def neural_network_model4(IMG_SIZE, LR):
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
convnet = conv_2d(convnet, 64, (5, 5), activation='relu')
convnet = max_pool_2d(convnet, (5, 5), strides=(2, 2))
convnet = conv_2d(convnet, 64, (3, 3), activation='relu')
convnet = conv_2d(convnet, 64, (3, 3), activation='relu')
convnet = avg_pool_2d(convnet, (3, 3), strides=(2, 2))
convnet = dropout(convnet, 0.7)
convnet = conv_2d(convnet, 128, (3, 3), activation='relu')
convnet = conv_2d(convnet, 128, (3, 3), activation='relu')
convnet = avg_pool_2d(convnet, (3, 3), strides=(2, 2))
convnet = dropout(convnet, 0.7)
convnet = flatten(convnet)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.7)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.7)
convnet = fully_connected(convnet, 7, activation='softmax')
convnet = regression(convnet,
optimizer='adam',
learning_rate=LR,
loss='categorical_crossentropy',
name='targets')
convnet = dropout(convnet, 0.7)
model = tflearn.DNN(convnet, tensorboard_dir='log')
return model
def generate_model():
convnet = input_data(shape=[None, 200, 200, 3], name='input')
convnet = conv_2d(convnet, 32, 2, strides=[1, 2, 2, 1], activation='relu')
convnet = max_pool_2d(convnet, [1, 2, 2, 1])
convnet = conv_2d(convnet, 64, 2, strides=[1, 2, 2, 1], activation='relu')
convnet = avg_pool_2d(convnet, [1, 2, 2, 1])
convnet = conv_2d(convnet, 128, 2, strides=[1, 2, 2, 1], activation='relu')
convnet = max_pool_2d(convnet, [1, 2, 2, 1])
convnet = conv_2d(convnet, 256, 2, strides=[1, 2, 2, 1], activation='relu')
convnet = max_pool_2d(convnet, [1, 2, 2, 1])
convnet = conv_2d(convnet, 128, 2, strides=[1, 2, 2, 1], activation='relu')
convnet = max_pool_2d(convnet, [1, 2, 2, 1])
convnet = conv_2d(convnet, 64, 2, strides=[1, 2, 2, 1], activation='relu')
convnet = avg_pool_2d(convnet, [1, 2, 2, 1])
convnet = conv_2d(convnet, 32, 2, strides=[1, 2, 2, 1], activation='relu')
convnet = max_pool_2d(convnet, [1, 2, 2, 1])
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet,
learning_rate=0.001,
loss='categorical_crossentropy',
optimizer='adam')
model = tflearn.DNN(convnet,
tensorboard_verbose=3,
tensorboard_dir=vis_dir)
return model
def CNNModel(x, reuse=False):
conv_1 = conv_2d(x, 8, [32, 1], activation='relu', regularizer="L2", scope='conv_1', reuse=reuse)
avg_pool_1 = avg_pool_2d(conv_1, [1, 2])
output_layer_1 = local_response_normalization(avg_pool_1)
conv_2 = conv_2d(output_layer_1, 16, [1, 1], activation='relu', regularizer="L2", scope='conv_2', reuse=reuse)
avg_pool_2 = avg_pool_2d(conv_2, [1, 2])
output_layer_2 = local_response_normalization(avg_pool_2)
output_conv = flatten(output_layer_2)
return output_conv
def light_convolution(network, input, hidden_number, default_hidden):
def central_crop(input, shape):
x = input.get_shape()[1] // 2 - shape[0] // 2
y = input.get_shape()[2] // 2 - shape[1] // 2
return input[0:, x:x + shape[0], y:y + shape[1], 0:]
with tf.variable_scope('light_convolution'):
input = tf.reshape(input, [-1, 32, 32, 3])
input = avg_pool_2d(input, 2)
input = tf.reduce_mean(input, axis=3, keep_dims=True)
input = conv_2d(input, 32, 3, activation='relu', regularizer='L2')
input = max_pool_2d(input, 2)
input = conv_2d(input, 64, 3, activation='relu', regularizer='L2')
input = max_pool_2d(input, 2)
input = tf.reshape(input, [
-1,
input.get_shape()[1] * input.get_shape()[2] *
input.get_shape()[3]
])
input, _ = default_hidden(network, input, hidden_number)
variable_scope = tf.get_variable_scope().name
if variable_scope:
variable_scope += '/'
return input, tf.global_variables(variable_scope)
def All_extract(self, input_x, layer_name):
with tf.name_scope(layer_name):
output_height = int(np.shape(input_x)[1])
output_weight = int(np.shape(input_x)[2])
local_x_list = []
logits_list = []
# all fig
local_x1 = avg_pool_2d(input_x[:, :, :, :],
(output_height, output_weight),
