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 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 neural_network_model(input_size):
network = input_data(shape=[None, input_size, 1], name='input')
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=LR,
loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(network, 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 _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 train_nmf_network(mfcc_array, sdr_array, n_epochs, take):
"""
:param mfcc_array:
:param sdr_array:
:param n_epochs:
:param take:
:return:
"""
with tf.Graph().as_default():
network = input_data(shape=[None, 13, 100, 1])
network = conv_2d(network, 32, [5, 5], activation="relu", regularizer="L2")
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, [5, 5], activation="relu", regularizer="L2")
network = max_pool_2d(network, 2)
network = fully_connected(network, 128, activation="relu")
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation="relu")
network = dropout(network, 0.8)
network = fully_connected(network, 1, activation="linear")
regress = tflearn.regression(network, optimizer="rmsprop", loss="mean_square", learning_rate=0.001)
# Training
model = tflearn.DNN(regress) # , session=sess)
model.fit(
mfcc_array,
sdr_array,
n_epoch=n_epochs,
snapshot_step=1000,
show_metric=True,
run_id="repet_choice_{0}_epochs_take_{1}".format(n_epochs, take),
)
return model
def neural_network_model(input_size):
"""
Function is to build NN based on the input size
:param input_size: feature size of each observation
:return: tensorflow model
"""
network = input_data(shape=[None, input_size], name='input')
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, learning_rate=LR, name='targets')
model = tflearn.DNN(network, tensorboard_dir='logs/ann/ann_0')
return model
def train_repet_network(beat_spectrum_array, sdr_array, n_epochs, take):
"""
:param beat_spectrum_array:
:param sdr_array:
:param n_epochs:
:param take:
:return:
"""
beat_spec_len = 432
with tf.Graph().as_default():
input_layer = input_data(shape=[None, beat_spec_len, 1])
conv1 = conv_1d(input_layer, 32, 4, activation="relu", regularizer="L2")
max_pool1 = max_pool_1d(conv1, 2)
conv2 = conv_1d(max_pool1, 64, 80, activation="relu", regularizer="L2")
max_pool2 = max_pool_1d(conv2, 2)
fully1 = fully_connected(max_pool2, 128, activation="relu")
dropout1 = dropout(fully1, 0.8)
fully2 = fully_connected(dropout1, 256, activation="relu")
dropout2 = dropout(fully2, 0.8)
linear = fully_connected(dropout2, 1, activation="linear")
regress = tflearn.regression(linear, optimizer="rmsprop", loss="mean_square", learning_rate=0.001)
# Training
model = tflearn.DNN(regress) # , session=sess)
model.fit(
beat_spectrum_array,
sdr_array,
n_epoch=n_epochs,
snapshot_step=1000,
show_metric=True,
run_id="repet_choice_{0}_epochs_take_{1}".format(n_epochs, take),
)
return model
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 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 main():
pickle_folder = '../pickles_rolloff'
pickle_folders_to_load = [f for f in os.listdir(pickle_folder) if os.path.isdir(join(pickle_folder, f))]
pickle_folders_to_load = sorted(pickle_folders_to_load)
# pickle parameters
fg_or_bg = 'background'
sdr_type = 'sdr'
feature = 'sim_mat'
beat_spec_len = 432
# training params
n_classes = 16
training_percent = 0.85
testing_percent = 0.15
validation_percent = 0.00
# set up training, testing, & validation partitions
print('Loading sim_mat and sdrs')
sim_mat_array, sdr_array = get_generated_data(feature, fg_or_bg, sdr_type)
print('sim_mat and sdrs loaded')
print('splitting and grooming data')
train, test, validate = split_into_sets(len(pickle_folders_to_load), training_percent,
testing_percent, validation_percent)
trainX = np.expand_dims([sim_mat_array[i] for i in train], -1)
trainY = np.expand_dims([sdr_array[i] for i in train], -1)
testX = np.expand_dims([sim_mat_array[i] for i in test], -1)
testY = np.array([sdr_array[i] for i in test])
print('setting up CNN')
# Building convolutional network
network = input_data(shape=[None, beat_spec_len, beat_spec_len, 1])
network = conv_2d(network, 32, 10, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 20, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
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, 1, activation='linear')
regress = tflearn.regression(network, optimizer='sgd', loss='mean_square', learning_rate=0.01)
print('running CNN')
# Training
model = tflearn.DNN(regress, tensorboard_verbose=1)
model.fit(trainX, trainY, n_epoch=10,
snapshot_step=1000, show_metric=True, run_id='{} classes'.format(n_classes - 1))
predicted = np.array(model.predict(testX))[:,0]
print('plotting')
plot(testY, predicted)
def build_network():
network = tflearn.input_data(shape=[None, 2])
network = tflearn.fully_connected(network, 64, activation='relu')
network = dropout(network, 0.9)
network = tflearn.fully_connected(network, 128, activation='relu')
network = dropout(network, 0.9)
network = tflearn.fully_connected(network, 2, activation='softmax')
network = tflearn.regression(network, optimizer='sgd', learning_rate=0.1,
loss='categorical_crossentropy')
return network
def main():
pickle_folder = '../pickles_rolloff'
pickle_folders_to_load = [f for f in os.listdir(pickle_folder) if os.path.isdir(join(pickle_folder, f))]
pickle_folders_to_load = sorted(pickle_folders_to_load)
# pickle parameters
fg_or_bg = 'background'
sdr_type = 'sdr'
feature = 'beat_spec'
beat_spec_len = 432
# training params
n_classes = 16
training_percent = 0.85
testing_percent = 0.15
validation_percent = 0.00
# set up training, testing, & validation partitions
beat_spec_array, sdr_array = load_beat_spec_and_sdrs(pickle_folders_to_load, pickle_folder,
feature, fg_or_bg, sdr_type)
train, test, validate = split_into_sets(len(pickle_folders_to_load), training_percent,
testing_percent, validation_percent)
trainX = np.expand_dims([beat_spec_array[i] for i in train], -1)
trainY = np.expand_dims([sdr_array[i] for i in train], -1)
testX = np.expand_dims([beat_spec_array[i] for i in test], -1)
testY = np.array([sdr_array[i] for i in test])
# Building convolutional network
network = input_data(shape=[None, beat_spec_len, 1])
network = conv_1d(network, 32, 4, activation='relu', regularizer="L2")
network = max_pool_1d(network, 2)
network = conv_1d(network, 64, 80, activation='relu', regularizer="L2")
network = max_pool_1d(network, 2)
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu') # look for non-tanh things???
