': testX}, {'target': testY}),
snapshot_step=100, show_metric=True, run_id='review')
def _model3():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
network = input_data(shape=[None, inputSize, inputSize, dim],
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, len(yTest[0]), activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
print('Model has been made!!!?')
# Training
model = tflearn.DNN(network, checkpoint_path='model_densenet_cifar10',
max_checkpoints=10, tensorboard_verbose=0,
clip_gradients=0.)
model.load(_path)
pred = model.predict(xTest)
df = pd.DataFrame(pred)
df.to_csv(_path + ".csv")
newList = pred.copy()
newList = convert2(newList)
if _CSV: makeCSV(newList)
pred = convert2(pred)
pred = convert3(pred)
yTest = convert3(yTest)
print(metrics.confusion_matrix(yTest, pred))
print(metrics.classification_report(yTest, pred))
print('Accuracy', accuracy_score(yTest, pred))
print()
if _wrFile: writeTest(pred)
def make_core_network(network):
network = tflearn.reshape(network, [-1, 28, 28, 1], name="reshape")
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 10, activation='softmax')
return network
def do_cnn_word2vec_2d(trainX, testX, trainY, testY):
global max_features
global max_document_length
print "CNN and word2vec2d"
y_test = testY
#trainX = pad_sequences(trainX, maxlen=max_features, value=0.)
#testX = pad_sequences(testX, maxlen=max_features, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY, nb_classes=2)
testY = to_categorical(testY, nb_classes=2)
# Building convolutional network
network = input_data(shape=[None,max_document_length,max_features,1], name='input')
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(trainX, trainY,
n_epoch=5, shuffle=True, validation_set=(testX, testY),
show_metric=True,run_id="sms")
y_predict_list = model.predict(testX)
print y_predict_list
y_predict = []
for i in y_predict_list:
print i[0]
if i[0] > 0.5:
y_predict.append(0)
else:
y_predict.append(1)
print(classification_report(y_test, y_predict))
print metrics.confusion_matrix(y_test, y_predict)
def create_model(learning_rate, input_shape, nb_classes, base_path, drop=1):
network = input_data(shape=input_shape, name='input')
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, drop)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, drop)
network = fully_connected(network, nb_classes, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=learning_rate,
loss='categorical_crossentropy', name='target')
model = tflearn.DNN(network, tensorboard_verbose=0, checkpoint_path=base_path + "/checkpoints/step")
return model
def build_model_2_conv(learning_rate, input_shape, nb_classes, base_path, drop):
network = input_data(shape=input_shape, name='input')
network = conv_2d(network, 64, [4, 16], activation='relu')
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, [4, 16], activation='relu')
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='relu')
network = dropout(network, drop)
network = fully_connected(network, 64, activation='relu')
network = dropout(network, drop)
network = fully_connected(network, nb_classes, activation='softmax')
network = regression(network, optimizer='sgd', learning_rate=learning_rate,
loss='categorical_crossentropy', name='target')
model = tflearn.DNN(network, tensorboard_verbose=3, tensorboard_dir=base_path + "/tflearn_logs/",
checkpoint_path=base_path + "/checkpoints/step")
return model
def _model1():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 32, 3, strides = 4, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, strides = 2, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, len(yTest[0]), activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target')
model = tflearn.DNN(network, tensorboard_verbose=3)
model.load(_path)
pred = model.predict(xTest)
df = pd.DataFrame(pred)
df.to_csv(_path + ".csv")
newList = pred.copy()
newList = convert2(newList)
if _CSV: makeCSV(newList)
pred = convert2(pred)
pred = convert3(pred)
yTest = convert3(yTest)
print(metrics.confusion_matrix(yTest, pred))
print(metrics.classification_report(yTest, pred))
print('Accuracy', accuracy_score(yTest, pred))
print()
if _wrFile: writeTest(pred)
def create_alexnet(num_classes, restore=False):
# Building 'AlexNet'
network = input_data(shape=[None, 224, 224, 3])
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
return network
def create_cnn_layers():
shape = [None, IMAGE_STD_HEIGHT, IMAGE_STD_WIDTH, RGB_COLOR_COUNT]
# input_layer = Input(name='input', shape=shape)
input_layer = input_data(name='input', shape=shape)
# h = Convolution2D(22, 5, 5, activation='relu', dim_ordering=dim_ordering)(input_layer)
h = conv_2d_specialized(input_layer, 22, [5, 5])
POOL_SIZE = [2, 2]
# h = MaxPooling2D(pool_size=POOL_SIZE)(h)
h = max_pool_2d(h, POOL_SIZE, padding='valid')
h = local_response_normalization(h)
# h = Convolution2D(44, 3, 3, activation='relu', dim_ordering=dim_ordering)(h)
h = conv_2d_specialized(h, 44, [3, 3])
# h = MaxPooling2D(pool_size=POOL_SIZE)(h)
h = max_pool_2d(h, POOL_SIZE, padding='valid')
h = local_response_normalization(h)
# h = Dropout(0.25)(h)
h = dropout(h, 1-0.25)
# last_cnn_layer = Flatten()(h)
last_cnn_layer = flatten(h)
return input_layer, last_cnn_layer
def _model3():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
network = input_data(shape=[None, inputSize, inputSize, dim],
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, len(Y[0]), activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
print('Model has been made!!!?')
# Training
model = tflearn.DNN(network, checkpoint_path='model_densenet_cifar10',
max_checkpoints=10, tensorboard_verbose=0,
clip_gradients=0.)
model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest),
snapshot_epoch=False, snapshot_step=200,
show_metric=True, batch_size=batchNum, shuffle=True,
run_id='resnext_cifar10')
if modelStore: model.save(_id + '-model.tflearn')
def build_alex_network(self, network):
# network = input_data(shape=[None, 227, 227, 3])
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
return network
def test_vbs1(self):
with tf.Graph().as_default():
# Data loading and preprocessing
import tflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
X = X[:20, :, :, :]
Y = Y[:20, :]
testX = testX[:10, :, :, :]
testY = testY[:10, :]
# Building convolutional network
network = input_data(shape=[None, 28, 28, 1], name='input')
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=3)
model.fit({'input': X}, {'target': Y}, n_epoch=1,
batch_size=10,
validation_set=({'input': testX}, {'target': testY}),
validation_batch_size=5,
snapshot_step=10, show_metric=True, run_id='convnet_mnist_vbs')
self.assertEqual(model.train_ops[0].validation_batch_size, 5)
self.assertEqual(model.train_ops[0].batch_size, 10)
def main():
"""
the main party
:return:
"""
# pickle parameters
fg_or_bg = 'background'
sdr_type = 'sdr'
feature = 'sim_mat'
l = 432
n_classes = 11
testing_percent = 0.1
sim_mat_array, sdr_array = get_generated_data(feature, fg_or_bg, sdr_type)
sim_mat_array = np.expand_dims(sim_mat_array, -1)
sdr_array_1h, hist = sdrs_to_one_hots(sdr_array, n_classes, True)
network = input_data(shape=[None, l, l, 1], name='input')
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, n_classes, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit({'input': sim_mat_array}, {'target': sdr_array_1h}, n_epoch=10,
validation_set=testing_percent,
snapshot_step=100, show_metric=True, run_id='convnet_mnist')
def initialize_model(lr):
net = input_data(shape=[None, 28, 28, 1])
net = local_response_normalization(net)
net = conv_2d(net, 32, 3, activation='relu', regularizer="L2")
net = max_pool_2d(net, 2)
net = local_response_normalization(net)
net = conv_2d(net, 64, 3, activation='relu', regularizer="L2")
net = max_pool_2d(net, 2)
net = local_response_normalization(net)
net = conv_2d(net, 64, 3, activation='relu', regularizer="L2")
net = max_pool_2d(net, 2)
net = local_response_normalization(net)
net = conv_2d(net, 128, 3, activation='relu', regularizer="L2")
net = max_pool_2d(net, 2)
net = local_response_normalization(net)
net = fully_connected(net, 128, activation='relu')
net = dropout(net, 0.8)
net = fully_connected(net, 64, activation='relu')
net = dropout(net, 0.8)
net = fully_connected(net, 10, activation='softmax')
net = regression(net, optimizer='adam', learning_rate=lr, loss='categorical_crossentropy')
model = tflearn.DNN(net)
return model
def do_cnn(trainX, testX, trainY, testY):
# Converting labels to binary vectors
trainY = to_categorical(trainY, nb_classes=4)
testY = to_categorical(testY, nb_classes=4)
# Building convolutional network
network = input_data(shape=[None, 32, 32,1], name='input')
network = conv_2d(network, 16, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 16, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 16, activation='tanh')
network = dropout(network, 0.1)
network = fully_connected(network, 16, activation='tanh')
network = dropout(network, 0.1)
network = fully_connected(network, 4, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(trainX, trainY, n_epoch=10, validation_set=(testX, testY),show_metric=True, run_id="malware")
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import h5py
data = h5py.File('forms_out.h5', 'r')
train_input = data['train_image']
test_input = data['test_image']
train_label = data['train_label']
test_label = data['test_label']
network = input_data(shape = [None, 256, 256, 3], name='data')
network = conv_2d(network, 96, 11, strides=4, activation='relu', regularizer='L2', name='conv1')
network = local_response_normalization(network, depth_radius = 5, alpha = 0.0001, beta = 0.75, name='norm1')
network = max_pool_2d(network, 3, strides=2, name= 'pool1')
network = conv_2d(network, 256, 5, strides=1, activation = 'relu', regularizer='L2', name='conv2')
network = local_response_normalization(network, depth_radius = 5, alpha=0.0001, beta=0.75, name = 'norm2')
network = max_pool_2d(network, 3, strides=2, name='pool2')
network = conv_2d(network, 384, 3, strides=1, activation = 'relu', regularizer='L2', name='conv3')
network = conv_2d(network, 384, 3, strides=1, activation = 'relu', regularizer='L2', name='conv4')
network = conv_2d(network, 256, 3, strides=1, activation = 'relu', regularizer='L2', name='conv5')
network = max_pool_2d(network, 3, strides=2, name='pool5')
network = fully_connected(network, 4096, activation='relu', name='fc6')
network = dropout(network, 0.5, name='drop6')
network = fully_connected(network, 4096, activation='relu', name='fc7')
network = dropout(network, 0.5, name='drop7')
network = fully_connected(network, 11, activation='softmax', name='final')
network = regression(network, optimizer='sgd', learning_rate=0.01, loss='softmax_categorical_crossentropy', batch_size=50, name='target')
def gamornet_build_model_tflearn(input_shape,
trainable_bools=[True] * 8,
load_layers_bools=[True] * 8):
input_shape = check_input_shape_validity(input_shape)
load_layers_bools = check_bools_validity(load_layers_bools)
trainable_bools = check_bools_validity(trainable_bools)
model = input_data(
shape=[None, input_shape[0], input_shape[1], input_shape[2]])
model = conv_2d(model,
96,
11,
strides=4,
activation='relu',
trainable=trainable_bools[0],
restore=load_layers_bools[0])
model = max_pool_2d(model, 3, strides=2)
model = local_response_normalization(model)
model = conv_2d(model,
256,
5,
activation='relu',
trainable=trainable_bools[1],
restore=load_layers_bools[1])
model = max_pool_2d(model, 3, strides=2)
model = local_response_normalization(model)
model = conv_2d(model,
384,
3,
activation='relu',
trainable=trainable_bools[2],
restore=load_layers_bools[2])
model = conv_2d(model,
384,
3,
activation='relu',
trainable=trainable_bools[3],
restore=load_layers_bools[3])
model = conv_2d(model,
256,
3,
activation='relu',
trainable=trainable_bools[4],
restore=load_layers_bools[4])
model = max_pool_2d(model, 3, strides=2)
model = local_response_normalization(model)
model = fully_connected(model,
4096,
activation='tanh',
trainable=trainable_bools[5],
restore=load_layers_bools[5])
model = dropout(model, 0.5)
model = fully_connected(model,
4096,
activation='tanh',
trainable=trainable_bools[6],
restore=load_layers_bools[6])
model = dropout(model, 0.5)
model = fully_connected(model,
3,
activation='softmax',
trainable=trainable_bools[7],
restore=load_layers_bools[7])
return model
from __future__ import division, print_function, absolute_import import tflearn from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.conv import conv_2d, max_pool_2d, avg_pool_2d from tflearn.layers.normalization import local_response_normalization from tflearn.layers.merge_ops import merge from tflearn.layers.estimator import regression import tflearn.datasets.oxflower17 as oxflower17 X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227)) network = input_data(shape=[None, 227, 227, 3]) conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') # 3a inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce') inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool') inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
def build_network(self):
# Smaller 'AlexNet'
# https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
# img_aug.add_random_flip_updown()
img_aug.add_random_crop([SIZE_FACE, SIZE_FACE], padding=4)
img_aug.add_random_rotation(max_angle=16.0)
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
img_prep.add_featurewise_stdnorm(per_channel=True)
print('[+] Building CNN')
self.network = input_data(shape=[None, SIZE_FACE, SIZE_FACE, 1],
data_preprocessing=img_prep,
data_augmentation=img_aug)
self.network = conv_2d(self.network,
64,
3,
activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = conv_2d(self.network,
64,
3,
activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = local_response_normalization(self.network)
self.network = max_pool_2d(self.network, 2, strides=2)
self.network = dropout(self.network, 0.8)
self.network = conv_2d(self.network,
128,
3,
activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = conv_2d(self.network,
128,
3,
activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = max_pool_2d(self.network, 2, strides=2)
self.network = dropout(self.network, 0.8)
self.network = conv_2d(self.network,
256,
3,
activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = conv_2d(self.network,
256,
3,
activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = max_pool_2d(self.network, 2, strides=2)
self.network = dropout(self.network, 0.8)
self.network = conv_2d(self.network,
512,
3,
activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = conv_2d(self.network,
512,
3,
activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = max_pool_2d(self.network, 2, strides=2)
self.network = dropout(self.network, 0.8)
self.network = fully_connected(