name=layer_name + '_AvgPool2D')
# local_x1 = global_avg_pool(input_x, name='Global_avg_pooling')
local_x1 = tf.layers.dropout(inputs=local_x1,
rate=self.drop_rate,
training=self.training,
name=layer_name + '_drop1')
local_x1 = conv_layer(local_x1,
filter=256,
kernel=[1, 1],
layer_name=layer_name + '_split_conv')
local_x1 = Batch_Normalization(local_x1,
training=self.training,
scope=layer_name + '_batch')
local_x1 = flatten(local_x1)
local_x_list.append(local_x1)
local_x1 = tf.layers.dropout(inputs=local_x1,
rate=self.drop_rate,
training=self.training,
name=layer_name + '_drop2')
local_x1 = Fully_connected(local_x1,
self.class_num,
layer_name=layer_name +
'_fully_connected')
logits_list.append(local_x1)
return local_x_list, logits_list
def NiN(input_layer, num_class):
network = conv_2d(input_layer, 192, 5, activation='relu')
network = conv_2d(network, 160, 1, activation='relu')
network = conv_2d(network, 96, 1, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = avg_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 3, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 10, 1, activation='relu')
network = avg_pool_2d(network, 8)
network = flatten(network)
return network
def model_lenet5(data):
layer1_conv = conv_2d(data,
nb_filter=6,
filter_size=5,
strides=[1, 1, 1, 1],
activation='relu',
padding='SAME',
bias=True)
layer1_pool = avg_pool_2d(layer1_conv,
kernel_size=2,
strides=2,
padding='SAME')
layer2_conv = conv_2d(layer1_pool,
nb_filter=16,
filter_size=5,
strides=[1, 1, 1, 1],
activation='relu',
padding='VALID',
bias=True)
layer2_pool = avg_pool_2d(layer2_conv,
kernel_size=2,
strides=2,
padding='SAME')
flat_layer = flatten(layer2_pool)
layer3_fccd = fully_connected(flat_layer,
n_units=120,
activation='relu',
bias=True)
layer4_fccd = fully_connected(layer3_fccd,
n_units=84,
activation='relu',
bias=True)
#w = tf.Variable(tf.truncated_normal([84, 10], stddev=0.1))
#b = tf.Variable(tf.constant(1.0, shape = [10]))
#logits = tf.matmul(layer4_fccd, w) + b
logits = fully_connected(layer3_fccd,
n_units=10,
activation='relu',
bias=True)
return logits
def Net_in_Net1(network):
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 160, 1, activation='relu')
network = conv_2d(network, 96, 1, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = dropout(network, 1.0)
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = avg_pool_2d(network, 3, strides=2)
network = dropout(network, 1.0)
network = conv_2d(network, 192, 3, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 10, 1, activation='relu')
network = avg_pool_2d(network, 8)
network = flatten(network)
network = fully_connected(network, output_dim, activation='softmax')
return network
def inception_block_a(input_a):
inception_a_conv1_1_1 = conv_2d(input_a,
96,
1,
activation='relu',
name='inception_a_conv1_1_1')
inception_a_conv1_3_3_reduce = conv_2d(input_a,
64,
1,
activation='relu',
name='inception_a_conv1_3_3_reduce')
inception_a_conv1_3_3 = conv_2d(inception_a_conv1_3_3_reduce,
96,
3,
activation='relu',
name='inception_a_conv1_3_3')
inception_a_conv2_3_3_reduce = conv_2d(input_a,
64,
1,
activation='relu',
name='inception_a_conv2_3_3_reduce')
inception_a_conv2_3_3_sym_1 = conv_2d(inception_a_conv2_3_3_reduce,
96,
3,
activation='relu',
name='inception_a_conv2_3_3')
inception_a_conv2_3_3 = conv_2d(inception_a_conv2_3_3_sym_1,
96,
3,
activation='relu',
name='inception_a_conv2_3_3')
inception_a_pool = avg_pool_2d(input_a,
kernel_size=3,
name='inception_a_pool',
strides=1)
inception_a_pool_1_1 = conv_2d(inception_a_pool,
96,
1,
activation='relu',
name='inception_a_pool_1_1')
# merge inception_a
inception_a = merge([
inception_a_conv1_1_1, inception_a_conv1_3_3, inception_a_conv2_3_3,
inception_a_pool_1_1
],
mode='concat',
axis=3)
return inception_a
def model(img):
MODEL_NAME = 'LeNet_5_COLOR'
IMG_SIZE = 100