network = dropout(network, 0.8)
network = fully_connected(network, 1, activation='linear')
regress = tflearn.regression(network, optimizer='sgd', loss='mean_square', learning_rate=0.01)
# Training
model = tflearn.DNN(regress, tensorboard_verbose=1)
model.fit(trainX, trainY, n_epoch=100,
snapshot_step=1000, show_metric=True, run_id='relus_100_3')
predicted = np.array(model.predict(testX))[:,0]
# pprint.pprint()
print("Test MSE: ", np.square(testY - predicted).mean())
plot(testY, predicted)
def build_model_1_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 = 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 main():
pickle_folder = 'pickles_combined'
# pickle parameters
fg_or_bg = 'background'
sdr_type = 'sdr'
feature = 'beat_spec'
# training params
training_percent = 0.85
testing_percent = 0.15
validation_percent = 0.00
beat_spec_max = 355
# set up training, testing, & validation partitions
beat_spec_array, sdr_array = unpickle_beat_spec_and_sdrs(pickle_folder, beat_spec_max)
train, test, validate = split_into_sets(len(beat_spec_array), training_percent,
testing_percent, validation_percent)
trainX = np.expand_dims([beat_spec_array[i] for i in train], -1)
trainY = np.expand_dims([sdr_array[i] for i in train], -1)
testX = np.expand_dims([beat_spec_array[i] for i in test], -1)
testY = np.array([sdr_array[i] for i in test])
# Building convolutional network
network = input_data(shape=[None, beat_spec_max, 1])
network = conv_1d(network, 32, 4, activation='relu', regularizer="L2")
network = max_pool_1d(network, 2)
network = conv_1d(network, 64, 80, activation='relu', regularizer="L2")
network = max_pool_1d(network, 2)
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu') # look for non-tanh things???
network = dropout(network, 0.8)
network = fully_connected(network, 1, activation='linear')
regress = tflearn.regression(network, optimizer='sgd', loss='mean_square', learning_rate=0.01)
start = time.time()
# Training
model = tflearn.DNN(regress, tensorboard_verbose=1)
model.fit(trainX, trainY, n_epoch=2000,
snapshot_step=1000, show_metric=True, run_id='mir1k_2000_truncate')
elapsed = (time.time() - start)
predicted = np.array(model.predict(testX))[:,0]
print("Test MSE: ", np.square(testY - predicted).mean())
print(elapsed, "seconds")
plot(testY, predicted)
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 _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 main():
"""
:return:
"""
pickle_folder = '../NMF/mfcc_pickles'
pickle_folders_to_load = [f for f in os.listdir(pickle_folder) if os.path.isdir(join(pickle_folder, f))]
fg_or_bg = 'background'
sdr_type = 'sdr'
feature = 'mfcc_clusters'
beat_spec_len = 432
n_epochs = 200
take = 1
# set up training, testing, & validation partitions
mfcc_array, sdr_array = load_mfcc_and_sdrs(pickle_folders_to_load, pickle_folder,
feature, fg_or_bg, sdr_type)
mfcc_array = np.expand_dims(mfcc_array, -1)
sdr_array = np.expand_dims(sdr_array, -1)
# Building convolutional network
network = input_data(shape=[None, 13, 100, 1])
network = conv_2d(network, 32, [5, 5], activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, [5, 5], activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 1, activation='linear')
regress = tflearn.regression(network, optimizer='rmsprop', loss='mean_square', learning_rate=0.001)
start = time.time()
# Training
model = tflearn.DNN(regress) # , session=sess)
model.fit(mfcc_array, sdr_array, n_epoch=n_epochs,
snapshot_step=1000, show_metric=True,
run_id='repet_save_{0}_epochs_take_{1}'.format(n_epochs, take))
elapsed = (time.time() - start)
print('Finished training after ' + elapsed + 'seconds. Saving...')
model_output_folder = 'network_outputs/'
model_output_file = join(model_output_folder, 'nmf_save_{0}_epochs_take_{1}'.format(n_epochs, take))
model.save(model_output_file)
def do_cnn(trainX, trainY,testX, testY):
global n_words
# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=MAX_DOCUMENT_LENGTH, value=0.)
testX = pad_sequences(testX, maxlen=MAX_DOCUMENT_LENGTH, 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], name='input')
network = tflearn.embedding(network, input_dim=n_words+1, output_dim=128)
branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")
branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")
branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")
network = merge([branch1, branch2, branch3], mode='concat', axis=1)
network = tf.expand_dims(network, 2)
network = global_max_pool(network)
network = dropout(network, 0.5)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(trainX, trainY, n_epoch = 20, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=32)
def createModel(nbClasses,imageSize):
print("[+] Creating model...")
convnet = input_data(shape=[None, imageSize, imageSize, 1], name='input')
convnet = conv_2d(convnet, 64, 2, activation='elu', weights_init="Xavier")
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 128, 2, activation='elu', weights_init="Xavier")
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 256, 2, activation='elu', weights_init="Xavier")
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 512, 2, activation='elu', weights_init="Xavier")
convnet = max_pool_2d(convnet, 2)
convnet = fully_connected(convnet, 1024, activation='elu')
convnet = dropout(convnet, 0.5)
convnet = fully_connected(convnet, nbClasses, activation='softmax')
convnet = regression(convnet, optimizer='rmsprop', loss='categorical_crossentropy')
model = tflearn.DNN(convnet)
print(" Model created! ✅")
return model
def do_cnn_doc2vec(trainX, testX, trainY, testY):
global max_features
print "CNN and doc2vec"
#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_features], name='input')
network = tflearn.embedding(network, input_dim=1000000, output_dim=128,validate_indices=False)
branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")
branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")
branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")
network = merge([branch1, branch2, branch3], mode='concat', axis=1)
network = tf.expand_dims(network, 2)
network = global_max_pool(network)
network = dropout(network, 0.8)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(trainX, trainY,
n_epoch=5, shuffle=True, validation_set=(testX, testY),
show_metric=True, batch_size=100,run_id="review")
def get_cnn_model(checkpoint_path='cnn_servo_model', width=72, height=48, depth=3, session=None):
# Inputs
network = input_data(shape=[None, height, width, depth], name='input')
# Convolution no.1
# Relu introduces non linearity into training
network = conv_2d(network, 8, [5, 3], activation='relu')
# Convolution no.2
network = conv_2d(network, 12, [5, 8], activation='relu')
# Convolution no.3
network = conv_2d(network, 16, [5, 16], activation='relu')
# Convolution no.4
network = conv_2d(network, 24, [3, 20], activation='relu')
# Convolution no.5
network = conv_2d(network, 24, [3, 24], activation='relu')
# Fully connected no.1
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
# Fully connected no.2
network = fully_connected(network, 100, activation='relu')
network = dropout(network, 0.8)
# Fully connected no.3
network = fully_connected(network, 50, activation='relu')
network = dropout(network, 0.8)
# Fully connected no.4
network = fully_connected(network, 10, activation='relu')
network = dropout(network, 0.8)
# Fully connected no.5
network = fully_connected(network, 1, activation='tanh')
# Regression
network = regression(network, loss='mean_square', metric='accuracy', learning_rate=1e-4,name='target')
# Verbosity yay nay
# 0 = nothing
model = tflearn.DNN(network, tensorboard_verbose=2, checkpoint_path=checkpoint_path, session=session)
return model
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 main():
"""
:return:
"""
# pickle parameters
fg_or_bg = 'background'
sdr_type = 'sdr'
feature = 'sim_mat'
beat_spec_len = 432
# set up training, testing, & validation partitions
sim_mat_array, sdr_array = get_generated_data(feature, fg_or_bg, sdr_type)
# training params
n_classes = 10
n_training_steps = 1000
training_step_size = 100
training_percent = 0.85
testing_percent = 0.15
validation_percent = 0.00
sdr_array_1h, hist = sdrs_to_one_hots(sdr_array, n_classes, True)
train, test, validate = split_into_sets(len(sim_mat_array), training_percent,
testing_percent, validation_percent)
# Building convolutional network
network = input_data(shape=[None, beat_spec_len, 1], name='input')
network = conv_1d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_1d(network, 2)
# network = local_response_normalization(network)
# network = batch_normalization(network)
# network = conv_1d(network, 64, 3, activation='relu', regularizer="L2")
# network = max_pool_1d(network, 2)
# network = local_response_normalization(network)
# network = batch_normalization(network)
# network = fully_connected(network, 128, activation='tanh')
# network = dropout(network, 0.5)
network = fully_connected(network, 512, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, n_classes, activation='softmax')
# network = fully_connected(network, 1, activation='linear')
network = regression(network, optimizer='adagrad', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
X = np.expand_dims(sim_mat_array, -1)
Y = np.array(sdr_array_1h)
# X = np.expand_dims([beat_spec_array[i] for i in train], -1)
# Y = np.array([sdr_array_1h[i] for i in train])
# testX = np.expand_dims([beat_spec_array[i] for i in test], -1)
# testY = np.array([sdr_array[i] for i in test])
# Training
model = tflearn.DNN(network, tensorboard_verbose=1)
model.fit({'input': X}, {'target': Y}, n_epoch=20,
validation_set=0.1,
snapshot_step=1000, show_metric=True, run_id='{} classes'.format(n_classes - 1))
def vggnet():
X, Y = oxflower17.load_data(one_hot=True,resize_pics=(227, 227))
# Building 'VGG Network'
network = input_data(shape=[None, 227, 227, 3])
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 128, 3, activation='relu')
network = conv_2d(network, 128, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 256, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = fully_connected(network, 4096, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 17, activation='softmax')
network = regression(network, optimizer='rmsprop',
loss='categorical_crossentropy',
learning_rate=0.0001)
# Training
model = tflearn.DNN(network, checkpoint_path='model_vgg',
max_checkpoints=1, tensorboard_verbose=0)
model.fit(X, Y, n_epoch=500, shuffle=True,
show_metric=True, batch_size=32, snapshot_step=500,
snapshot_epoch=False, run_id='vgg')
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 main():
"""
Trains a CNN architecture and plots the results over a validation set.