self.network, 4096, activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = dropout(self.network, 0.7)
self.network = fully_connected(
self.network, 4096, activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = dropout(self.network, 0.7)
self.network = fully_connected(
self.network, 1024, activation='relu',
regularizer='L2') #, weight_decay=0.0001)
self.network = dropout(self.network, 0.7)
self.network = fully_connected(self.network,
len(EMOTIONS),
activation='softmax') #, restore=False)
mom = tflearn.optimizers.Momentum(learning_rate=0.02,
lr_decay=0.8,
decay_step=500)
self.network = regression(
self.network, optimizer=mom,
loss='categorical_crossentropy') #, restore=False)
self.model = tflearn.DNN(self.network,
tensorboard_dir='../tmp/',
checkpoint_path=None,
max_checkpoints=None,
tensorboard_verbose=0)
self.model.load(join(SAVE_DIRECTORY, SAVE_MODEL_FILENAME),
weights_only=True)
def __init__(self, args):
# ctrl
self.img_size = args.img_size
self.label_size = args.label_size
self.model_name = args.model_name
self.gpu_usage = args.gpu_usage
# net
tflearn.config.init_graph(gpu_memory_fraction=self.gpu_usage)
network = input_data(shape=[None, self.img_size, self.img_size, 3])
conv1_7_7 = conv_2d(network,
64,
7,
strides=2,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3,
64,
1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce,
192,
3,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3,
kernel_size=3,
strides=2,
name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3,
64,
1,
activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3,
96,
1,
activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce,
128,
filter_size=3,
activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,
16,
filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce,
32,
filter_size=5,
activation='relu',
name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(
pool2_3_3,
kernel_size=3,
strides=1,
)
inception_3a_pool_1_1 = conv_2d(inception_3a_pool,
32,
filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([
inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1
],
mode='concat',
axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce,
192,
filter_size=3,
activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output,
32,
filter_size=1,
activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce,
96,
filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output,
kernel_size=3,
strides=1,
name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool,
64,
filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([
inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1
],
mode='concat',
axis=3,
name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output,
kernel_size=3,
strides=2,
name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3,
192,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3,
96,
filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce,
208,
filter_size=3,
activation='relu',
name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3,
16,
filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce,
48,
filter_size=5,
activation='relu',
name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3,
kernel_size=3,
strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool,
64,
filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([
inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1
],
mode='concat',
axis=3,
name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output,
160,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output,
112,
filter_size=1,
activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce,
224,
filter_size=3,
activation='relu',
name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output,
24,
filter_size=1,
activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output,
kernel_size=3,
strides=1,
name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool,
64,
filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([
inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1
],
mode='concat',
axis=3,
name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce,
256,
filter_size=3,
activation='relu',
name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output,
24,
filter_size=1,
activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output,
kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool,
64,
filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([
inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1
],
mode='concat',
axis=3,
name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output,
112,
filter_size=1,
activation='relu',
name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output,
144,
filter_size=1,
activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce,
288,
filter_size=3,
activation='relu',
name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output,
32,
filter_size=1,
activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output,
kernel_size=3,
strides=1,
name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool,
64,
filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([
inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1
],
mode='concat',
axis=3,
name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output,
256,
filter_size=1,
activation='relu',
name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output,
160,
filter_size=1,
activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output,
32,
filter_size=1,
activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output,
kernel_size=3,
strides=1,
name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool,
128,
filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([
inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1
],
axis=3,
mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output,
kernel_size=3,
strides=2,
name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3,
256,
filter_size=1,
activation='relu',
name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3,
160,
filter_size=1,
activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3,
32,
filter_size=1,
activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3,
kernel_size=3,
strides=1,
name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool,
128,
filter_size=1,
activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge([
inception_5a_1_1, inception_5a_3_3, inception_5a_5_5,
inception_5a_pool_1_1
],
axis=3,
mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output,
384,
filter_size=1,
activation='relu',
name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output,
192,
filter_size=1,
activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce,
384,
filter_size=3,
activation='relu',
name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output,
48,
filter_size=1,
activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output,
kernel_size=3,
strides=1,
name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool,
128,
filter_size=1,
activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([
inception_5b_1_1, inception_5b_3_3, inception_5b_5_5,
inception_5b_pool_1_1
],
axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
# output stage
loss = fully_connected(pool5_7_7,
self.label_size,
activation='softmax')
network = regression(loss,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
model_path = 'models/%s' % self.model_name
if not os.path.exists(model_path): os.makedirs(model_path)
self.model = tflearn.DNN(network,
checkpoint_path=model_path + '/model',
max_checkpoints=3,
tensorboard_verbose=2)
# load existing model checkpoint
ckpt = self.get_checkpoint(model_path)
if ckpt:
self.model.load(ckpt)
print("load existing checkpoint from %s" % ckpt)
def inception_v3(width,
height,
lr,
output=9,
model_name='sentnet_color.model'):
network = input_data(shape=[None, width, height, 1], name='input')
conv1_7_7 = conv_2d(network,
64,
10,
strides=2,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = avg_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3,
64,
1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce,
192,
3,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = avg_pool_2d(conv2_3_3,
kernel_size=3,
strides=2,
name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(conv2_3_3,
64,
1,
activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3,
96,
1,
activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce,
128,
filter_size=3,
activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,
16,
filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce,
32,
filter_size=5,
activation='relu',
name='inception_3a_5_5')
inception_3a_pool = avg_pool_2d(
pool2_3_3,
kernel_size=3,
strides=1,
)
inception_3a_pool_1_1 = conv_2d(inception_3a_pool,
32,
filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([
inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1
],
mode='concat',
axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce,
192,
filter_size=3,
activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output,
32,
filter_size=1,
activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce,
96,
filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = avg_pool_2d(inception_3a_output,
kernel_size=3,
strides=1,
name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool,
64,
filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
# merge the inception_3b_*
inception_3b_output = merge([
inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1
],
mode='concat',
axis=3,
name='inception_3b_output')
pool3_3_3 = avg_pool_2d(inception_3b_output,
kernel_size=3,
strides=2,
name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3,
192,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3,
96,
filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce,
208,
filter_size=3,
activation='relu',
name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3,
16,
filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce,
48,
filter_size=5,
activation='relu',
name='inception_4a_5_5')
inception_4a_pool = avg_pool_2d(pool3_3_3,
kernel_size=3,
strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool,
64,
filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([
inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1
],
mode='concat',
axis=3,
name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output,
160,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output,
112,
filter_size=1,
activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce,
224,
filter_size=3,
activation='relu',
name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output,
24,
filter_size=1,
activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4b_5_5')
inception_4b_pool = avg_pool_2d(inception_4a_output,
kernel_size=3,
strides=1,
name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool,
64,
filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([
inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1
],
mode='concat',
axis=3,
name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce,
256,
filter_size=3,
activation='relu',
name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output,
24,
filter_size=1,
activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4c_5_5')
inception_4c_pool = avg_pool_2d(inception_4b_output,
kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool,
64,
filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([
inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1
],
mode='concat',
axis=3,
name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output,
112,
filter_size=1,
activation='relu',
name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output,
144,
filter_size=1,
activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce,
288,
filter_size=3,
activation='relu',
name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output,
32,
filter_size=1,
activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4d_5_5')
inception_4d_pool = avg_pool_2d(inception_4c_output,
kernel_size=3,
strides=1,
name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool,
64,
filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([
inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1
],
mode='concat',
axis=3,
name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output,
256,
filter_size=1,
activation='relu',
name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output,
160,
filter_size=1,
activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output,
32,
filter_size=1,
activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_4e_5_5')
inception_4e_pool = avg_pool_2d(inception_4d_output,
kernel_size=3,
strides=1,
name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool,
128,
filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([
inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1
],
axis=3,
mode='concat')
pool4_3_3 = avg_pool_2d(inception_4e_output,
kernel_size=3,
strides=2,
name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3,
256,
filter_size=1,
activation='relu',
name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3,
160,
filter_size=1,
activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3,
32,
filter_size=1,
activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5a_5_5')
inception_5a_pool = avg_pool_2d(pool4_3_3,
kernel_size=3,
strides=1,
name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool,
128,
filter_size=1,
activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge([
inception_5a_1_1, inception_5a_3_3, inception_5a_5_5,
inception_5a_pool_1_1
],
axis=3,
mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output,
384,
filter_size=1,
activation='relu',
name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output,
192,
filter_size=1,
activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce,
384,
filter_size=3,
activation='relu',
name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output,
48,
filter_size=1,
activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5b_5_5')
inception_5b_pool = avg_pool_2d(inception_5a_output,
kernel_size=3,
strides=1,
name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool,
128,
filter_size=1,
activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([
inception_5b_1_1, inception_5b_3_3, inception_5b_5_5,
inception_5b_pool_1_1
],
axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, output, activation='softplus')
network = regression(loss,
optimizer='momentum',
loss='mean_square',
learning_rate=lr,
name='targets')
model = tflearn.DNN(network,
max_checkpoints=0,
tensorboard_verbose=0,
tensorboard_dir='log')
return model
def test_vm1(self):
with tf.Graph().as_default():
# Data loading and preprocessing
import tflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
X = X[:10, :, :, :]
Y = Y[:10, :]
# Building convolutional network
network = input_data(shape=[None, 28, 28, 1], name='input')