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3], name='input')
convnet = conv_2d(convnet,
nb_filter=6,
filter_size=5,
strides=1,
activation='relu')
convnet = avg_pool_2d(convnet, kernel_size=2, strides=2)
convnet = conv_2d(convnet,
nb_filter=16,
filter_size=5,
strides=1,
activation='relu')
convnet = avg_pool_2d(convnet, kernel_size=2, strides=2)
convnet = fully_connected(convnet, 120, activation='relu')
convnet = fully_connected(convnet, 84, activation='relu')
convnet = fully_connected(convnet, 10, activation='softmax')
convnet = regression(convnet,
optimizer='adam',
learning_rate=0.001,
loss='categorical_crossentropy',
name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log', tensorboard_verbose=3)
#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)
if os.path.exists(
'/home/manish/Manish/Hackathon/google-images-download-master/{}.meta'
.format(MODEL_NAME)):
model.load(MODEL_NAME)
print('Model Loaded')
model_out = model.predict([img])[0]
print(model_out)
label = np.argmax(model_out)
print(items[label])
print(label)
return items[label]
def build_network_in_network(self, network):
# network = input_data(shape=[None, 32, 32, 3])
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 160, 1, activation='relu')
network = conv_2d(network, 96, 1, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = avg_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 3, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 2, 1, activation='relu')
network = avg_pool_2d(network, 8)
network = flatten(network)
network = regression(network, optimizer='adam',
loss='softmax_categorical_crossentropy',
learning_rate=0.001)
return network
def net(input_image):
ratios = [32, 16, 8, 4, 2, 1]
conv_num = 8
network = []
for i in range(len(ratios)):
network.append(avg_pool_2d(input_image, ratios[i]))
# block_i_0, block_i_1, block_i_2
for block in range(3):
with tf.name_scope('block_%d_%d' % (i, block)):
filter_size = 1 if (block + 1) % 3 == 0 else 3
network[i] = tflearn.conv_2d(network[i],
nb_filter=conv_num,
filter_size=filter_size,
weights_init='xavier',
name='Conv_%d_%d' % (i, block))
network[i] = tf.nn.batch_normalization(network[i], 0, 1.0, 0.0,
1.0, 1e-5, 'BatchNorm')
network[i] = tflearn.activations.leaky_relu(network[i])
if i == 0:
network[i] = tflearn.upsample_2d(network[i], 2)
else:
upnet = tf.nn.batch_normalization(network[i - 1], 0, 1.0, 0.0, 1.0,
1e-5, 'BatchNorm')
downnet = tf.nn.batch_normalization(network[i], 0, 1.0, 0.0, 1.0,
1e-5, 'BatchNorm')
# join_i
network[i] = tflearn.merge([upnet, downnet], 'concat', axis=3)
# block_i_3, block_i_4, block_i_5
for block in range(3, 6):
with tf.name_scope('block_%d_%d' % (i, block)):
filter_size = 1 if (block + 1) % 3 == 0 else 3
network[i] = tflearn.conv_2d(network[i],
nb_filter=conv_num * i,
filter_size=filter_size,
weights_init='xavier',
name='Conv_%d_%d' %
(i, block))
network[i] = tf.nn.batch_normalization(
network[i], 0, 1.0, 0.0, 1.0, 1e-5, 'BatchNorm')
network[i] = tflearn.activations.leaky_relu(network[i])
if i != len(ratios) - 1:
network[i] = tflearn.upsample_2d(network[i], 2)
network[len(ratios) - 1] = tflearn.conv_2d(network[len(ratios) - 1],
nb_filter=3,
filter_size=1,
weights_init='xavier',
name='Conv2d_out')
return network[len(ratios) - 1]
def Net_in_Net1(network, scale=False):
if scale is True:
network = scale(network)
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 160, 1, activation='relu')
network = conv_2d(network, 96, 1, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = avg_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 3, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 10, 1, activation='relu')
network = avg_pool_2d(network, 8)
network = flatten(network)
network = fully_connected(network, output_dim, activation='sigmoid')
return network
def Half_extract(input_x, layer_name, class_num, training, drop_rate=0.2):