Returns:
"""
# Load the SDR and hist data
data = load_data('reverb_pan_full_sdr.txt', 'pickle/')
# split data into train and test sets
test_percent = 0.15
train, test, validate = split_into_sets(len(data['sdr']), 1-test_percent,
test_percent, 0)
x_train = np.expand_dims([data['input'][i] for i in train], -1)
y_train = np.expand_dims([data['sdr'][i] for i in train], -1)
x_test = np.expand_dims([data['input'][i] for i in test], -1)
y_test = np.expand_dims([data['sdr'][i] for i in test], -1)
# construct the CNN.
inp = input_data(shape=[None, 50, 50, 1], name='input')
# two convolutional layers with max pooling
conv1 = conv_2d(inp, 32, [5, 5], activation='relu', regularizer="L2")
max_pool = max_pool_2d(conv1, 2)
conv2 = conv_2d(max_pool, 64, [5, 5], activation='relu', regularizer="L2")
max_pool2 = max_pool_2d(conv2, 2)
# two fully connected layers
full = fully_connected(max_pool2, 128, activation='tanh')
full = dropout(full, 0.8)
full2 = fully_connected(full, 256, activation='tanh')
full2 = dropout(full2, 0.8)
# output regression node
out = fully_connected(full2, 1, activation='linear')
network = regression(out, optimizer='sgd', learning_rate=0.01, name='target', loss='mean_square')
model = tflearn.DNN(network, tensorboard_verbose=1, checkpoint_path='checkpoint.p',
tensorboard_dir='tmp/tflearn_logs/')
model.fit({'input': x_train}, {'target': y_train}, n_epoch=1000, validation_set=(x_test, y_test),
snapshot_step=10000, run_id='convnet_duet_3x3')
predicted = np.array(model.predict(x_test))[:,0]
plot(y_test, predicted)
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 cnn():
network = input_data(shape=[None, IMAGE_HEIGHT, IMAGE_WIDTH, 1], name='input')
network = conv_2d(network, 8, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = batch_normalization(network)
network = conv_2d(network, 16, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = batch_normalization(network)
network = conv_2d(network, 16, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = batch_normalization(network)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, CODE_LEN * MAX_CHAR, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target')
return network
def do_cnn(x,y):
global max_document_length
print "CNN and tf"
trainX, testX, trainY, testY = train_test_split(x, y, test_size=0.4, random_state=0)
y_test=testY
trainX = pad_sequences(trainX, maxlen=max_document_length, value=0.)
testX = pad_sequences(testX, maxlen=max_document_length, 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], name='input')
network = tflearn.embedding(network, input_dim=1000000, output_dim=128)
branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")
branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")
branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")
network = merge([branch1, branch2, branch3], mode='concat', axis=1)
network = tf.expand_dims(network, 2)
network = global_max_pool(network)
network = dropout(network, 0.8)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.001,
loss='categorical_crossentropy', name='target')
model = tflearn.DNN(network, tensorboard_verbose=0)
#if not os.path.exists(pkl_file):
# Training
model.fit(trainX, trainY,
n_epoch=5, shuffle=True, validation_set=0.1,
show_metric=True, batch_size=100,run_id="webshell")
# model.save(pkl_file)
#else:
# model.load(pkl_file)
y_predict_list=model.predict(testX)
#y_predict = list(model.predict(testX,as_iterable=True))
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 'y_predict_list:'
print y_predict_list
print 'y_predict:'
print y_predict
#print y_test
do_metrics(y_test, y_predict)
def otherception3(width, height, frame_count, lr, output=9, model_name = 'otherception.model', device = 'gpu', num = '0'):
# with tf.device('/{}:{}'.format(device,num)):
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=9, 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=15, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=12, 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
covnet = max_pool_2d(covnet, 2)
covnet = conv_2d(covnet, 32, 2, activation='relu')
covnet = max_pool_2d(covnet, 2)
covnet = conv_2d(covnet, 64, 2, activation='relu')
covnet = max_pool_2d(covnet, 2)
covnet = conv_2d(covnet, 32, 2, activation='relu')
covnet = max_pool_2d(covnet, 2)
covnet = conv_2d(covnet, 64, 2, activation='relu')
covnet = max_pool_2d(covnet, 2)
covnet = fully_connected(covnet, 1024, activation='relu')
covnet = dropout(covnet, 0.8)
covnet = fully_connected(covnet, 3, activation='softmax')
covnet = regression(
covnet,
optimizer='adam',
learning_rate=learning_rate,
loss='categorical_crossentropy',
name='targets'
)
model = tflearn.DNN(covnet, tensorboard_dir = 'log')
# if os.path.exists('{}.meta'.format(MODEL_NAME)):
# model.load(MODEL_NAME)
# print('model loaded')
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, _dropout)
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=_learning_rate)
#comment train/load sections accordingly
# Train the model
model = tflearn.DNN(network, tensorboard_dir='log', tensorboard_verbose=0)
#load the trained model
model.load(model_path)
X_train_sd = X_train_sd.reshape([-1, 13, 107, 1])
# Building convolutional network
network = input_data(shape=[None, 13, 107, 1], name='input')
network = conv_2d(network,
32,
3,
activation='relu',
regularizer="L2",
name='conv1')
network = max_pool_2d(network, 2, name='max1')
network = fully_connected(network,
128,
activation='tanh',
name='dense1')
network = dropout(network, 0.8, name='drop1')
network = fully_connected(network, 2, activation='softmax')
network = regression(network,
optimizer='adam',
learning_rate=0.01,
loss='categorical_crossentropy',
name='target')
# Define model with checkpoint (autosave)
model = tflearn.DNN(network, tensorboard_verbose=3)
# Train model with checkpoint every epoch and every 500 steps
model.fit(X_train_sd,
Y_train,
n_epoch=n_epoch,
show_metric=True,
def create_cnn_3d_VGG16():
#img_prep = ImagePreprocessing()
#img_prep.add_featurewise_zero_center(mean=0.25)
network = input_data(
shape=[None, IMG_SIZE_PX, IMG_SIZE_PX, IMG_SIZE_PX, 1])
network = conv_3d(network, 64, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 64, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 2, strides=2)
network = conv_3d(network, 64, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 32, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 2, strides=2)
network = conv_3d(network, 256, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 256, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 256, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 2, strides=2)