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
# construct two varaibles to add as additional "valiation monitors"
# these varaibles are evaluated each time validation happens (eg at a snapshot)
# and the results are summarized and output to the tensorboard events file,
# together with the accuracy and loss plots.
#
# Here, we generate a dummy variable given by the sum over the current
# network tensor, and a constant variable. In practice, the validation
# monitor may present useful information, like confusion matrix
# entries, or an AUC metric.
with tf.name_scope('CustomMonitor'):
test_var = tf.reduce_sum(tf.cast(network, tf.float32), name="test_var")
test_const = tf.constant(32.0, name="custom_constant")
print ("network=%s, test_var=%s" % (network, test_var))
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target', validation_monitors=[test_var, test_const])
# Training
model = tflearn.DNN(network, tensorboard_verbose=3)
model.fit({'input': X}, {'target': Y}, n_epoch=1,
validation_set=({'input': testX}, {'target': testY}),
snapshot_step=10, show_metric=True, run_id='convnet_mnist')
# check for validation monitor variables
ats = tf.get_collection("Adam_testing_summaries")
print ("ats=%s" % ats)
self.assertTrue(len(ats)==4) # should be four variables being summarized: [loss, test_var, test_const, accuracy]
session = model.session
print ("session=%s" % session)
trainer = model.trainer
print ("train_ops = %s" % trainer.train_ops)
top = trainer.train_ops[0]
vmtset = top.validation_monitors_T
print ("validation_monitors_T = %s" % vmtset)
with model.session.as_default():
ats_var_val = tflearn.variables.get_value(vmtset[0])
ats_const_val = tflearn.variables.get_value(vmtset[1])
print ("summary values: var=%s, const=%s" % (ats_var_val, ats_const_val))
self.assertTrue(ats_const_val==32) # test to make sure the constant made it through
def build_net(network, X, Y, num_classes, num_epochs, checkpoint_path, size_batch, Xval=None, Yval=None, dec_step=100,
train=True):
tn = tflearn.initializations.truncated_normal(seed=100)
xav = tflearn.initializations.xavier(seed=100)
nor = tflearn.initializations.normal(seed=100)
network = conv_2d(network, 96, 11, strides=4, weights_init=nor, regularizer='L2')
network = batch_normalization(network)
network = tflearn.activations.softplus(network)
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, weights_init=nor, regularizer='L2')
network = batch_normalization(network)
network = tflearn.activations.softplus(network)
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, weights_init=nor, regularizer='L2')
network = batch_normalization(network)
network = tflearn.activations.softplus(network)
network = conv_2d(network, 384, 3, weights_init=nor, regularizer='L2')
network = batch_normalization(network)
network = tflearn.activations.softplus(network)
network = conv_2d(network, 256, 3, weights_init=nor, regularizer='L2')
network = batch_normalization(network)
network = tflearn.activations.softplus(network)
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 512, weights_init=nor, regularizer='L2')
network = batch_normalization(network)
network = tflearn.activations.softplus(network)
network = dropout(network, 0.5)
network = fully_connected(network, 512, weights_init=nor, regularizer='L2')
network = batch_normalization(network)
network = tflearn.activations.softplus(network)
network = dropout(network, 0.5)
network = fully_connected(network, num_classes, activation='softmax')
adadelta = tflearn.optimizers.AdaDelta(learning_rate=0.01, rho=0.95, epsilon=1e-08)
network = regression(network, optimizer=adadelta,
loss='categorical_crossentropy'
)
# Train
model = tflearn.DNN(network, tensorboard_verbose=0, checkpoint_path=checkpoint_path)
if train:
start_time = time.time()
if Xval is None or Yval is None:
model.fit(X, Y, n_epoch=num_epochs,
validation_set=0.0,
show_metric=True, run_id='hsi_cnn_model', shuffle=True, batch_size=size_batch)
else:
model.fit(X, Y, n_epoch=num_epochs,
validation_set=(Xval, Yval),
show_metric=True, run_id='hsi_cnn_model', shuffle=True, batch_size=size_batch)
print("\n\n-------------train time: %s seconds\n\n" % (time.time() - start_time))
return model
def construct_inceptionv1onfire (x,y):
# Build network as per architecture in [Dunnings/Breckon, 2018]
network = input_data(shape=[None, y, x, 3])
conv1_7_7 = conv_2d(network, 64, 5, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 128,3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
pool5_7_7 = avg_pool_2d(inception_4a_output, kernel_size=5, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, 2,activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(network, checkpoint_path='inceptiononv1onfire',
max_checkpoints=1, tensorboard_verbose=2)
return model
def defGoogleNet(nLabels, nFreq, nTime): tflearn.init_graph(gpu_memory_fraction=0.5, seed=6969) network = tflearn.input_data(shape=[None, nTime, nFreq, 1]) conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') # 3a inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce') inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool') inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) # 3b inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1') inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1') inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') # 4a inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce') inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3') inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce') inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5') inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool') inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1') inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output') # 4b inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce') inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3') inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce') inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5') inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool') inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1') inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output') # 4c inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1') inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce') inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3') inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce') inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5') inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1) inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1') inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output') # 4d inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1') inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce') inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3') inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce') inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5') inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool') inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1') inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output') # 4e inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1') inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce') inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3') inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce') inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5') inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool') inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1') inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat') pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3') # 5a inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1') inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce') inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3') inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce') inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5') inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool') inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1') inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat') # 5b inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1') inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce') inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3') inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce') inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5') inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool') inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1') inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat') pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1) pool5_7_7 = dropout(pool5_7_7, 0.2) net = tflearn.fully_connected(pool5_7_7, nLabels, activation='softmax') net = tflearn.regression(net, optimizer='adam',loss='categorical_crossentropy',learning_rate=0.001, shuffle_batches=False) return net
def network(img_shape, name, LR):
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
#
# # Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_blur(sigma_max=3.0)
img_aug.add_random_90degrees_rotation(rotations=[0, 2])
network = input_data(shape=img_shape,
name=name,
data_preprocessing=img_prep,
data_augmentation=img_aug)
conv1_7_7 = conv_2d(network,
64,
7,
strides=2,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3,
64,
1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce,
192,
3,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3,
kernel_size=3,
strides=2,
name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3,
64,
1,
activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3,
96,
1,
activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce,
128,
filter_size=3,
activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,
16,
filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce,
32,
filter_size=5,
activation='relu',
name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(
pool2_3_3,
kernel_size=3,
strides=1,
)
inception_3a_pool_1_1 = conv_2d(inception_3a_pool,
32,
filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([
inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1
],
mode='concat',
axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce,
192,
filter_size=3,
activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output,
32,
filter_size=1,
activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce,
96,
filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output,
kernel_size=3,
strides=1,
name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool,
64,
filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([
inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1
],
mode='concat',
axis=3,
name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output,
kernel_size=3,
strides=2,
name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3,
192,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3,
96,
filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce,
208,
filter_size=3,
activation='relu',
name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3,
16,
filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce,
48,
filter_size=5,
activation='relu',
name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3,
kernel_size=3,
strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool,
64,
filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([
inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1
],
mode='concat',
axis=3,
name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output,
160,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output,
112,
filter_size=1,
activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce,
224,
filter_size=3,
activation='relu',
name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output,
24,
filter_size=1,
activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output,
kernel_size=3,
strides=1,
name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool,
64,
filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([
inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1
],
mode='concat',
axis=3,
name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce,
256,
filter_size=3,
activation='relu',
name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output,
24,
filter_size=1,
activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output,
kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool,
64,
filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([
inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1
],
mode='concat',
axis=3,
name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output,
112,
filter_size=1,
activation='relu',
name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output,
144,
filter_size=1,
activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce,
288,
filter_size=3,
activation='relu',
name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output,
32,
filter_size=1,
activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output,
kernel_size=3,
strides=1,
name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool,
64,
filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([
inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1
],
mode='concat',
axis=3,
name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output,
256,
filter_size=1,
activation='relu',
name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output,
160,
filter_size=1,
activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output,
32,
filter_size=1,
activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output,
kernel_size=3,
strides=1,
name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool,
128,
filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([
inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1
],
axis=3,
mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output,
kernel_size=3,
strides=2,
name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3,
256,
filter_size=1,
activation='relu',
name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3,
160,
filter_size=1,
activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3,
32,
filter_size=1,
activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3,
kernel_size=3,
strides=1,
name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool,
128,
filter_size=1,
activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge([
inception_5a_1_1, inception_5a_3_3, inception_5a_5_5,
inception_5a_pool_1_1
],
axis=3,
mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output,
384,
filter_size=1,
activation='relu',
name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output,
192,
filter_size=1,
activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce,
384,
filter_size=3,
activation='relu',
name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output,
48,
filter_size=1,
activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output,
kernel_size=3,
strides=1,
name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool,
128,
filter_size=1,
activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([
inception_5b_1_1, inception_5b_3_3, inception_5b_5_5,
inception_5b_pool_1_1
],
axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, class_num, activation='softmax')
network = regression(loss,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=LR,
name='targets')
return network
img_num = img[0]
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
testing_data.append([np.array(img), img_num])
shuffle(testing_data)
np.save('test_data.npy', testing_data)
return testing_data
tf.reset_default_graph()
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = local_response_normalization(convnet)
print(convnet.shape)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = local_response_normalization(convnet)
convnet = max_pool_2d(convnet, 2)
print(convnet.shape)
convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = local_response_normalization(convnet)
print(convnet.shape)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.5)
def AudioPredict(soundfile, class_test,threshold):
import argparse
import tensorflow as tf
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import os
import numpy as np
import cv2
from skimage.util.shape import view_as_blocks
from skimage.util.shape import view_as_windows
np.random.seed(1001)
import librosa
from pytictoc import TicToc
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.logging.set_verbosity(tf.logging.ERROR)
patchscore = float(threshold.decode('base64', 'strict'))
timet = TicToc()
timet.tic()
goobeps=0.82
islandps=0.82
mlworldps=0.82
bootps=0.82
tpcount=2e9
tt=0
classtestlist=['boot','goobe','island', 'mlworld']
for p in range(len(classtestlist)):
if class_test==classtestlist[p]:
tt+=1
if tt==0:
print('class_test ', class_test, ' entered is not valid')
print('acceptable class entries: ', classtestlist)
print('loading audio file')
#load sound file, get melspectrogram, crop overlapping tiles and save tensor
X=np.random.random((1,128,128,1))
wav1, sr = librosa.core.load(soundfile)
stepper=int(np.ceil(len(wav1)/sr/6))
Sxx=librosa.feature.melspectrogram(wav1)
Sxx2 = np.log10(1+10*abs(Sxx))
Sxx2norm=255*(Sxx2-Sxx2.min())/(Sxx2.max()-Sxx2.min());
Sxx2norm=np.round_(Sxx2norm, decimals=0, out=None).astype(int)
Sxx2norm=Sxx2norm.astype(np.uint8)
if Sxx2norm.shape[1]>=128:
s1=int(np.floor(Sxx2norm.shape[1]/128)*128)
B = view_as_windows(Sxx2norm, window_shape=(128, 128),step=stepper)
for index in range(B.shape[1]):
x=B[0][index]
x = np.expand_dims(x, axis=0)
x = np.expand_dims(x, axis=3)
X = np.concatenate((X,x),axis=0)
# remove random start
X = X[1:,:,:,:]
tf.reset_default_graph()
# number of classes
y=2
# image preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center()# Zero Center (With mean computed over the whole dataset)
img_prep.add_featurewise_stdnorm() #STD Normalization (With std computed over the whole dataset)
# Building 'AlexNet'
network = input_data(shape=[None, 128, 128, 1],data_preprocessing=img_prep)
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, y, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.0001)
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=2)
# set up paramaters for model
if class_test=='boot':
classmodel='AudioModels/model_Alexnet_bootnet1/Alexnet_bootnet1'
patchscore=bootps
tilepasscount=np.floor(tpcount/(len(wav1)*len(X)))
if tilepasscount>len(X):
tilepasscount=np.floor(0.75*len(X))
print('loading boot test')
elif class_test=='goobe':
print('loading goobe test')
classmodel='AudioModels/model_Alexnet_goobenet1/Alexnet_goobenet1'
patchscore=goobeps
tilepasscount=np.floor(tpcount/(len(wav1)*len(X)))
if tilepasscount>len(X):
tilepasscount=np.floor(0.25*len(X))
elif class_test=='island':
print('loading island test')
classmodel='AudioModels/model_Alexnet_islandnet1/Alexnet_islandnet1'
patchscore=islandps
tilepasscount=np.floor(tpcount/(len(wav1)*len(X)))
if tilepasscount>len(X):
tilepasscount=np.floor(0.25*len(X))
elif class_test=='mlworld':
print('loading mlworld test')
classmodel='AudioModels/model_Alexnet_mlworldnet1/Alexnet_mlworldnet1'
patchscore=mlworldps
tilepasscount=np.floor(tpcount/(len(wav1)*len(X)))
if tilepasscount>len(X):
tilepasscount=np.floor(0.25*len(X))
model.load(classmodel)
print("totaltilecount", len(X))
print("tilepasscount", tilepasscount)
lastPF=[]
# Get prediction of tile list
res=model.predict(X)
print(res)
# method 1: PASS if any one tile gets 99% or better
topmatch=0
for k in range(len(res[:,0])):
if res[k,0]>=0.99:
topmatch+=1
# tally consecutive tiles that meet the threshold
z = np.where((res[:,0]-patchscore)>0)
z=z[0]
tally=[0]
for j in range(len(z)-1):
if z[j+1]-z[j]==1:
tally.append(1)
if(sum(tally)!=0):
tally.append(1)
timet.toc()
if(sum(tally)>=tilepasscount or topmatch>=1):
lastPF.append(1)
print(sum(tally),'/',len(X))
else:
lastPF.append(0)
print(sum(tally),'/',len(X))
if np.sum(lastPF)>=1:
print('PASS')
ret ='PASS'
return ret
else:
print('FAIL')
ret = 'FAIL'
return ret
# fix the column ordering, and subtract the mean
Xtrain = samplewise_zero_center(fix_columns(train_orig['X'], 32, 32, 3))
Ytrain = to_categorical(train_orig['y'] - 1, 10) # shift from 1-10 to 0-9
Xtest = samplewise_zero_center(fix_columns(test_orig['X'], 32, 32, 3))
Ytest = to_categorical(test_orig['y'] - 1, 10) # shift from 1-10 to 0-9
print(tf.__version__)
print('Xtrain.shape: {}, Ytrain.shape: {}'.format(Xtrain.shape, Ytrain.shape))
print('Xtest.shape: {}, Ytest.shape: {}'.format(Xtest.shape, Ytest.shape))
network = input_data(shape=[None, 32, 32, 3]) # orig input 227x227
network = conv_2d(network, 48, 8, strides=3, activation='relu') # orig output (55x55x96), filters 11x11, stride 4
network = max_pool_2d(network, 2, strides=1) # orig output (27x27x96), 3x3 stride 2
network = local_response_normalization(network) # orig output same as input
network = conv_2d(network, 126, 2, strides=1, activation='relu') # orig output (23x23x256), filters 5x5, stride 1
network = max_pool_2d(network, 2, strides=1) # orig output (11x11x256), 3x3 stride 2
network = local_response_normalization(network) # orig output same as input
network = conv_2d(network, 192, 3, strides=1, activation='relu') # orig output (9x9x384), filters 3x3 stride 1
network = conv_2d(network, 192, 2, strides=1, activation='relu') # orig output (7x7x384), filters 3x3 stride 1
network = conv_2d(network, 128, 2, strides=1, activation='relu') # orig output (5x5x256), filters x3 stride 1
network = max_pool_2d(network, 2, strides=1) # orig output (2x2x256), 3x3 stride 2
network = local_response_normalization(network) # orig output same as input
network = fully_connected(network, 1024, activation='tanh') # orig input unrolled to 1024x1, orig output 4096x1
network = dropout(network, 0.5)
network = fully_connected(network, 1024, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 10, activation='softmax')
network = regression(network,
optimizer='momentum',
def inception_v3(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
network = input_data(shape=[None, width, height,3], name='input')
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3,name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3,mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, output,activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network,
max_checkpoints=0, tensorboard_verbose=0,tensorboard_dir='log')
return model
def google_net(x):
#Layer 1
conv1_7_7 = tflearn.layers.conv.conv_2d(x,
nb_filter=64,
filter_size=7,
strides=2,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2, name='pool1_3_3_s2')
pool1_3_3 = local_response_normalization(pool1_3_3)
#Layer 2
conv2_3_3_reduce = tflearn.layers.conv.conv_2d(pool1_3_3,
nb_filter=64,
filter_size=1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = tflearn.layers.conv.conv_2d(conv2_3_3_reduce,
nb_filter=192,
filter_size=3,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3,
kernel_size=3,
strides=2,
name='pool2_3_3_s2')
#Inception 3a
inception_3a_1_1 = tflearn.layers.conv.conv_2d(pool2_3_3,
nb_filter=64,
filter_size=1,
activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = tflearn.layers.conv.conv_2d(
pool2_3_3,
nb_filter=96,
filter_size=1,
activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = tflearn.layers.conv.conv_2d(inception_3a_3_3_reduce,
nb_filter=128,
filter_size=3,
activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = tflearn.layers.conv.conv_2d(
pool2_3_3,
nb_filter=16,
filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = tflearn.layers.conv.conv_2d(inception_3a_5_5_reduce,
nb_filter=32,
filter_size=5,
activation='relu',
name='inception_3a_5_5')
inception_3a_pool_3_3 = max_pool_2d(pool2_3_3,
kernel_size=3,
strides=1,
name='inception_3a_pool_3_3')
inception_3a_pool_1_1 = tflearn.layers.conv.conv_2d(
inception_3a_pool_3_3,
nb_filter=32,
filter_size=1,
strides=1,
name='inception_3a_pool_1_1')
inception_3a_concat = merge([
inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1
],
mode='concat',
axis=3)
#Inception 3b
inception_3b_1_1 = tflearn.layers.conv.conv_2d(inception_3a_concat,
nb_filter=128,
filter_size=1,
activation='relu',
name='inception_3b_1_1')
inception_3b_3_3_reduce = tflearn.layers.conv.conv_2d(
inception_3a_concat,
nb_filter=128,
filter_size=1,
activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = tflearn.layers.conv.conv_2d(inception_3b_3_3_reduce,
nb_filter=192,
filter_size=3,
activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = tflearn.layers.conv.conv_2d(
inception_3a_concat,
nb_filter=32,
filter_size=1,
activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = tflearn.layers.conv.conv_2d(inception_3b_5_5_reduce,
nb_filter=96,
filter_size=5,
activation='relu',
name='inception_3b_5_5')
inception_3b_pool_3_3 = max_pool_2d(inception_3a_concat,
kernel_size=3,
strides=1,
name='inception_3b_pool')
inception_3b_pool_1_1 = tflearn.layers.conv.conv_2d(
inception_3b_pool_3_3,
nb_filter=64,
filter_size=1,
strides=1,
name='inception_3b_pool_1_1')
inception_3b_concat = merge([
inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1
],
mode='concat',
axis=3)
#Inception 4a
inception_4a_pool = max_pool_2d(inception_3b_concat,
kernel_size=3,
strides=2,
name='inception_4a_pool_3_3')
inception_4a_1_1 = tflearn.layers.conv.conv_2d(inception_4a_pool,
nb_filter=192,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4a_3_3_reduce = tflearn.layers.conv.conv_2d(
inception_4a_pool,
nb_filter=96,
filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = tflearn.layers.conv.conv_2d(inception_4a_3_3_reduce,
nb_filter=208,
filter_size=3,
activation='relu',
name='inception_4a_3_3')
inception_4a_5_5_reduce = tflearn.layers.conv.conv_2d(
inception_4a_pool,
nb_filter=16,
filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = tflearn.layers.conv.conv_2d(inception_4a_5_5_reduce,
nb_filter=48,
filter_size=5,
activation='relu',
name='inception_4a_5_5')
inception_4a_pool_3_3 = max_pool_2d(inception_4a_pool,
kernel_size=3,
strides=1,
name='inception_4a_pool_3_3')
inception_4a_pool_1_1 = tflearn.layers.conv.conv_2d(
inception_4a_pool_3_3,
nb_filter=64,
filter_size=1,
strides=1,
name='inception_4a_pool_1_1')
inception_4a_concat = merge([
inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1
],
mode='concat',
axis=3)
#Inception 4b
inception_4b_1_1 = tflearn.layers.conv.conv_2d(inception_4a_concat,