with tf.name_scope(layer_name):
output_height = int(np.shape(input_x)[1])
output_weight = int(np.shape(input_x)[2])
stripe_w2 = output_weight
stripe_h2 = int(output_height / 2)
local_x_list = []
logits_list = []
input_x = Batch_Normalization(input_x,
training=training,
scope=layer_name + '_batch_norm')
input_x = tf.nn.relu(input_x, name=layer_name + 'relu')
for i in range(2): # up and down
local_x2 = avg_pool_2d(
input_x[:, i * stripe_h2:(i + 1) * stripe_h2, :, :],
(stripe_h2, stripe_w2))
local_x2 = tf.layers.dropout(inputs=local_x2,
rate=drop_rate,
training=training,
name=layer_name + '_drop1')
local_x2 = conv_layer(local_x2,
filter=256,
kernel=[1, 1],
layer_name=layer_name + '_split_conv' +
str(i))
local_x2 = Batch_Normalization(local_x2,
training=training,
scope=layer_name + '_batch' +
str(i))
local_x2 = flatten(local_x2)
local_x_list.append(local_x2)
local_x2 = tf.layers.dropout(inputs=local_x2,
rate=drop_rate,
training=training,
name=layer_name + '_drop2')
local_x2 = Fully_connected(local_x2,
class_num,
use_bias=False,
layer_name=layer_name +
'_fully_connected_' + str(i))
logits_list.append(local_x2)
return local_x_list, logits_list
def Part_extract(self, input_x, layer_name):
with tf.name_scope(layer_name):
output_height = int(np.shape(input_x)[1])
output_weight = int(np.shape(input_x)[2])
stripe_w = output_weight
stripe_h = int(output_height / 6)
local_x_list = []
logits_list = []
for i in range(6): # 6 parts
local_x = avg_pool_2d(
input_x[:, i * stripe_h:(i + 1) * stripe_h, :, :],
(stripe_h, stripe_w))
# print('shape of local_x: ', np.shape(local_x))
local_x = tf.layers.dropout(inputs=local_x,
rate=self.drop_rate,
training=self.training,
name=layer_name + '_drop1')
local_x = conv_layer(local_x,
filter=256,
kernel=[1, 1],
layer_name=layer_name + '_split_conv' +
str(i))
# print('shape of local_x after conv: ', np.shape(local_x))
local_x = Batch_Normalization(local_x,
training=self.training,
scope=layer_name + '_batch' +
str(i))
local_x = flatten(local_x)
# print('shape of local_x1: ', np.shape(local_x))
local_x_list.append(local_x)
local_x = tf.layers.dropout(inputs=local_x,
rate=self.drop_rate,
training=self.training,
name=layer_name + '_drop2')
local_x = Fully_connected(local_x,
self.class_num,
layer_name=layer_name +
'_fully_connected_' + str(i))
# print('shape of local_x after FC: ', np.shape(local_x))
logits_list.append(local_x)
# print('shape of logits_list: ', np.shape(logits_list))
return local_x_list, logits_list
def Part_extract(input_x,
layer_name,
class_num,
training,
num_dims=256,
drop_rate=0.2):
with tf.name_scope(layer_name):
output_height = int(np.shape(input_x)[1])
output_weight = int(np.shape(input_x)[2])
stripe_w = output_weight
stripe_h = int(output_height / 6)
local_x_list = []
logits_list = []
for i in range(6): # 6 parts
local_x = avg_pool_2d(
input_x[:, i * stripe_h:(i + 1) * stripe_h, :, :],
(stripe_h, stripe_w))
local_x = tf.layers.dropout(local_x,
rate=drop_rate,
training=training,
name=layer_name + '_drop1')
local_x = conv_layer(local_x,
filter=num_dims,
kernel=[1, 1],
layer_name=layer_name + '_split_conv' +
str(i))
local_x = Batch_Normalization(local_x,
training=training,
scope=layer_name + '_batch' + str(i))
local_x = flatten(local_x)
local_x_list.append(local_x)
# local_x = tf.layers.dropout(local_x, rate=drop_rate, training=training,
# name=layer_name + '_drop2')
local_x = Fully_connected(local_x,
class_num,
use_bias=False,
layer_name=layer_name +
'_fully_connected_' + str(i))
logits_list.append(local_x)
return local_x_list, logits_list
def inception_block_c(input_c):
inception_c_1_1 = conv_2d(input_c, 256, 1, activation='relu', name='inception_c_1_1')
inception_c_3_3_reduce = conv_2d(input_c, 384, filter_size=1, activation='relu', name='inception_c_3_3_reduce')
inception_c_3_3_asym_1 = conv_2d(inception_c_3_3_reduce, 256, filter_size=[1,3], activation='relu',name='inception_c_3_3_asym_1')