network = conv_3d(network, 512, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 512, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 512, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 2, strides=2)
network = conv_3d(network, 512, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 512, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 512, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 2, strides=2)
network = fully_connected(network, 2048)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
network = fully_connected(network, 2048)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
output = fully_connected(network, num_class, activation='softmax')
network = regression(output,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
return network
X = X.reshape([-1, 28, 28, 1]) #reshaping 784 pixel image into flat 28*28
test_x = test_x.reshape([-1, 28, 28,
1]) #reshaping 784 pixel image into flat 28*28
convnet = input_data(shape=[None, 28, 28, 1], name='input')
convnet = conv_2d(convnet, 32, 2, activation='relu') #Convolutional Layer 1
convnet = max_pool_2d(convnet, 2) #Pooling Layer 1
convnet = conv_2d(convnet, 64, 2, activation='relu') #Convolutional Layer 2
convnet = max_pool_2d(convnet, 2) #Pooling Layer 2
convnet = fully_connected(convnet, 1024,
activation='relu') #Fully Connected Layer
convnet = dropout(convnet, 0.8) # basically 80% of neurons will fire
convnet = fully_connected(convnet, 10,
activation='softmax') #Output Layer with Softmax
convnet = regression(convnet,
optimizer='adam',
learning_rate=0.01,
loss='categorical_crossentropy',
name='targets') #Regression layer
model = tflearn.models.DNN(
convnet, tensorboard_dir='log'
) #Deep Neural Network Model with TensorBoard log directory
#After training commenting this part of code
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')
acc = Accuracy(name="Accuracy")
network = regression(network,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.0005,
metric=acc)
model = tflearn.DNN(network,
checkpoint_path='jun_glnet_cat_dog.tflearn',
max_checkpoints=1,
tensorboard_verbose=3,
tensorboard_dir='tmp/tflearn_logs/')
model.fit(trainX,
trainY,
train_x, train_y, test_x, test_y = tflearn.datasets.mnist.load_data(
one_hot=True)
train_x = train_x.reshape(-1, 28, 28, 1)
test_x = test_x.reshape(-1, 28, 28, 1)
# In[3]:
# 定义神经网络模型
conv_net = input_data(shape=[None, 28, 28, 1], name='input')
conv_net = conv_2d(conv_net, 32, 2, activation='relu')
conv_net = max_pool_2d(conv_net, 2)
conv_net = conv_2d(conv_net, 64, 2, activation='relu')
conv_net = max_pool_2d(conv_net, 2)
conv_net = fully_connected(conv_net, 1024, activation='relu')
conv_net = dropout(conv_net, 0.8)
conv_net = fully_connected(conv_net, 10, activation='softmax')
conv_net = regression(conv_net,
optimizer='adam',
loss='categorical_crossentropy',
name='output')
# In[4]:
model = tflearn.DNN(conv_net)
# In[5]:
# 训练
model.fit({'input': train_x}, {'output': train_y},
n_epoch=13,
def build_model(optimizer=HYPERPARAMS.optimizer,
optimizer_param=HYPERPARAMS.optimizer_param,
learning_rate=HYPERPARAMS.learning_rate,
keep_prob=HYPERPARAMS.keep_prob,
learning_rate_decay=HYPERPARAMS.learning_rate_decay,
decay_step=HYPERPARAMS.decay_step):
images_network = input_data(
shape=[None, NETWORK.input_size, NETWORK.input_size, 1], name='input1')
images_network = conv_2d(images_network,
64,
5,
activation=NETWORK.activation)
#images_network = local_response_normalization(images_network)
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 3, strides=2)
images_network = conv_2d(images_network,
64,
5,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 3, strides=2)
images_network = conv_2d(images_network,
128,
4,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = dropout(images_network, keep_prob=keep_prob)
images_network = fully_connected(images_network,
1024,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_fully_connected_layers:
images_network = batch_normalization(images_network)
if NETWORK.use_landmarks or NETWORK.use_hog_and_landmarks:
if NETWORK.use_hog_sliding_window_and_landmarks:
landmarks_network = input_data(shape=[None, 2728], name='input2')
elif NETWORK.use_hog_and_landmarks:
landmarks_network = input_data(shape=[None, 208], name='input2')
else:
landmarks_network = input_data(shape=[None, 68, 2], name='input2')
landmarks_network = fully_connected(landmarks_network,
1024,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_fully_connected_layers:
landmarks_network = batch_normalization(landmarks_network)
landmarks_network = fully_connected(landmarks_network,
40,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_fully_connected_layers:
landmarks_network = batch_normalization(landmarks_network)
images_network = fully_connected(images_network,
40,
activation=NETWORK.activation)
network = merge([images_network, landmarks_network], 'concat', axis=1)
else:
network = images_network
network = fully_connected(network,
NETWORK.output_size,
activation='softmax')
if optimizer == 'momentum':
optimizer = Momentum(learning_rate=learning_rate,
momentum=optimizer_param,
lr_decay=learning_rate_decay,
decay_step=decay_step)
elif optimizer == 'adam':
optimizer = Adam(learning_rate=learning_rate,
beta1=optimizer_param,
beta2=learning_rate_decay)
else:
print "Unknown optimizer: {}".format(optimizer)
network = regression(network,
optimizer=optimizer,
loss=NETWORK.loss,
learning_rate=learning_rate,
name='output')
return network
drug_gru_2_gate_matrix.append(v)
elif "GRU_3/GRU_3/GRUCell/Candidate/Linear/Matrix" in v.name:
drug_gru_2_candidate_matrix.append(v)
elif "GRU_3/GRU_3/GRUCell/Gates/Linear/Bias" in v.name:
drug_gru_2_gate_bias.append(v)
elif "GRU_3/GRU_3/GRUCell/Candidate/Linear/Bias" in v.name:
drug_gru_2_candidate_bias.append(v)
elif "Embedding_1" in v.name:
drug_embd_W.append(v)
fc_1 = fully_connected(pool_2,
num1_neurons,
activation='leakyrelu',
weights_init="xavier",
name='fully1')
drop_2 = dropout(fc_1, drop_out)
fc_2 = fully_connected(drop_2,
num2_neurons,
activation='leakyrelu',
weights_init="xavier",
name='fully2')
drop_3 = dropout(fc_2, drop_out)
fc_3 = fully_connected(drop_3,
num3_neurons,
activation='leakyrelu',
weights_init="xavier",
name='fully3')
drop_4 = dropout(fc_3, drop_out)
linear = fully_connected(drop_4, 5, activation='softmax', name='fully4')
classification = regression(linear,
optimizer='adam',
def searchlist(request):
image= request.GET.get('img')
img_name= image.split('-')[0]
import glob
import shutil
import os
import cv2 # working with, mainly resizing, images
import numpy as np # dealing with arrays
from random import shuffle # mixing up or currently ordered data that might lead our network astray in training.