nb_filter=160,
filter_size=1,
activation='relu',
name='inception_4b_1_1')
inception_4b_3_3_reduce = tflearn.layers.conv.conv_2d(
inception_4a_concat,
nb_filter=112,
filter_size=1,
activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = tflearn.layers.conv.conv_2d(inception_4b_3_3_reduce,
nb_filter=224,
filter_size=3,
activation='relu',
name='inception_4b_3_3')
inception_4b_5_5_reduce = tflearn.layers.conv.conv_2d(
inception_4a_concat,
nb_filter=24,
filter_size=1,
activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = tflearn.layers.conv.conv_2d(inception_4b_5_5_reduce,
nb_filter=64,
filter_size=5,
activation='relu',
name='inception_4b_5_5')
inception_4b_pool_3_3 = max_pool_2d(inception_4a_concat,
kernel_size=3,
strides=1,
name='inception_4b_pool')
inception_4b_pool_1_1 = tflearn.layers.conv.conv_2d(
inception_4b_pool_3_3,
nb_filter=64,
filter_size=1,
strides=1,
name='inception_4b_pool_1_1')
inception_4b_concat = merge([
inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1
],
mode='concat',
axis=3)
#Average Pool
avg_pool_4b = avg_pool_2d(inception_4a_concat, kernel_size=5, strides=3)
#Convolution
conv_4b = tflearn.layers.conv.conv_2d(avg_pool_4b,
nb_filter=128,
filter_size=1,
activation='relu')
#FC
fc_4b = fully_connected(conv_4b, n_units=1024, activation='relu')
dropout_4b = tflearn.layers.core.dropout(fc_4b, 0.3)
#Softmax
softmax0 = tflearn.layers.core.activation(dropout_4b, activation='softmax')
#Inception 4c
inception_4c_1_1 = tflearn.layers.conv.conv_2d(inception_4b_concat,
nb_filter=128,
filter_size=1,
activation='relu',
name='inception_4c_1_1')
inception_4c_3_3_reduce = tflearn.layers.conv.conv_2d(
inception_4b_concat,
nb_filter=128,
filter_size=1,
activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = tflearn.layers.conv.conv_2d(inception_4c_3_3_reduce,
nb_filter=256,
filter_size=3,
activation='relu',
name='inception_4c_3_3')
inception_4c_5_5_reduce = tflearn.layers.conv.conv_2d(
inception_4b_concat,
nb_filter=24,
filter_size=1,
activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = tflearn.layers.conv.conv_2d(inception_4c_5_5_reduce,
nb_filter=64,
filter_size=5,
activation='relu',
name='inception_4c_5_5')
inception_4c_pool_3_3 = max_pool_2d(inception_4b_concat,
kernel_size=3,
strides=1,
name='inception_4c_pool')
inception_4c_pool_1_1 = tflearn.layers.conv.conv_2d(
inception_4c_pool_3_3,
nb_filter=64,
filter_size=1,
strides=1,
name='inception_4c_pool_1_1')
inception_4c_concat = merge([
inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1
],
mode='concat',
axis=3)
#Inception 4d
inception_4d_1_1 = tflearn.layers.conv.conv_2d(inception_4c_concat,
nb_filter=112,
filter_size=1,
activation='relu',
name='inception_4d_1_1')
inception_4d_3_3_reduce = tflearn.layers.conv.conv_2d(
inception_4c_concat,
nb_filter=144,
filter_size=1,
activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = tflearn.layers.conv.conv_2d(inception_4d_3_3_reduce,
nb_filter=288,
filter_size=3,
activation='relu',
name='inception_4d_3_3')
inception_4d_5_5_reduce = tflearn.layers.conv.conv_2d(
inception_4c_concat,
nb_filter=32,
filter_size=1,
activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = tflearn.layers.conv.conv_2d(inception_4c_5_5_reduce,
nb_filter=64,
filter_size=5,
activation='relu',
name='inception_4d_5_5')
inception_4d_pool_3_3 = max_pool_2d(inception_4c_concat,
kernel_size=3,
strides=1,
name='inception_4d_pool')
inception_4d_pool_1_1 = tflearn.layers.conv.conv_2d(
inception_4d_pool_3_3,
nb_filter=64,
filter_size=1,
strides=1,
name='inception_4d_pool_1_1')
inception_4d_concat = merge([
inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1
],
mode='concat',
axis=3)
#Inception 4e
inception_4e_1_1 = tflearn.layers.conv.conv_2d(inception_4d_concat,
nb_filter=256,
filter_size=1,
activation='relu',
name='inception_4e_1_1')
inception_4e_3_3_reduce = tflearn.layers.conv.conv_2d(
inception_4d_concat,
nb_filter=160,
filter_size=1,
activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = tflearn.layers.conv.conv_2d(inception_4e_3_3_reduce,
nb_filter=320,
filter_size=3,
activation='relu',
name='inception_4e_3_3')
inception_4e_5_5_reduce = tflearn.layers.conv.conv_2d(
inception_4d_concat,
nb_filter=32,
filter_size=1,
activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = tflearn.layers.conv.conv_2d(inception_4e_5_5_reduce,
nb_filter=128,
filter_size=5,
activation='relu',
name='inception_4e_5_5')
inception_4e_pool_3_3 = max_pool_2d(inception_4c_concat,
kernel_size=3,
strides=1,
name='inception_4e_pool')
inception_4e_pool_1_1 = tflearn.layers.conv.conv_2d(
inception_4d_pool_3_3,
nb_filter=128,
filter_size=1,
strides=1,
name='inception_4e_pool_1_1')
inception_4e_concat = merge([
inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1
],
mode='concat',
axis=3)
#Average Pool
avg_pool_4e = avg_pool_2d(inception_4d_concat, kernel_size=5, strides=3)
#Convolution
conv_4e = tflearn.layers.conv.conv_2d(avg_pool_4e,
nb_filter=128,
filter_size=1,
activation='relu')
#FC
fc_4e = fully_connected(conv_4e, n_units=1024, activation='relu')
dropout_4e = tflearn.layers.core.dropout(fc_4e, 0.3)
#Softmax
softmax1 = tflearn.layers.core.activation(dropout_4e, activation='softmax')
#Inception 5a
inception_5a_pool = max_pool_2d(inception_4e_concat,
kernel_size=3,
strides=2,
name='inception_5a_pool_3_3')
inception_5a_1_1 = tflearn.layers.conv.conv_2d(inception_5a_pool,
nb_filter=256,
filter_size=1,
activation='relu',
name='inception_5a_1_1')
inception_5a_3_3_reduce = tflearn.layers.conv.conv_2d(
inception_5a_pool,
nb_filter=160,
filter_size=1,
activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = tflearn.layers.conv.conv_2d(inception_5a_3_3_reduce,
nb_filter=320,
filter_size=3,
activation='relu',
name='inception_5a_3_3')
inception_5a_5_5_reduce = tflearn.layers.conv.conv_2d(
inception_5a_pool,
nb_filter=32,
filter_size=1,
activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = tflearn.layers.conv.conv_2d(inception_5a_5_5_reduce,
nb_filter=128,
filter_size=5,
activation='relu',
name='inception_5a_5_5')
inception_5a_pool_3_3 = max_pool_2d(inception_5a_pool,
kernel_size=3,
strides=1,
name='inception_5a_pool_3_3')
inception_5a_pool_1_1 = tflearn.layers.conv.conv_2d(
inception_5a_pool_3_3,
nb_filter=128,
filter_size=1,
strides=1,
name='inception_5a_pool_1_1')
inception_5a_concat = merge([
inception_5a_1_1, inception_5a_3_3, inception_5a_5_5,
inception_5a_pool_1_1
],
mode='concat',
axis=3)
#Inception 5b
inception_5b_1_1 = tflearn.layers.conv.conv_2d(inception_5a_concat,
nb_filter=384,
filter_size=1,
activation='relu',
name='inception_5b_1_1')
inception_5b_3_3_reduce = tflearn.layers.conv.conv_2d(
inception_5a_concat,
nb_filter=192,
filter_size=1,
activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = tflearn.layers.conv.conv_2d(inception_5b_3_3_reduce,
nb_filter=384,
filter_size=3,
activation='relu',
name='inception_5b_3_3')
inception_5b_5_5_reduce = tflearn.layers.conv.conv_2d(
inception_5a_concat,
nb_filter=48,
filter_size=1,
activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = tflearn.layers.conv.conv_2d(inception_5b_5_5_reduce,
nb_filter=128,
filter_size=5,
activation='relu',
name='inception_5b_5_5')
inception_5b_pool_3_3 = max_pool_2d(inception_5a_concat,
kernel_size=3,
strides=1,
name='inception_5b_pool')
inception_5b_pool_1_1 = tflearn.layers.conv.conv_2d(
inception_5b_pool_3_3,
nb_filter=128,
filter_size=1,
strides=1,
name='inception_5b_pool_1_1')
inception_5b_concat = merge([
inception_5b_1_1, inception_5b_3_3, inception_5b_5_5,
inception_5b_pool_1_1
],
mode='concat',
axis=3)
#Average Pool
avg_pool_5b = avg_pool_2d(inception_5b_concat, kernel_size=7, strides=1)
#Convolution
conv_5b = tflearn.layers.conv.conv_2d(avg_pool_5b,
nb_filter=128,
filter_size=1,
activation='relu')
fc_5b = fully_connected(conv_5b, n_units=1024, activation='relu')
dropout_5b = tflearn.layers.core.dropout(fc_5b, 0.3)
#Softmax
softmax2 = tflearn.layers.core.activation(dropout_5b, activation='softmax')
return softmax2
def build_le_network(self, network):
""" Build google net. (google inception v3)
Args:
network:
Returns:
"""
from tflearn.layers.merge_ops import merge
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2,
name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3,
activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5,
activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge(
[inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1], mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1,
activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128,
filter_size=1, activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3,
activation='relu', name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32,
filter_size=1, activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3,
strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
# merge the inception_3b_*
inception_3b_output = merge(
[inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1], mode='concat', axis=3,
name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2,
name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1,
activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3,
activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5,
activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge(
[inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1], mode='concat', axis=3,
name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1,
activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112,
filter_size=1, activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3,
activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24,
filter_size=1, activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5,
activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3,
strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge(
[inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1], mode='concat', axis=3,
name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1,
activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128,
filter_size=1, activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3,
activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24,
filter_size=1, activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5,
activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge(
[inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1], mode='concat', axis=3,
name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1,
activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144,
filter_size=1, activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3,
activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32,
filter_size=1, activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5,
activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3,
strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge(
[inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1], mode='concat', axis=3,
name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1,
activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160,
filter_size=1, activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3,
activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32,
filter_size=1, activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5,
activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3,
strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge(
[inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1], axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2,
name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1,
activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1,
activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3,
activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1,
activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5,
activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1,
name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1,
activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge(
[inception_5a_1_1, inception_5a_3_3, inception_5a_5_5,
inception_5a_pool_1_1], axis=3, mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1,
activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192,
filter_size=1, activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3,
activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48,
filter_size=1, activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5,
activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3,
strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1,
activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge(
[inception_5b_1_1, inception_5b_3_3, inception_5b_5_5,
inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, 2, activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
return network
def network(img_shape, name, LR):
network = input_data(shape=img_shape, name=name)
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3,name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3,mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, class_num ,activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=LR,
name='targets')
return network
def _model4():