inception_c_3_3_asym_2 = conv_2d(inception_c_3_3_reduce, 256, filter_size=[3,1], activation='relu',name='inception_c_3_3_asym_2')
inception_c_3_3=merge([inception_c_3_3_asym_1,inception_c_3_3_asym_2],mode='concat',axis=3)
inception_c_5_5_reduce = conv_2d(input_c, 384, filter_size=1, activation='relu', name = 'inception_c_5_5_reduce')
inception_c_5_5_asym_1 = conv_2d(inception_c_5_5_reduce, 448, filter_size=[1,3], name = 'inception_c_5_5_asym_1')
inception_c_5_5_asym_2 = conv_2d(inception_c_5_5_asym_1, 512, filter_size=[3,1], activation='relu',name='inception_c_5_5_asym_2')
inception_c_5_5_asym_3 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[1,3], activation='relu',name='inception_c_5_5_asym_3')
inception_c_5_5_asym_4 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[3,1], activation='relu',name='inception_c_5_5_asym_4')
inception_c_5_5=merge([inception_c_5_5_asym_4,inception_c_5_5_asym_3],mode='concat',axis=3)
inception_c_pool = avg_pool_2d(input_c, kernel_size=3, strides=1 )
inception_c_pool_1_1 = conv_2d(inception_c_pool, 256, filter_size=1, activation='relu', name='inception_c_pool_1_1')
# merge the inception_c
inception_c_output = merge([inception_c_1_1, inception_c_3_3, inception_c_5_5, inception_c_pool_1_1], mode='concat', axis=3)
return inception_c_output
def inception_block_b(input_b):
inception_b_1_1 = conv_2d(input_b, 384, 1, activation='relu', name='inception_b_1_1')
inception_b_3_3_reduce = conv_2d(input_b, 192, filter_size=1, activation='relu', name='inception_b_3_3_reduce')
inception_b_3_3_asym_1 = conv_2d(inception_b_3_3_reduce, 224, filter_size=[1,7], activation='relu',name='inception_b_3_3_asym_1')
inception_b_3_3 = conv_2d(inception_b_3_3_asym_1, 256, filter_size=[7,1], activation='relu',name='inception_b_3_3')
inception_b_5_5_reduce = conv_2d(input_b, 192, filter_size=1, activation='relu', name = 'inception_b_5_5_reduce')
inception_b_5_5_asym_1 = conv_2d(inception_b_5_5_reduce, 192, filter_size=[7,1], name = 'inception_b_5_5_asym_1')
inception_b_5_5_asym_2 = conv_2d(inception_b_5_5_asym_1, 224, filter_size=[1,7], name = 'inception_b_5_5_asym_2')
inception_b_5_5_asym_3 = conv_2d(inception_b_5_5_asym_2, 224, filter_size=[7,1], name = 'inception_b_5_5_asym_3')
inception_b_5_5 = conv_2d(inception_b_5_5_asym_3, 256, filter_size=[1,7], name = 'inception_b_5_5')
inception_b_pool = avg_pool_2d(input_b, kernel_size=3, strides=1 )
inception_b_pool_1_1 = conv_2d(inception_b_pool, 128, filter_size=1, activation='relu', name='inception_b_pool_1_1')
# merge the inception_b
inception_b_output = merge([inception_b_1_1, inception_b_3_3, inception_b_5_5, inception_b_pool_1_1], mode='concat', axis=3)
return inception_b_output
def All_extract(input_x,
layer_name,
class_num,
training,
num_dims=256,
drop_rate=0.2):
with tf.name_scope(layer_name):
output_height = int(np.shape(input_x)[1])
output_weight = int(np.shape(input_x)[2])
local_x_list = []
logits_list = []
local_x1 = avg_pool_2d(input_x[:, :, :, :],
(output_height, output_weight),
name=layer_name + '_AvgPool2D')
# local_x1 = tf.layers.dropout(local_x1, rate=drop_rate, training=training,
# name=layer_name + '_drop1')
local_x1 = conv_layer(local_x1,
filter=num_dims,
kernel=[1, 1],
layer_name=layer_name + '_split_conv')
local_x1 = Batch_Normalization(local_x1,
training=training,
scope=layer_name + '_batch')
local_x1 = flatten(local_x1)
local_x_list.append(local_x1)
# local_x1 = tf.layers.dropout(local_x1, rate=drop_rate, training=training,
# name=layer_name + '_drop2')
local_x1 = Fully_connected(local_x1,
class_num,
use_bias=False,
layer_name=layer_name + '_fully_connected')
logits_list.append(local_x1)
return local_x_list, logits_list
def getReseau():
size = settings.size
nb_filter = settings.nb_filter
filter_size = settings.filter_size