from tqdm import tqdm # a nice pretty percentage bar for tasks. Thanks to viewer Daniel BA1/4hler for this suggestion
if img_name == 'd':
verify_dir = 'testpicture/0'
elif img_name == 'm':
verify_dir = 'testpicture/1'
elif img_name == 'a':
verify_dir = 'testpicture/2'
else:
verify_dir = 'testpicture/4'
IMG_SIZE = 50
LR = 1e-3
MODEL_NAME = 'diabetic-{}-{}.model'.format(LR, '2conv-basic')
def process_verify_data():
verifying_data = []
for img in tqdm(os.listdir(verify_dir)):
path = os.path.join(verify_dir, img)
img_num = img.split('.')[0]
img = cv2.imread(path, cv2.IMREAD_COLOR)
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
verifying_data.append([np.array(img), img_num])
np.save('verify_data.npy', verifying_data)
return verifying_data
verify_data = process_verify_data()
# verify_data = np.load('verify_data.npy')
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import tensorflow as tf
tf.reset_default_graph()
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3], name='input')
convnet = conv_2d(convnet, 32, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 64, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 128, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 32, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 64, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 4, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')
if os.path.exists('{}.meta'.format(MODEL_NAME)):
model.load(MODEL_NAME)
print('model loaded!')
import matplotlib.pyplot as plt
fig = plt.figure()
for num, data in enumerate(verify_data):
img_num = data[1]
img_data = data[0]
y = fig.add_subplot(3, 4, num + 1)
orig = img_data
data = img_data.reshape(IMG_SIZE, IMG_SIZE, 3)
# model_out = model.predict([data])[0]
model_out = model.predict([data])[0]
if np.argmax(model_out) == 0:
str_label = 'No Diabetic Retinopathy !'
rating= '0'
elif np.argmax(model_out) == 1:
str_label = 'Mild Diabetic Retinopathy !'
rating = '1'
elif np.argmax(model_out) == 2:
str_label = 'Moderate Diabetic Retinopathy!'
rating = '2'
elif np.argmax(model_out) == 3:
str_label = 'Proliferative Diabetic Retinopathy !'
rating = '3'
image_url = '/images/' + image
context = {
"image" : image,
"url": image_url,
"str_label" : str_label,
"rating" : rating,
}
return render(request, 'sajilo/searchlist.html', context)
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)
convnet = fully_connected(convnet, 512, activation='relu')
convnet = dropout(convnet, 0.5)
convnet = fully_connected(convnet, 5, activation='softmax')
convnet = regression(convnet,
optimizer='adam',
learning_rate=LR,
loss='categorical_crossentropy',
name='targets')
model = tflearn.DNN(convnet)
model.load("/home/pallab/gestures-cnn/tfmodels/" + MODEL_NAME)
cap = cv2.VideoCapture(0)
#Layer3
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
#Layer4
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
#Layer5
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
#Layer6
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
#Output Layer
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet,
optimizer='adam',
learning_rate=LR,
loss='categorical_crossentropy',
name='targets')
#print(train_data)
model = tflearn.models.DNN(convnet, tensorboard_dir='log')
if os.path.exists('{}.meta'.format(MODEL_NAME)):
model.load(MODEL_NAME)
img_prep.add_featurewise_stdnorm()
# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)
# Convolutional network building
network = input_data(shape=[None, 32, 32, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 10, activation='softmax')
#network = regression(network, optimizer='adam',
#loss='categorical_crossentropy',
#learning_rate=0.001)
network = regression(network, optimizer='adadelta',
loss='categorical_crossentropy',
learning_rate=1)
# Train using classifier
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(X, Y, n_epoch=50, shuffle=True, validation_set=(X_test, Y_test),
show_metric=True, batch_size=96, run_id='cifar10_cnn')
trainingData = createTrainData() import tflearn from tflearn.layers.conv import conv_2d, max_pool_2d from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.estimator import regression NeuralNet = input_data(shape=[None, imageSize, imageSize, 1], name='input') NeuralNet = conv_2d(NeuralNet, 32, 2, activation='relu') NeuralNet = max_pool_2d(NeuralNet, 2) NeuralNet = conv_2d(NeuralNet, 64, 2, activation='relu') NeuralNet = max_pool_2d(NeuralNet, 2) NeuralNet = fully_connected(NeuralNet, 1024, activation='relu') NeuralNet = dropout(NeuralNet, 0.8) NeuralNet = fully_connected(NeuralNet, 2, activation='softmax') NeuralNet = regression(NeuralNet, optimizer='adam', learning_rate=LearningRate, loss='categorical_crossentropy', name='targets') model = tflearn.DNN(NeuralNet, tensorboard_dir='log') shuffle(trainingData) train = trainingData[:-100] test = trainingData[-100:] X = numpy.array([i[0] for i in train]).reshape(-1,imageSize, imageSize, 1) Y = [i[1] for i in train] test_x = numpy.array([i[0] for i in test]).reshape(-1,imageSize, imageSize, 1) test_y = [i[1] for i in test]
# Convolutional network building
# Hyper params:
_learning_rate = 0.001
_dropout = 0.5
network = input_data(shape=[None, 20, 20, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, _dropout)
network = fully_connected(network, 2, activation='softmax')
network = regression(network,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=_learning_rate)
model = tflearn.DNN(network, tensorboard_dir='log', tensorboard_verbose=0)
#load the trained model
model.load(model_path)
img = Image.open(images_path + "image1.jpg")
fig, ax = plt.subplots(1)
ax.imshow(img)
def videoFetch(self, thread_id):
path = self.path
img_width = 175
img_height = 150
testing_data = []
start_time = time.time()
cap = cv2.VideoCapture(path)
frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1
fragment_size = frame_count / 8
init_frame = math.floor(fragment_size * thread_id)
print(
"Thread {} starting Frame Extraction from {}th frame. Please wait for sometime."