# Taken from TFLearn examples and based on Googles Inception. DO NOT RUN!!!!
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
# 3a
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
# 3b
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1')
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
# 4a
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
# 4b
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
# 4c
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output')
# 4d
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
# 4e
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
# 5a
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
# fc
loss = fully_connected(pool5_7_7, len(Y[0]), activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
# to train
model = tflearn.DNN(network, checkpoint_path='model_googlenet',
max_checkpoints=1, tensorboard_verbose=2)
model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), shuffle=True,
show_metric=True, batch_size=batchNum, snapshot_step=200,
snapshot_epoch=False, run_id='googlenet_oxflowers17')
if modelStore: model.save(_id + '-model.tflearn')
def construct_inceptionv1onfire (x,y): # Build network as per architecture in [Dunnings/Breckon, 2018] network = input_data(shape=[None, y, x, 3]) conv1_7_7 = conv_2d(network, 64, 5, strides=2, activation='relu', name = 'conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 128,3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' ) inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, ) inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') # merge the inception_3a__ inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' ) inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1') #merge the inception_3b_* inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce') inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3') inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce') inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5') inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool') inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1') inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output') pool5_7_7 = avg_pool_2d(inception_4a_output, kernel_size=5, strides=1) pool5_7_7 = dropout(pool5_7_7, 0.4) loss = fully_connected(pool5_7_7, 2,activation='softmax') network = regression(loss, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001) model = tflearn.DNN(network, checkpoint_path='inceptiononv1onfire', max_checkpoints=1, tensorboard_verbose=2) return model
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import tflearn.datasets.oxflower17 as oxflower17
X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))
# AeinNet
net = input_data(shape=[None, 227, 227, 3])
net = conv_2d(net, 64, 11, strides=4, activation='relu')
net = max_pool_2d(net, 3, strides=2)
net = local_response_normalization(net)
net = conv_2d(net, 256, 5, activation='relu')
net = max_pool_2d(net, 3, strides=2)
net = local_response_normalization(net)
net = fully_connected(net, 2048, activation='relu')
net = dropout(net, 0.5)
net = fully_connected(net, 17, activation='softmax')
net = regression(net,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=1e-3)
# Training
model = tflearn.DNN(net,
def architecture10(input, num_class):
conv1_7_7 = conv_2d(input,
64,
7,
strides=2,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3,
64,
1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce,
192,
3,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3,
kernel_size=3,
strides=2,
name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3,
64,
1,
activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3,
96,
1,
activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce,
128,
filter_size=3,
activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,
16,
filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce,
32,
filter_size=5,
activation='relu',
name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(
pool2_3_3,
kernel_size=3,
strides=1,
)
inception_3a_pool_1_1 = conv_2d(inception_3a_pool,
32,
filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([
inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1
],
mode='concat',
axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce,
192,
filter_size=3,
activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output,
32,
filter_size=1,
activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce,
96,
filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output,
kernel_size=3,
strides=1,
name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool,
64,
filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([
inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1
],
mode='concat',
axis=3,
name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output,
kernel_size=3,
strides=2,
name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3,
192,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3,
96,
filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce,
208,
filter_size=3,
activation='relu',
name='inception_4a_3_3')
# inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
# inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3,
kernel_size=3,
strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool,
64,
filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge(
[inception_4a_1_1, inception_4a_3_3, inception_4a_pool_1_1],
mode='concat',
axis=3,
name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output,
160,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output,
112,
filter_size=1,
activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce,
224,
filter_size=3,
activation='relu',
name='inception_4b_3_3')
# inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
# inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output,
kernel_size=3,
strides=1,
name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool,
64,
filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge(
[inception_4b_1_1, inception_4b_3_3, inception_4b_pool_1_1],
mode='concat',
axis=3,
name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce,
256,
filter_size=3,
activation='relu',
name='inception_4c_3_3')
# inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
# inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output,
kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool,
64,
filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge(
[inception_4c_1_1, inception_4c_3_3, inception_4c_pool_1_1],
mode='concat',
axis=3,
name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output,
112,
filter_size=1,
activation='relu',
name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output,
144,
filter_size=1,
activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce,
288,
filter_size=3,
activation='relu',
name='inception_4d_3_3')
# inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
# inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output,
kernel_size=3,
strides=1,
name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool,
64,
filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge(
[inception_4d_1_1, inception_4d_3_3, inception_4d_pool_1_1],
mode='concat',
axis=3,
name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output,
256,
filter_size=1,
activation='relu',
name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output,
160,
filter_size=1,
activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_4e_3_3')
# inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
# inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output,
kernel_size=3,
strides=1,
name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool,
128,
filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge(
[inception_4e_1_1, inception_4e_3_3, inception_4e_pool_1_1],
axis=3,
mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output,
kernel_size=3,
strides=2,
name='pool_3_3')
# inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
# inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
# inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
# inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
# inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
# inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
# inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
# inception_5a_output = merge([inception_5a_1_1, inception_5a_pool_1_1], axis=3,mode='concat')
# inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
# inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
# inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
# inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
# inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
# inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
# inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
# inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_pool_1_1], axis=3, mode='concat')
# pool5_7_7 = avg_pool_2d(inception_4e_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(inception_4e_output, 0.4)
loss = fully_connected(pool5_7_7, num_class, activation='softmax')
network = regression(loss,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
return tflearn.DNN(network,
checkpoint_path='model_googlenet',
max_checkpoints=1,
tensorboard_verbose=0)
X = h5f['X']
Y = h5f['Y']
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_90degrees_rotation(rotations=[0, 2])
network = input_data(shape=[None, 300, 300, 3], data_augmentation=img_aug)
conv1_7_7 = conv_2d(network,
64,
7,
strides=2,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3,
64,
1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce,
192,
3,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3,
kernel_size=3,
strides=2,
name='pool2_3_3_s2')
def build_model(self):
network = input_data(shape=[None, self.image_size, self.image_size, 3])
conv1_7_7 = conv_2d(network,
64,
7,
strides=2,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3,
64,
1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce,
192,
3,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3,
kernel_size=3,
strides=2,
name='pool2_3_3_s2')
# 3a
inception_3a_1_1 = conv_2d(pool2_3_3,
64,
1,
activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3,
96,
1,
activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce,
128,
filter_size=3,
activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,
16,
filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce,
32,
filter_size=5,
activation='relu',
name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3,
kernel_size=3,
strides=1,
name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool,
32,
filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
inception_3a_output = merge([
inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1
],
mode='concat',
axis=3)
# 3b
inception_3b_1_1 = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce,
192,
filter_size=3,
activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output,
32,
filter_size=1,
activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce,
96,
filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output,
kernel_size=3,
strides=1,
name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool,
64,
filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
inception_3b_output = merge([
inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1
],
mode='concat',
axis=3,
name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output,
kernel_size=3,
strides=2,
name='pool3_3_3')
# 4a
inception_4a_1_1 = conv_2d(pool3_3_3,
192,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3,
96,
filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce,
208,
filter_size=3,
activation='relu',
name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3,
16,
filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce,
48,
filter_size=5,
activation='relu',
name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3,
kernel_size=3,
strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool,
64,
filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([
inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1
],
mode='concat',
axis=3,
name='inception_4a_output')
# 4b
inception_4b_1_1 = conv_2d(inception_4a_output,
160,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output,
112,
filter_size=1,
activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce,
224,
filter_size=3,
activation='relu',
name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output,
24,
filter_size=1,
activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output,
kernel_size=3,
strides=1,
name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool,
64,
filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([
inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1
],
mode='concat',
axis=3,
name='inception_4b_output')
# 4c
inception_4c_1_1 = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce,
256,
filter_size=3,
activation='relu',
name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output,
24,
filter_size=1,
activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output,
kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool,
64,
filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([
inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1
],
mode='concat',
axis=3,
name='inception_4c_output')
# 4d
inception_4d_1_1 = conv_2d(inception_4c_output,
112,
filter_size=1,
activation='relu',
name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output,
144,
filter_size=1,
activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce,
288,
filter_size=3,
activation='relu',
name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output,
32,
filter_size=1,
activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output,
kernel_size=3,
strides=1,
name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool,
64,
filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([
inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1
],
mode='concat',
axis=3,
name='inception_4d_output')
# 4e
inception_4e_1_1 = conv_2d(inception_4d_output,
256,
filter_size=1,
activation='relu',
name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output,
160,
filter_size=1,
activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output,
32,
filter_size=1,
activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output,
kernel_size=3,
strides=1,
name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool,
128,
filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([
inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1
],
axis=3,
mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output,
kernel_size=3,
strides=2,
name='pool_3_3')
# 5a
inception_5a_1_1 = conv_2d(pool4_3_3,
256,
filter_size=1,
activation='relu',
name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3,
160,
filter_size=1,
activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3,
32,
filter_size=1,
activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3,
kernel_size=3,
strides=1,
name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool,
128,
filter_size=1,
activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge([
inception_5a_1_1, inception_5a_3_3, inception_5a_5_5,
inception_5a_pool_1_1
],
axis=3,
mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output,
384,
filter_size=1,
activation='relu',
name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output,
192,
filter_size=1,
activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce,
384,
filter_size=3,
activation='relu',
name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output,
48,
filter_size=1,
activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output,
kernel_size=3,
strides=1,
name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool,
128,
filter_size=1,
activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([
inception_5b_1_1, inception_5b_3_3, inception_5b_5_5,
inception_5b_pool_1_1
],
axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7,
len(self.classes),
activation='softmax')