# Make sure the data is normalized
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
# Create extra synthetic training data by flipping, rotating and blurring the
# images on our data set.
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)
img_aug.add_random_blur(sigma_max=3.)
# Define our network architecture:
# Input is a 32x32 image with 3 color channels (red, green and blue)
network = input_data(shape=[None, size, size, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
reseau = settings.reseau
if reseau == 1:
# Step 1: Convolution
network = conv_2d(network, nb_filter, filter_size, activation='relu')
# Step 2: Max pooling
network = max_pool_2d(network, 2)
# Step 3: Convolution again
network = conv_2d(network,
nb_filter * 2,
filter_size,
activation='relu')
# Step 4: Convolution yet again
network = conv_2d(network,
nb_filter * 2,
filter_size,
activation='relu')
# Step 5: Max pooling again
network = max_pool_2d(network, 2)
# Step 6: Fully-connected 512 node neural network
network = fully_connected(network, nb_filter * 8, activation='relu')
# Step 7: Dropout - throw away some data randomly during training to prevent over-fitting
network = dropout(network, 0.5)
elif reseau == 2:
network = conv_2d(network, 32, 3, activation='relu')
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 32, 3, activation='relu')
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = fully_connected(network, 512, activation='relu')
elif reseau == 3:
network = conv_2d(network, 32, 3, activation='relu')
network = avg_pool_2d(network, 2)
network = conv_2d(network, 32, 3, activation='relu')
network = avg_pool_2d(network, 2)
network = conv_2d(network, 32, 3, activation='relu')
network = avg_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
elif reseau == 4:
network = conv_2d(network, 32, 3, activation='relu')
network = conv_2d(network, 32, 3, activation='relu')
network = conv_2d(network, 32, 5, padding='valid', activation='relu')
network = conv_2d(network, 32, 3, activation='relu')
network = conv_2d(network, 32, 3, activation='relu')
network = conv_2d(network, 32, 5, padding='valid', activation='relu')
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
elif reseau == 5:
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = avg_pool_2d(network, 2)
network = conv_2d(network, 32, 3, activation='relu')
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = fully_connected(network, 512, activation='relu')
# Step 8: Fully-connected neural network with three outputs (0=isn't a bird, 1=is a bird) to make the final prediction
network = fully_connected(network,
ld.getLabelsNumber(),
activation='softmax')
# Tell tflearn how we want to train the network
network = regression(network,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=settings.learning_rate)
# Wrap the network in a model object
# model = tflearn.DNN(network, tensorboard_verbose=0, checkpoint_path='dataviz-classifier.tfl.ckpt')
model = tflearn.DNN(
network, tensorboard_verbose=0
) # , checkpoint_path='data-classifier/dataviz-classifier.tfl.ckpt')
return model
activation='prelu',
name='conv4_3')
pool4_3 = max_pool_2d(conv4_3, 3, strides=2)
#---------------------------------------------------------
inception_5_output = merge([inception_4_1, inception_4_2, pool4_3],
mode='concat',
axis=3)
pool5 = max_pool_2d(inception_5_output, kernel_size=5, strides=3)
conv5 = conv_2d(pool5, 64, 2, strides=1, activation='prelu', name='conv5')
conv5 = local_response_normalization(conv5)
conv5 = conv_2d(conv5, 64, 2, strides=1, activation='prelu')
conv5 = batch_normalization(conv5)
pool6 = avg_pool_2d(conv5, 2, strides=1)
net = dropout(pool6, DROPOUT_PROB)
loss = fully_connected(net, NUM_CLASS, activation='softmax')
network2 = regression(loss,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(network2,
tensorboard_dir='/home/guest/dujinhong/model1/',
checkpoint_path='/home/guest/dujinhong/model1/',