.format(thread_id, init_frame))
end_frame = math.floor(fragment_size * (thread_id + 1) - 1)
count = init_frame
cap.set(1, init_frame)
print("Frame Extraction in Progress by Thread {}".format(thread_id))
while cap.isOpened():
ret, frame = cap.read()
if (ret):
img = cv2.resize(frame, (img_width, img_height))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_num = "%#05d" % (count + 1)
testing_data.append([np.array(img), img_num])
count = count + 1
if (count == end_frame):
end_time = time.time()
cap.release()
print(
"Thread {} finished extracting frames.\n{} frames found by Thread {}"
.format(thread_id, end_frame - init_frame, thread_id))
print("It took {} seconds for Frame Extraction by Thread {}".
format(end_time - start_time, thread_id))
break
# np.save('/home/ghost/Desktop/ecc/test_data_{}.npy'.format(thread_id), testing_data)
IMG_SIZE1 = 175
IMG_SIZE2 = 150
LR = 0.0001
MODEL_NAME = 'ECR-{}-{}.model'.format(LR, '2conv-basic')
tf.reset_default_graph()
convnet = input_data(shape=[None, IMG_SIZE1, IMG_SIZE2, 1],
name='input')
convnet = conv_2d(convnet, 32, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 32, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 32, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = dropout(convnet, 0.3)
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = dropout(convnet, 0.3)
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = dropout(convnet, 0.3)
convnet = conv_2d(convnet, 128, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 128, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 128, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = flatten(convnet)
convnet = fully_connected(convnet, 256, activation='relu')
convnet = dropout(convnet, 0.3)
convnet = fully_connected(convnet, 512, activation='relu')
convnet = dropout(convnet, 0.3)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet,
optimizer='adam',
learning_rate=LR,
loss='binary_crossentropy',
name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')
if os.path.exists('{}.meta'.format(MODEL_NAME)):
model.load(MODEL_NAME)
print('Explicit Content Censor Loaded by Thread {}'.format(
thread_id))
explicit = 0
non_explicit = 0
print('Video Censoring started by Thread {}'.format(thread_id))
for num, data in enumerate(testing_data[:]):
# explicit: [1,0]
# normal: [0,1]
img_data = data[0]
img_no = data[1]
data = img_data.reshape(IMG_SIZE1, IMG_SIZE2, 1)
model_out = model.predict([data])[0]
actual_frame_num = init_frame + num
if (np.argmax(model_out) == 0):
explicit_frames.append(actual_frame_num)
y_train = Y
X_test = testX
y_test = testY
X_train = X_train.reshape([-1, 28, 28, 1])
X_test = X_test.reshape([-1, 28, 28, 1])
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', name='dense1')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh', name='dense2')
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')
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit({'input': X_train}, {'target': y_train},
n_epoch=20,
validation_set=({
'input': X_test
def deepLearning():
import os as os
import cv2
import numpy as np
img_size = 50
lr = 1e-3
epoch = 100
step = 500
img_test_dir = 'D:\\code\\16462\\static\\preprocessed_images'
model_name = "D:\\code\\16462\\deeplearning\\Processed_PA-0.001-8tryconv-basic-project-50.model"
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import tensorflow as tf
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 = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.5)
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet,
optimizer='adam',
learning_rate=lr,
loss='categorical_crossentropy',
name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')
model.load(model_name)
def label_img(img):
#word_label=img.split('.')[-3]
if img == 'Cardiomegaly': return [1, 0]
elif img == 'No Cardiomegaly': return [0, 1]
def create_img_test_data():
img_testing_data = []
#count=0
for img in os.listdir(img_test_dir):
path = os.path.join(img_test_dir, img)
img = cv2.resize(cv2.imread(path, cv2.IMREAD_GRAYSCALE),
(img_size, img_size))
img_testing_data.append([np.array(img)])
np.save('new_img_test_data_trainDown.npy', img_testing_data)
return img_testing_data
create_img_test_data()
test_img = np.load('new_img_test_data_trainDown.npy')
# import matplotlib.pyplot as plt
# plt.rcParams['figure.figsize'] = (15,15)
result = []
for num, data in enumerate(test_img[:]):
count = 0
#img_num = data[1]
img_data = data[0]
orig = img_data
data = img_data.reshape(img_size, img_size, 1)
model_out = model.predict([data])[0]
if np.argmax(model_out) == 1: str_label = 'No Cardiomegaly'
else: str_label = 'Cardiomegaly'
result.append(str_label)
return result
# print(result)
# deepLearning()
def network1(train, train_amount):
global model
# region SPLIT DATA FOR TRAIN/VALIDATION
train_amount = int(train_amount * 5.5 / 6)
x_train = np.array([i[0] for i in train[:train_amount]
]).reshape(-1, s.IMG_SIZE, s.IMG_SIZE, 1)
x_train = x_train / 255.0
y_train = [i[1] for i in train[:train_amount]]
x_validation = np.array([i[0] for i in train[train_amount:]
]).reshape(-1, s.IMG_SIZE, s.IMG_SIZE, 1)
x_validation = x_validation / 255.0
y_validation = [i[1] for i in train[train_amount:]]
# endregion
# region NETWORK
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center(mean=[0.4735053442384178])
network = input_data(shape=[None, s.IMG_SIZE, s.IMG_SIZE, 1],
name='input',
data_preprocessing=img_prep)
network = conv_2d(network, 32, 3, activation='relu', scope='conv1_1')
network = conv_2d(network, 64, 3, activation='relu', scope='conv1_2')
network = max_pool_2d(network, 2, strides=2, name='maxpool_1')
network = conv_2d(network, 128, 3, activation='relu', scope='conv2_1')
network = max_pool_2d(network, 2, strides=2, name='maxpool_2')
network = conv_2d(network, 128, 3, activation='relu', scope='conv3_1')
network = max_pool_2d(network, 2, strides=2, name='maxpool_3')
network = conv_2d(network, 256, 3, activation='relu', scope='conv4_1')
network = max_pool_2d(network, 2, strides=2, name='maxpool_4')
network = fully_connected(network, 1024, activation='relu', scope='fc5')
network = dropout(network, 0.5, name='dropout_1')
network = fully_connected(network, 1024, activation='relu', scope='fc6')
network = dropout(network, 0.5, name='dropout_2')
network = fully_connected(network,
s.len_animals,
activation='softmax',
scope='fc7')
network = regression(network,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=s.LR,
name='targets')
model = tflearn.DNN(network, tensorboard_verbose=0, tensorboard_dir='log')
# endregion
if os.path.exists('{}.meta'.format(s.MODEL_NAME)):
model.load(s.MODEL_NAME)
print('Model loaded')
# region TRAIN1
model.fit(x_train,
y_train,
n_epoch=12,
validation_set=({
'input': x_validation
}, {
'targets': y_validation
}),
shuffle=True,
snapshot_epoch=True,
show_metric=True,
batch_size=100,
run_id=s.MODEL_NAME)
# endregion
# region SAVE
model.save(s.MODEL_NAME)
print('Network trained and saved as {0}'.format(s.MODEL_NAME))
train, test, train_labels, test_labels = train_test_split(X, Y, test_size=0.09)
train = train.reshape([-1, 28, 28, 1])
test = test.reshape([-1, 28, 28, 1])
cnn = input_data(shape=[None, 28, 28, 1], name='input')
cnn = conv_2d(cnn, 32, 2, activation='relu')
cnn = max_pool_2d(cnn, 2)
cnn = conv_2d(cnn, 64, 2, activation='relu')
cnn = max_pool_2d(cnn, 2)
cnn = fully_connected(cnn, 28, activation='relu')
cnn = dropout(cnn, 0.8)
cnn = fully_connected(cnn, 3, activation='softmax')
cnn = regression(cnn,
optimizer='adam',
learning_rate=0.01,
loss='categorical_crossentropy',
name='targets')
model = tflearn.DNN(cnn)
model.fit({'input': train}, {'targets': train_labels},
n_epoch=10,
validation_set=({
'input': test
}, {
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.embedding_ops import embedding
from tflearn.layers.recurrent import bidirectional_rnn, BasicLSTMCell
from tflearn.layers.estimator import regression
# IMDB Dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl',
n_words=10000,
valid_portion=0.1)
trainX, trainY = train
testX, testY = test
# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=200, value=0.)