network = regression(loss,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=self.learning_rate)
model = tflearn.DNN(network, tensorboard_dir='log')
return model
def inception_v3(width,
height,
frame_count,
lr,
output=8,
model_name='AIcar.model'):
network = input_data(shape=[None, width, height, 3], name='input')
conv1_7_7 = conv_2d(network,
64,
28,
strides=4,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 9, strides=4)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3,
64,
1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce,
192,
12,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3,
kernel_size=12,
strides=2,
name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3,
64,
1,
activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3,
96,
1,
activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce,
128,
filter_size=12,
activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,
16,
filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce,
32,
filter_size=15,
activation='relu',
name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(
pool2_3_3,
kernel_size=12,
strides=1,
)
inception_3a_pool_1_1 = conv_2d(inception_3a_pool,
32,
filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([
inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1
],
mode='concat',
axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce,
192,
filter_size=3,
activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output,
32,
filter_size=1,
activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce,
96,
filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output,
kernel_size=3,
strides=1,
name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool,
64,
filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([
inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1
],
mode='concat',
axis=3,
name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output,
kernel_size=3,
strides=2,
name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3,
192,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3,
96,
filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce,
208,
filter_size=3,
activation='relu',
name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3,
16,
filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce,
48,
filter_size=5,
activation='relu',
name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3,
kernel_size=3,
strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool,
64,
filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([
inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1
],
mode='concat',
axis=3,
name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output,
160,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output,
112,
filter_size=1,
activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce,
224,
filter_size=3,
activation='relu',
name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output,
24,
filter_size=1,
activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output,
kernel_size=3,
strides=1,
name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool,
64,
filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([
inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1
],
mode='concat',
axis=3,
name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce,
256,
filter_size=3,
activation='relu',
name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output,
24,
filter_size=1,
activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output,
kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool,
64,
filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([
inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1
],
mode='concat',
axis=3,
name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output,
112,
filter_size=1,
activation='relu',
name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output,
144,
filter_size=1,
activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce,
288,
filter_size=3,
activation='relu',
name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output,
32,
filter_size=1,
activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output,
kernel_size=3,
strides=1,
name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool,
64,
filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([
inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1
],
mode='concat',
axis=3,
name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output,
256,
filter_size=1,
activation='relu',
name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output,
160,
filter_size=1,
activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output,
32,
filter_size=1,
activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output,
kernel_size=3,
strides=1,
name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool,
128,
filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([
inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1
],
axis=3,
mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output,
kernel_size=3,
strides=2,
name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3,
256,
filter_size=1,
activation='relu',
name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3,
160,
filter_size=1,
activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3,
32,
filter_size=1,
activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3,
kernel_size=3,
strides=1,
name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool,
128,
filter_size=1,
activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge([
inception_5a_1_1, inception_5a_3_3, inception_5a_5_5,
inception_5a_pool_1_1
],
axis=3,
mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output,
384,
filter_size=1,
activation='relu',
name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output,
192,
filter_size=1,
activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce,
384,
filter_size=3,
activation='relu',
name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output,
48,
filter_size=1,
activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output,
kernel_size=3,
strides=1,
name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool,
128,
filter_size=1,
activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([
inception_5b_1_1, inception_5b_3_3, inception_5b_5_5,
inception_5b_pool_1_1
],
axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, output, activation='softmax')
momentum = tflearn.optimizers.Momentum(learning_rate=lr,
lr_decay=0.96,
decay_step=100)
#defualt: optimizer='momentum' learning_rate=lr
network = regression(loss,
optimizer=momentum,
loss='categorical_crossentropy',
name='targets')
model = tflearn.DNN(network,
max_checkpoints=0,
tensorboard_verbose=3,
tensorboard_dir='log')
return model
activation='prelu',
name='inception_3_2_reduce')
inception_3_2 = conv_2d(inception_3_2_reduce,
32,
filter_size=5,
activation='prelu',
name='inception_3_2')
conv2_1_3 = conv_2d(pool1_1,
64,
2,
strides=1,
activation='prelu',
name='conv2_1_3')
pool2_1_3 = max_pool_2d(conv2_1_3, 2, strides=1)
pool2_1_3 = local_response_normalization(pool2_1_3)
conv3_3 = conv_2d(pool2_1_3,
32,
2,
strides=1,
activation='prelu',
name='conv3_3')
pool3_3 = max_pool_2d(conv3_3, 3, strides=2)
#---------------------------------------------------------
inception_3_output = merge([inception_3_1, inception_3_2, pool3_3],
mode='concat',
axis=3)
pool4 = max_pool_2d(inception_3_output, kernel_size=2, strides=1)
pool4 = local_response_normalization(pool4)
def Convolutional_Model_Alexnet_second(img_height,
img_width,
color_channels,
n_classes,
run_id='',
second_graph=tf.Graph()):
# data size
pixels = img_height * img_width
batch_size = 64
# pool window sizes
pool_1_window_size = 3
pool_2_window_size = 3
pool_3_window_size = 3
# conv window sizes
conv_1_window_size = 11
conv_2_window_size = 5
conv_3_1_window_size = 3
conv_3_2_window_size = 3
conv_3_3_window_size = 3
# pool stride sizes
pool_1_strides = 2
pool_2_strides = 2
pool_3_strides = 2
# conv stride sizes
conv_1_strides = 4
conv_2_strides = None #Default
conv_3_1_strides = None #Default
conv_3_2_strides = None #Default
conv_3_3_strides = None #Default
# compressed data size
compressed_img_height = img_height / pool_1_window_size / pool_2_window_size
compressed_img_width = img_width / pool_1_window_size / pool_2_window_size
# nodes
n_nodes_conv_layer_1 = 96
n_nodes_conv_layer_2 = 256
n_nodes_conv_layer_3_1 = 384
n_nodes_conv_layer_3_2 = 384
n_nodes_conv_layer_3_3 = 256
n_nodes_fc_layer_4 = 4096
n_nodes_fc_layer_5 = 4096
# input changes for fully connected
n_inputs_fc_layer_3 = compressed_img_width * compressed_img_height * n_nodes_conv_layer_2
#
with second_graph.as_default():
# Input Layer
second_convnet = input_data(
shape=[None, img_width, img_height, color_channels],
name='{}_input'.format(
run_id)) # name should be different between models
# Convolution - Pool Layer 1
second_convnet = conv_2d(second_convnet,
n_nodes_conv_layer_1,
conv_1_window_size,
strides=conv_1_strides,
activation='relu')
second_convnet = max_pool_2d(second_convnet,
pool_1_window_size,
strides=pool_1_strides)
second_convnet = local_response_normalization(second_convnet)
# Convolution - Pool Layer 2
second_convnet = conv_2d(second_convnet,
n_nodes_conv_layer_2,
conv_2_window_size,
activation='relu')
second_convnet = max_pool_2d(second_convnet,
pool_2_window_size,
strides=pool_2_strides)
second_convnet = local_response_normalization(second_convnet)
# 3 Convolutions 1 Pool Layer 3
second_convnet = conv_2d(second_convnet,
n_nodes_conv_layer_3_1,
conv_3_1_window_size,
activation='relu')
second_convnet = conv_2d(second_convnet,
n_nodes_conv_layer_3_2,
conv_3_2_window_size,
activation='relu')
second_convnet = conv_2d(second_convnet,
n_nodes_conv_layer_3_3,
conv_3_3_window_size,
activation='relu')
second_convnet = max_pool_2d(second_convnet,
pool_3_window_size,
strides=pool_3_strides)
second_convnet = local_response_normalization(second_convnet)
# Fully connected layer 4
second_convnet = fully_connected(second_convnet,
n_nodes_fc_layer_4,
activation='tanh')
second_convnet = dropout(second_convnet, 0.5) # 50% keep rate
# Fully connected layer 4
second_convnet = fully_connected(second_convnet,
n_nodes_fc_layer_5,
activation='tanh')
second_convnet = dropout(second_convnet, 0.5) # 50% keep rate
###
# Output layer
second_convnet = fully_connected(second_convnet,
n_classes,
activation='softmax')
second_convnet = regression(
second_convnet,
optimizer='momentum',
learning_rate=0.001,
loss='categorical_crossentropy',
name='{}_targets'.format(
run_id) # name should be different between models
)
# Set the model to follow this network
# tensorboard_verbose = 0: Loss, Accuracy (Best Speed)
tensorboard_log = MakeLogFile('')
second_model = tflearn.DNN(second_convnet,
tensorboard_dir=tensorboard_log)
return second_model, second_graph
def google_graph(X):
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)
img_aug.add_random_crop([64, 64], padding=4)
network = input_data(shape=[None, 64, 64, 3], data_preprocessing=img_prep, data_augmentation=img_aug)
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1],
mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu',
name='inception_3b_pool_1_1')
# merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1],
mode='concat', axis=3, name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1],
mode='concat', axis=3, name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1],
mode='concat', axis=3, name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1],
mode='concat', axis=3, name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1],
mode='concat', axis=3, name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3,
mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3,
mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
network = fully_connected(pool5_7_7, 2, activation='softmax')
network = regression(network, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.0005)
google_model = tflearn.DNN(network)
google_model.load('model\\google\\jun_glnet_cat_dog_final.tflearn')
google_result = google_model.predict(X)
return google_result
from tensorflow.examples.tutorials.mnist import input_data
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
# Data loading and preprocessing
mnist = input_data.read_data_sets("/data/", one_hot=True)
X, Y, testX, testY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
# Building convolutional network
network = tflearn.input_data(shape=[None, 28, 28, 1], name='input')
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit({'input': X}, {'target': Y}, n_epoch=20,
validation_set=({'input': testX}, {'target': testY}),
g_tr_im = [g_im[x] for x in shuffle_ids]
g_tr_lbl = [g_lbl[x] for x in shuffle_ids]
g_tr_files = [g_files[x] for x in shuffle_ids]
g_tr_features = [g_features[x] for x in shuffle_ids]
# Building 'AlexNet'
#modified for image size
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_blur(sigma_max=3.)
img_aug.add_random_rotation(max_angle=25.)
img_aug.add_random_flip_updown()
input_data = input_data(shape=[None, 227, 227, 3], data_augmentation=img_aug)
conv0 = conv_2d(input_data, 96, 11, strides=4, activation='relu')
max0 = max_pool_2d(conv0, 3, strides=2)
local0 = local_response_normalization(max0)
conv1 = conv_2d(local0, 256, 5, activation='relu')
max1 = max_pool_2d(conv1, 3, strides=2)
local1 = local_response_normalization(max1)
conv2 = conv_2d(local1, 384, 3, activation='relu')
conv3 = conv_2d(conv2, 384, 3, activation='relu')
conv4 = conv_2d(conv3, 256, 3, activation='relu')
max2 = max_pool_2d(conv4, 3, strides=2)
local2 = local_response_normalization(max2)
full1 = fully_connected(local2, 4096, activation='tanh')
drop1 = dropout(full1, 0.5)
full2 = fully_connected(drop1, 4096, activation='tanh', name='layer')
drop2 = dropout(full2, 0.5)
full3 = fully_connected(drop2, 6, activation='softmax')
network = regression(full3,
optimizer='momentum',
def GoogLeNet1(network, scale=False):
if scale is True:
network = scale(network)
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3,name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3,mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
network = fully_connected(pool5_7_7, output_dim,activation='sigmoid')
return network
inputBatchXY1 = np.zeros((NumberOfSamples*totalNumberOfSamples,windowSize1,windowSize1,2))
inputBatchXZ1 = np.zeros((NumberOfSamples*totalNumberOfSamples,windowSize1,windowSize1,2))
inputBatchYZ1 = np.zeros((NumberOfSamples*totalNumberOfSamples,windowSize1,windowSize1,2))
inputBatchXY2 = np.zeros((NumberOfSamples*totalNumberOfSamples,windowSize2,windowSize2,2))
inputBatchXZ2 = np.zeros((NumberOfSamples*totalNumberOfSamples,windowSize2,windowSize2,2))
inputBatchYZ2 = np.zeros((NumberOfSamples*totalNumberOfSamples,windowSize2,windowSize2,2))
inputBatchLabel = np.zeros(NumberOfSamples*totalNumberOfSamples)
numberOfValuesBorderChecking = borderCheckingSize * borderCheckingSize * borderCheckingSize
# NETWORK XY
networkXY0 = input_data(shape=[None, windowSize0, windowSize0, 2], name='inputXY0')
networkXY0 = conv_2d(networkXY0, 24, 5, activation='relu',padding='same', regularizer="L2",name='XY0')
networkXY0 = max_pool_2d(networkXY0, 2)
networkXY0 = local_response_normalization(networkXY0)
networkXY0 = conv_2d(networkXY0, 32, 3, activation='relu',padding='same', regularizer="L2")
networkXY0 = max_pool_2d(networkXY0, 2)
networkXY0 = local_response_normalization(networkXY0)
networkXY0 = conv_2d(networkXY0, 48, 3, activation='relu',padding='same', regularizer="L2")
networkXY0 = max_pool_2d(networkXY0,2)
networkXY0 = fully_connected(networkXY0, 256, activation='tanh')
networkXY0 = dropout(networkXY0, 0.5)
networkXY1 = input_data(shape=[None, windowSize1, windowSize1, 2], name='inputXY1')
networkXY1 = conv_2d(networkXY1, 24, 7, activation='relu',padding='same', regularizer="L2",name='XY1')
networkXY1 = max_pool_2d(networkXY1, 2)
networkXY1 = local_response_normalization(networkXY1)
networkXY1 = conv_2d(networkXY1, 32, 5, activation='relu',padding='same', regularizer="L2")
networkXY1 = max_pool_2d(networkXY1, 2)
networkXY1 = local_response_normalization(networkXY1)
def _model4():