max_checkpoints=1,
tensorboard_verbose=0)
model.load('/home/guest/dujinhong/model1/DNN_trainer2', verbose=True)
model.fit(train_data,
def inception_v2(width, height, learning_rate):
num_classes = 17
dropout_keep_prob = 0.8
network = input_data(shape=[None, width, height, 3])
conv1a_3_3 = relu(
batch_normalization(
conv_2d(network,
32,
3,
strides=2,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(
batch_normalization(
conv_2d(conv1a_3_3,
32,
3,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(
batch_normalization(
conv_2d(conv2a_3_3,
64,
3,
bias=False,
activation=None,
name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3,
3,
strides=2,
padding='VALID',
name='MaxPool_3a_3x3')
conv3b_1_1 = relu(
batch_normalization(
conv_2d(maxpool3a_3_3,
80,
1,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(
batch_normalization(
conv_2d(conv3b_1_1,
192,
3,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3,
3,
strides=2,
padding='VALID',
name='MaxPool_5a_3x3')
tower_conv = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
96,
1,
bias=False,
activation=None,
name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
48,
1,
bias=False,
activation=None,
name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
64,
5,
bias=False,
activation=None,
name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
64,
1,
bias=False,
activation=None,
name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2_0,
96,
3,
bias=False,
activation=None,
name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
96,
3,
bias=False,
activation=None,
name='Conv2d_5b_b2_0c_3x3')))
tower_pool3_0 = avg_pool_2d(maxpool5a_3_3,
3,
strides=1,
padding='same',
name='AvgPool_5b_b3_0a_3x3')
tower_conv3_1 = relu(
batch_normalization(
conv_2d(tower_pool3_0,
64,
1,
bias=False,
activation=None,
name='Conv2d_5b_b3_0b_1x1')))
tower_5b_out = merge(
[tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1],
mode='concat',
axis=3)
net = repeat(tower_5b_out, 10, block35, scale=0.17)
tower_conv = relu(
batch_normalization(
conv_2d(net,
384,
3,
bias=False,
strides=2,
activation=None,
padding='VALID',
name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
256,
3,
bias=False,
activation=None,
name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(
batch_normalization(
conv_2d(tower_conv1_1,
384,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net,
3,
strides=2,
padding='VALID',
name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(
batch_normalization(
conv_2d(tower_conv,
384,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1,
288,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='COnv2d_1a_3x3')))
tower_conv2 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2,
288,
3,
bias=False,
name='Conv2d_0b_3x3',
activation=None)))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
320,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net,
3,
strides=2,
padding='VALID',
name='MaxPool_1a_3x3')
net = merge([tower_conv0_1, tower_conv1_1, tower_conv2_2, tower_pool],
mode='concat',
axis=3)
net = repeat(net, 9, block8, scale=0.2)
net = block8(net, activation=None)
net = relu(
batch_normalization(
conv_2d(net,
1536,
1,
bias=False,
activation=None,
name='Conv2d_7b_1x1')))
net = avg_pool_2d(net,
net.get_shape().as_list()[1:3],
strides=2,
padding='VALID',
name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, num_classes, activation='softmax')
network = tflearn.regression(loss,
optimizer='RMSprop',
loss='categorical_crossentropy',
learning_rate=learning_rate)
#learning_rate=0.0001)
model = tflearn.DNN(network,
checkpoint_path='inception_resnet_v2',
max_checkpoints=1,
tensorboard_verbose=2,
tensorboard_dir="./tflearn_logs/")
return model
(X, Y), (X_test, Y_test) = cifar10.load_data()
X, Y = shuffle(X, Y)
Y = to_categorical(Y, 10)
Y_test = to_categorical(Y_test, 10)