testX = pad_sequences(testX, maxlen=200, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY)
testY = to_categorical(testY)
# Network building
net = input_data(shape=[None, 200])
net = embedding(net, input_dim=20000, output_dim=128)
net = bidirectional_rnn(net, BasicLSTMCell(128), BasicLSTMCell(128))
net = dropout(net, 0.5)
net = fully_connected(net, 2, activation='softmax')
net = regression(net, optimizer='adam', loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net, clip_gradients=0., tensorboard_verbose=2)
model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=64)
def create_train_data():
training_data = []
for img in tqdm(os.listdir(TRAIN_DIR)):
label = label_img(img)
path = os.path.join(TRAIN_DIR,img)
img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
training_data.append([np.array(img),np.array(label)])
shuffle(training_data)
np.save('train_data.npy', training_data)
return training_data
def process_test_data():
testing_data = []
for img in tqdm(os.listdir(TEST_DIR)):
path = os.path.join(TEST_DIR,img)
img_num = img.split('.')[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
train_data = create_train_data()
# If you have already created the dataset:
#train_data = np.load('train_data.npy')
import tflearn
import tensorflow as tf
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
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, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')
if os.path.exists('C:/Users/H/Desktop/KaggleDogsvsCats/{}.meta'.format(MODEL_NAME)):
model.load(MODEL_NAME)
print('model loaded!')
train = train_data[:-500]
test = train_data[-500:]
X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
Y = [i[1] for i in train]
test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
test_y = [i[1] for i in test]
model.fit({'input': X}, {'targets': Y}, n_epoch=10, validation_set=({'input': test_x}, {'targets': test_y}),
snapshot_step=500, show_metric=True, run_id=MODEL_NAME)
model.save(MODEL_NAME)
import matplotlib.pyplot as plt
# if you need to create the data:
#test_data = process_test_data()
# if you already have some saved:
test_data = np.load('test_data.npy')
fig=plt.figure()
for num,data in enumerate(test_data[:12]):
# cat: [1,0]
# dog: [0,1]
img_num = data[1]
img_data = data[0]
y = fig.add_subplot(3,4,num+1)
orig = img_data
data = img_data.reshape(IMG_SIZE,IMG_SIZE,1)
#model_out = model.predict([data])[0]
model_out = model.predict([data])[0]
if np.argmax(model_out) == 1: str_label='Dog'
else: str_label='Cat'
y.imshow(orig,cmap='gray')
plt.title(str_label)
y.axes.get_xaxis().set_visible(False)
y.axes.get_yaxis().set_visible(False)
plt.show()
with open('submission_file.csv','w') as f:
f.write('id,label\n')
with open('submission_file.csv','a') as f:
for data in tqdm(test_data):
img_num = data[1]
img_data = data[0]
orig = img_data
data = img_data.reshape(IMG_SIZE,IMG_SIZE,1)
model_out = model.predict([data])[0]
f.write('{},{}\n'.format(img_num,model_out[1]))
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 256, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 256, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 128, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = fully_connected(convnet, 1000, activation='relu')
convnet = dropout(convnet, 0.75)
convnet = fully_connected(convnet, 6, activation='softmax')
convnet = regression(convnet,
optimizer='adam',
learning_rate=0.001,
loss='categorical_crossentropy',
name='regression')
model = tflearn.DNN(convnet, tensorboard_verbose=0)
# Load Saved Model
model.load("TrainedModel/GestureRecogModel.tfl")
def get_network_architecture(image_width, image_height, number_of_classes, learning_rate):
number_of_channels = 1
network = input_data(
shape=[None, image_width, image_height, number_of_channels],
data_preprocessing=img_prep,
data_augmentation=img_aug,
name='InputData'
)
"""
def conv_2d(incoming, nb_filters, filter_size, strides=1, padding='same',
activation='linear', bias='True', weights_init='uniform_scaling',
bias_init='zeros', regularizer=None, weight_decay=0.001,
trainable=True, restore=True, reuse=False, scope=None,
name='Conv2D')
network = conv_2d(network, 32, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_1')
network = max_pool_2d(network, (2, 2), strides=2, padding='same', name='MaxPool2D_1')
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_1')
network = dropout(network, 0.5, name='Dropout_1')
network = batch_normalization(network, name='BatchNormalization')
network = flatten(network, name='Flatten')
network = fully_connected(network, 512, activation='relu', name='FullyConnected_1')
network = fully_connected(network, number_of_classes, activation='softmax', name='FullyConnected_Final')
print(' {}: {}'.format('Conv2D................', network.shape))
print(' {}: {}'.format('MaxPool2D.............', network.shape))
print(' {}: {}'.format('Dropout...............', network.shape))
print(' {}: {}'.format('BatchNormalization....', network.shape))
print(' {}: {}'.format('Flatten...............', network.shape))
print(' {}: {}'.format('FullyConnected........', network.shape))
print(' {}: {}'.format('FullyConnected_Final..', network.shape))
CONV / FC -> Dropout -> BN -> activation function -> ...