# Taken from TFLearn examples and based on Googles Inception. DO NOT RUN!!!!
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
# 3a
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
# 3b
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1')
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
# 4a
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
# 4b
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
# 4c
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output')
# 4d
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
# 4e
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
# 5a
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
# fc
loss = fully_connected(pool5_7_7, len(yTest[0]), activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
# to train
model = tflearn.DNN(network, checkpoint_path='model_googlenet',
max_checkpoints=1, tensorboard_verbose=2)
model.load(_path)
pred = model.predict(xTest)
df = pd.DataFrame(pred)
df.to_csv(_path + ".csv")
newList = pred.copy()
newList = convert2(newList)
if _CSV: makeCSV(newList)
pred = convert2(pred)
pred = convert3(pred)
yTest = convert3(yTest)
print(metrics.confusion_matrix(yTest, pred))
print(metrics.classification_report(yTest, pred))
print('Accuracy', accuracy_score(yTest, pred))
print()
if _wrFile: writeTest(pred)
def __init__(self, x, y, bn, classification):
self.x = x
self.y = y
conv1_7_7 = conv_2d(x, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
# 3a
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu',
name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu',
name='inception_3a_pool_1_1')
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1],
mode='concat', axis=3)
# 3b
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu',
name='inception_3b_pool_1_1')
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1],
mode='concat', axis=3, name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
# 4a
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu',
name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu',
name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1],
mode='concat', axis=3, name='inception_4a_output')
# 4b
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu',
name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu',
name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1],
mode='concat', axis=3, name='inception_4b_output')
# 4c
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu',
name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu',
name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1],
mode='concat', axis=3, name='inception_4c_output')
# 4d
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu',
name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu',
name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1],
mode='concat', axis=3, name='inception_4d_output')
# 4e
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu',
name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu',
name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1],
axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
# 5a
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu',
name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu',
name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1],
axis=3, mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu',
name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu',
name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1],
axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
fc1 = tf.layers.flatten(pool5_7_7)
self.learning_rate = tf.placeholder(tf.float32)
self.global_step = tf.Variable(0, trainable=False, name='global_step')
if classification is True:
self.logits = dense(fc1, NUM_CLASSES, fn=None, batch_norm=True)
self.loss = tf.losses.softmax_cross_entropy(onehot_labels=self.y, logits=self.logits)
self.lr_decay = tf.train.exponential_decay(self.learning_rate, self.global_step, 1000, 0.9, staircase=True)
self.extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(self.extra_update_ops):
self.adam = tf.train.AdamOptimizer(self.lr_decay).minimize(self.loss,
global_step=self.global_step)
self.sgd = tf.train.GradientDescentOptimizer(self.lr_decay).minimize(self.loss,
global_step=self.global_step)
self.rms = tf.train.RMSPropOptimizer(self.lr_decay).minimize(self.loss,
global_step=self.global_step)
self.momentum = tf.train.MomentumOptimizer(self.lr_decay, momentum=0.9).minimize(self.loss,
global_step=self.global_step)
self.y_prob = tf.nn.softmax(self.logits)
self.y_pred = tf.argmax(self.y_prob, 1)
self.correct_prediction = tf.equal(self.y_pred, tf.arg_max(y, 1))
self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))
tf.summary.scalar("accuray", self.accuracy)
tf.summary.scalar("loss", self.loss)
else:
self.logits = tf.layers.dense(fc1, 1, activation=tf.nn.relu)
self.loss = tf.losses.mean_squared_error(labels=self.y, predictions=self.logits)
self.lr_decay = tf.train.exponential_decay(self.learning_rate, self.global_step, 1000, 0.9, staircase=True)
self.extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(self.extra_update_ops):
self.adam = tf.train.AdamOptimizer(self.lr_decay).minimize(self.loss,
global_step=self.global_step)
self.sgd = tf.train.GradientDescentOptimizer(self.lr_decay).minimize(self.loss,
global_step=self.global_step)
self.rms = tf.train.RMSPropOptimizer(self.lr_decay).minimize(self.loss,
global_step=self.global_step)
self.momentum = tf.train.MomentumOptimizer(self.lr_decay, momentum=0.9).minimize(self.loss,
global_step=self.global_step)
tf.summary.scalar("loss", self.loss)
self.merged_summary_op = tf.summary.merge_all()
check_path = 'model_' + optimizer_name + str(learning_rate)
max_checkpoints = 10
snapshot_step=200
is_snapshot_epoch=False
# Dataset
import tflearn.datasets.oxflower17 as oxflower17
X, Y = oxflower17.load_data(dirname='/home/jiawei/dataset/17flowers/', one_hot=True, resize_pics=(227, 227))
tflearn.config.init_graph(log_device=True, soft_placement=True)
# Building 'AlexNet'
network = input_data(shape=[None, 227, 227, 3])
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 17, activation='softmax')
network = regression(network, optimizer=optimizer_name,
loss=loss_name,
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import tflearn.datasets.mnist as mnist
X, Y, X_test, Y_test = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
X_test = X_test.reshape([-1, 28, 28, 1])
# Building the network
CNN = input_data(shape=[None, 28, 28, 1], name='input')
CNN = conv_2d(CNN, 32, 5, activation='relu', regularizer='L2')
CNN = max_pool_2d(CNN, 2)
CNN = local_response_normalization(CNN)
CNN = conv_2d(CNN, 64, 5, activation='relu', regularizer='L2')
CNN = max_pool_2d(CNN, 2)
CNN = local_response_normalization(CNN)
CNN = fully_connected(CNN, 1024, activation=None)
CNN = dropout(CNN, 0.5)
CNN = fully_connected(CNN, 10, activation='softmax')
CNN = regression(CNN,
optimizer='adam',
learning_rate=0.0001,
loss='categorical_crossentropy',
name='target')
# Training the network
model = tflearn.DNN(CNN,
tensorboard_verbose=0,
def googlenet(WIDTH, HEIGHT, LEARNING_RATE, OUTPUT=5):
network = input_data(shape=[None, WIDTH, HEIGHT, 3], name='input')
conv1_7_7 = conv_2d(network,
64,
7,
strides=2,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3,
64,
1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce,
192,
3,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3,
kernel_size=3,
strides=2,
name='pool2_3_3_s2')
# 3a
inception_3a_1_1 = conv_2d(pool2_3_3,
64,
1,
activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3,
96,
1,
activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce,
128,
filter_size=3,
activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,
16,
filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce,
32,
filter_size=5,
activation='relu',
name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3,
kernel_size=3,
strides=1,
name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool,
32,
filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
inception_3a_output = merge([
inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1
],
mode='concat',
axis=3)
# 3b
inception_3b_1_1 = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce,
192,
filter_size=3,
activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output,
32,
filter_size=1,
activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce,
96,
filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output,
kernel_size=3,
strides=1,
name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool,
64,
filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
inception_3b_output = merge([
inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1
],
mode='concat',
axis=3,
name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output,
kernel_size=3,
strides=2,
name='pool3_3_3')
# 4a
inception_4a_1_1 = conv_2d(pool3_3_3,
192,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3,
96,
filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce,
208,
filter_size=3,
activation='relu',
name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3,
16,
filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce,
48,
filter_size=5,
activation='relu',
name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3,
kernel_size=3,
strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool,
64,
filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([
inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1
],
mode='concat',
axis=3,
name='inception_4a_output')
# 4b
inception_4b_1_1 = conv_2d(inception_4a_output,
160,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output,
112,
filter_size=1,
activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce,
224,
filter_size=3,
activation='relu',
name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output,
24,
filter_size=1,
activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output,
kernel_size=3,
strides=1,
name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool,
64,
filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([
inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1
],
mode='concat',
axis=3,
name='inception_4b_output')
# 4c
inception_4c_1_1 = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce,
256,
filter_size=3,
activation='relu',
name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output,
24,
filter_size=1,
activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output,
kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool,
64,
filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([
inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1
],
mode='concat',
axis=3,
name='inception_4c_output')
# 4d
inception_4d_1_1 = conv_2d(inception_4c_output,
112,
filter_size=1,
activation='relu',
name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output,
144,
filter_size=1,
activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce,
288,
filter_size=3,
activation='relu',
name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output,
32,
filter_size=1,
activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output,
kernel_size=3,
strides=1,
name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool,
64,
filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([
inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1
],
mode='concat',
axis=3,
name='inception_4d_output')
# 4e
inception_4e_1_1 = conv_2d(inception_4d_output,
256,
filter_size=1,
activation='relu',
name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output,
160,
filter_size=1,
activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output,
32,
filter_size=1,
activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output,
kernel_size=3,
strides=1,
name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool,
128,
filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([
inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1
],
axis=3,
mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output,
kernel_size=3,
strides=2,
name='pool_3_3')
# 5a
inception_5a_1_1 = conv_2d(pool4_3_3,
256,
filter_size=1,
activation='relu',
name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3,
160,
filter_size=1,
activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3,
32,
filter_size=1,
activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3,
kernel_size=3,
strides=1,
name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool,
128,
filter_size=1,
activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge([
inception_5a_1_1, inception_5a_3_3, inception_5a_5_5,
inception_5a_pool_1_1
],
axis=3,
mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output,
384,
filter_size=1,
activation='relu',
name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output,
192,
filter_size=1,
activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce,
384,
filter_size=3,
activation='relu',
name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output,
48,
filter_size=1,
activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output,
kernel_size=3,
strides=1,
name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool,
128,
filter_size=1,
activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([
inception_5b_1_1, inception_5b_3_3, inception_5b_5_5,
inception_5b_pool_1_1
],
axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
# fc
loss = fully_connected(pool5_7_7, OUTPUT, activation='softmax')
network = regression(loss,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=LEARNING_RATE,
name="targets")
# to train
model = tflearn.DNN(network,
checkpoint_path='model_googlenet2',
max_checkpoints=1,
tensorboard_verbose=2,
tensorboard_dir="log")
return model
def get_network():
network = input_data(
shape=[None, gvg.board_size, gvg.board_size, gvg.board_channels],
name="input")
conv1_3_3 = conv_2d(network,
64,
3,
strides=2,
activation='relu',
name='conv1_3_3')
conv1_3_3 = local_response_normalization(conv1_3_3)
conv2_3_3 = conv_2d(conv1_3_3, 192, 3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = conv2_3_3
inception_3a_1_1 = conv_2d(pool2_3_3,
64,
1,
activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3,
96,
1,
activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce,
128,
filter_size=3,
activation='relu',
name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,
16,
filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce,
32,
filter_size=5,
activation='relu',
name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(
pool2_3_3,
kernel_size=3,
strides=1,
)
inception_3a_pool_1_1 = conv_2d(inception_3a_pool,
32,
filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([
inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1
],
mode='concat',
axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce,
192,
filter_size=3,
activation='relu',
name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output,
32,
filter_size=1,
activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce,
96,
filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output,
kernel_size=3,
strides=1,
name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool,
64,
filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([
inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1
],
mode='concat',
axis=3,
name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output,
kernel_size=3,
strides=2,
name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3,
192,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3,
96,
filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce,
208,
filter_size=3,
activation='relu',
name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3,
16,
filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce,
48,
filter_size=5,
activation='relu',
name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3,
kernel_size=3,
strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool,
64,
filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([
inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1
],
mode='concat',
axis=3,
name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output,
160,
filter_size=1,
activation='relu',
name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output,
112,
filter_size=1,
activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce,
224,
filter_size=3,
activation='relu',
name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output,
24,
filter_size=1,
activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output,
kernel_size=3,
strides=1,
name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool,
64,
filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([
inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1
],
mode='concat',
axis=3,
name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce,
256,
filter_size=3,
activation='relu',
name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output,
24,
filter_size=1,
activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output,
kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool,
64,
filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([
inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1
],
mode='concat',
axis=3,
name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output,
112,
filter_size=1,
activation='relu',
name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output,
144,
filter_size=1,
activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce,
288,
filter_size=3,
activation='relu',
name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output,
32,
filter_size=1,
activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output,
kernel_size=3,
strides=1,
name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool,
64,
filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([
inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1
],
mode='concat',
axis=3,
name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output,
256,
filter_size=1,
activation='relu',
name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output,
160,
filter_size=1,
activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output,
32,
filter_size=1,
activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output,
kernel_size=3,
strides=1,
name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool,
128,
filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([
inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1
],
axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_4e_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.6)
loss = fully_connected(pool5_7_7,
gvg.board_size * gvg.board_size,
activation='softmax')
network = regression(loss,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.0025,
name="target")
return network