Convolutional filters: { 32, 64, 128 }
Convolutional filter sizes: { 1, 3, 5, 11 }
Convolutional strides: 1
Activation: ReLu
Pooling kernel sizes: { 2, 3, 4, 5 }
Pooling kernel strides: 2
Dropout probability: 0.5
- Higher probability of keeping in earlier stages
- Lower probability of keeping in later stages
"""
print('\nNetwork architecture:')
print(' {}: {}'.format('InputData.............', network.shape))
network = conv_2d(network, 16, (7, 7), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_1')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_1')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 16, (7, 7), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_2')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_2')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_1')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_1')
print(' {}: {}'.format('Dropout...............', network.shape))
network = conv_2d(network, 32, (5, 5), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_3')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_3')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 32, (5, 5), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_4')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_4')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_2')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_2')
print(' {}: {}'.format('Dropout...............', network.shape))
network = conv_2d(network, 64, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_5')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_5')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 64, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_6')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_6')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_3')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_3')
print(' {}: {}'.format('Dropout...............', network.shape))
network = conv_2d(network, 128, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_7')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_7')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 128, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_8')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_8')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_4')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_4')
print(' {}: {}'.format('Dropout...............', network.shape))
network = conv_2d(network, 256, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_9')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_9')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 256, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_10')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_10')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_5')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_5')
print(' {}: {}'.format('Dropout...............', network.shape))
network = flatten(network, name='Flatten')
print(' {}: {}'.format('Flatten...............', network.shape))
network = fully_connected(network, 512, activation='relu', name='FullyConnected_1')
print(' {}: {}'.format('FullyConnected........', network.shape))
network = dropout(network, 0.5, name='Dropout_6')
print(' {}: {}'.format('Dropout...............', network.shape))
network = fully_connected(network, number_of_classes, activation='softmax', name="FullyConnected_Final")
print(' {}: {}'.format('FullyConnected_Final..', network.shape))
optimizer = Adam(learning_rate=learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')
# optimizer = SGD(learning_rate=learning_rate, lr_decay=0.01, decay_step=100, staircase=False, use_locking=False, name='SGD')
# optimizer = RMSProp(learning_rate=learning_rate, decay=0.9, momentum=0.9, epsilon=1e-10, use_locking=False, name='RMSProp')
# optimizer = Momentum(learning_rate=learning_rate, momentum=0.9, lr_decay=0.01, decay_step=100, staircase=False, use_locking=False, name='Momentum')
metric = Accuracy(name='Accuracy')
# metric = R2(name='Standard Error')
# metric = WeightedR2(name='Weighted Standard Error')
# metric = Top_k(k=6, name='Top K')
network = regression(
network,
optimizer=optimizer,
loss='categorical_crossentropy',
metric=metric,
learning_rate=learning_rate,
name='Regression'
)
return network
name='conv1_3_3_4',
weights_init='Xavier')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network,
512,
3,
strides=1,
activation='relu',
regularizer='L2',
name='conv1_3_3_5',
weights_init='Xavier')
network = max_pool_2d(network, 2, strides=2)
# Fully Connected Layer
network = fully_connected(network, 512, activation='relu')
# Dropout layer
network = dropout(network, 1)
# Fully Connected Layer
#network = fully_connected(network, 512, activation='relu')
# Dropout layer
#network = dropout(network, 1)
# Fully Connected Layer
network = fully_connected(network, 6, activation='softmax')
# Final network
network = regression(network,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.0007,
metric=acc)
# The model with details on where to save
# Will save in current directory
def main():
f = open("results.txt", "w+")
get_data = create_training_data()
train_data = get_data[0]
data_indeces = get_data[1]
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3], name='input')
convnet = conv_2d(convnet, 32, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 64, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 128, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 32, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 64, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 19, activation='softmax')
convnet = regression(convnet,
optimizer='adam',
learning_rate=LR,
loss='categorical_crossentropy',
name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')
k_fold = 5
fold_number = 0
MODEL_NEW_NAME = 'jycropdisease-fold-new{}-{}-{}.model'.format(
fold_number + 1, LR, '2conv-basic')
data = train_data
print("======= FOLD %d =======" % (fold_number + 1))
split_data = split_training_data(data, data_indeces, k_fold, fold_number)
train = split_data['train_data']
test = split_data['test_data']
X = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 3)
Y = [i[1] for i in train]
test_x = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 3)
test_y = [i[1] for i in test]
model.fit({'input': X}, {'targets': Y},
n_epoch=8,
validation_set=({
'input': test_x
}, {
'targets': test_y
}),
snapshot_step=1000,
show_metric=True,
run_id=MODEL_NEW_NAME)
model.save(MODEL_NEW_NAME)
f.close()
def build_network(self):
# Smaller 'AlexNet'
# https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py
print('[+] Building CNN')
'''
# Why 3 hidden layers?
# 1986: Backpropagation - Usually more than 3 hidden layer is not helpful
'''
'''
[-]input layer
#This layer is use for inputting data to a network.
#List of int, to create a new placeholder
# shape = [batch, height, width, in_channels]
'''
self.network = input_data(shape=[None, SIZE_FACE, SIZE_FACE, 1]) # add data whose shape is [None,48, 48 ,1] into an 'input_data' layer
'''
[-]conv_2d
#arg1 - incoming: [batch, height, width, in_channels]
#arg2 - nb_filter: The number oft convolution filters
#arg3 - filter_size( kernel size ) : Size of filters
#strides - default : 1
'''
self.network = conv_2d(self.network, 64, 5, activation='relu') # 1st layer
#self.network = local_response_normalization(self.network) #
'''
[-]max pooling 2D
# arg1 - incoming:
# arg2 - kernel_size: Pooling kernel size
# arg3 - strides : stides of conv operation e.g,(0,0)->(0,2)->(0,4)
'''
self.network = max_pool_2d(self.network, 3, strides=2) # pool
self.network = conv_2d(self.network, 64, 5, activation='relu') # 2nd layer
self.network = max_pool_2d(self.network, 3, strides=2) # pool
self.network = conv_2d(self.network, 128, 4, activation='relu') # 3rd layer
'''
[-]Dropout
reference: tflearn.org/layers/core/#dropout
Introduction:
#Outputs the input element scaled up by 1/keep_prob. The scaling is so that the expected sum is unchanged
#By default, each element is kept or dropped independently. If noise_shape is specified, it must be broadcastable to the shape of x, and only dimensions with noise_shape[i] == shape(x)[i] will make
independent decisions. For example, if shape(x) = [k, l, m, n] and noise_shape = [k, 1, 1, n], each batch and channel component will be kept independently and each row and column will be kept or not kept together
#arg1 - incoming: []
#arg2 - keep_prob: A float representing the probability that each element is kept
'''
self.network = dropout(self.network, 0.3) # final: output layer
'''
[-]fully_connected
return : 2D Tensor[samples, n_units]
arg1 - incoming: 2+D Tensor []
arg2 - n_units: the # of units for this layer
'''
self.network = fully_connected(self.network, 3072, activation='relu') # A fully connected layer
self.network = fully_connected(
self.network, len(EMOTIONS), activation='softmax') # A fully connected layer
'''
[-]regression
To apply a regreesion to the provided input.
# optimizer: Optimizer to use
# loss: Loss function used by this layer optimizer
'''
self.network = regression(
self.network,
optimizer='momentum',
loss='categorical_crossentropy'
)# conput loss and optimizer
'''
Deep Neural Network Model
# network: NN to be used
# checkpoint_path : the path to store model file
# max_checkpoint: Maximum amount of checkpoints
# tensorboard_verbose: Summary verbose level, it accepts different levels of tensorboard logs.
'''
self.model = tflearn.DNN(
self.network,
checkpoint_path=SAVE_DIRECTORY + '/emotion_recognition',
max_checkpoints=1,
tensorboard_verbose=2
) #model max_checkpoints = 1: save only one model file.
self.load_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
tf.reset_default_graph()
convent = input_data(shape = [ None , IMG_SIZE , IMG_SIZE , 1] , name = 'input')
convent = conv_2d(convent , 32 , 5 ,activation='relu')
convent = max_pool_2d(convent, 5)
convent = conv_2d(convent , 64 , 5 , activation = 'relu')
convent = max_pool_2d(convent, 5)
convent = conv_2d(convent , 128 , 5 ,activation = 'relu')
convent = max_pool_2d(convent , 5)
convent = conv_2d(convent , 64 , 5 ,activation = 'relu')
convent = max_pool_2d(convent, 5)
convent = conv_2d(convent , 32 , 5 ,activation='relu')
convent = max_pool_2d(convent, 5)
convent = fully_connected(convent, 1024 , activation = 'relu')
convent = dropout(convent , 0.8)
convent = fully_connected(convent , 2 , activation = 'softmax')
convent = regression(convent , optimizer = 'adam' , learning_rate = LR , loss = 'categorical_crossentropy' , name = 'targets' )
model = tflearn.DNN(convent , tensorboard_dir='log' , tensorboard_verbose = 0)
model.fit( { 'input' : X_train }, { 'targets' : y_train} , n_epoch = 10 ,
validation_set = ( { 'input' : X_test} , {'targets' : y_test}),
snapshot_step = 500 , show_metric = True , run_id = MODEL_NAME )
fig = plt.figure(figsize = (16,12))
for num , data in enumerate(test_data[:16]) :
img_num = data[1]
img_data = data[0]