# Building 'Network In Network'
network = input_data(shape=[None, 32, 32, 3])
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 160, 1, activation='relu')
network = conv_2d(network, 96, 1, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = avg_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 3, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 10, 1, activation='relu')
network = avg_pool_2d(network, 8)
network = flatten(network)
network = regression(network,
optimizer='adam',
loss='softmax_categorical_crossentropy',
learning_rate=0.001)
# training
model = tflearn.DNN(network)
model.fit(X,
Y,
def _model5():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
def block35(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 32, 3, bias=False, activation=None,name='Conv2d_0b_3x3')))
tower_conv2_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 48,3, bias=False, activation=None, name='Conv2d_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 64,3, bias=False, activation=None, name='Conv2d_0c_3x3')))
tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block17(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv_1_0 = relu(batch_normalization(conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv_1_1 = relu(batch_normalization(conv_2d(tower_conv_1_0, 160,[1,7], bias=False, activation=None,name='Conv2d_0b_1x7')))
tower_conv_1_2 = relu(batch_normalization(conv_2d(tower_conv_1_1, 192, [7,1], bias=False, activation=None,name='Conv2d_0c_7x1')))
tower_mixed = merge([tower_conv,tower_conv_1_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block8(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1')))
tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
num_classes = len(Y[0])
dropout_keep_prob = 0.8
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3')
conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3')
tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3')))
tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3')
tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1')))
tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3)
net = repeat(tower_5b_out, 10, block35, scale=0.17)
'''
tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,3, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,3, bias=False, name='Conv2d_0b_3x3',activation=None)))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 3, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID', name='MaxPool_1a_3x3')
'''
tower_conv = relu(batch_normalization(conv_2d(net, 384, 1, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID',name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,1, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,1, bias=False, name='Conv2d_0b_3x3',activation=None)))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 1, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID', name='MaxPool_1a_3x3')
####
net = merge([tower_conv0_1, tower_conv1_1,tower_conv2_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 9, block8, scale=0.2)
net = block8(net, activation=None)
net = relu(batch_normalization(conv_2d(net, 1536, 1, bias=False, activation=None, name='Conv2d_7b_1x1')))
net = avg_pool_2d(net, net.get_shape().as_list()[1:3],strides=2, padding='VALID', name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, num_classes,activation='softmax')
network = tflearn.regression(loss, optimizer='RMSprop',
loss='categorical_crossentropy',
learning_rate=0.0001)
model = tflearn.DNN(network, checkpoint_path='inception_resnet_v2',
max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir="./tflearn_logs/")
model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), shuffle=True,
show_metric=True, batch_size=batchNum, snapshot_step=2000,
snapshot_epoch=False, run_id='inception_resnet_v2_oxflowers17')
if modelStore: model.save(_id + '-model.tflearn')
def ImageNetInceptionV2(outnode,
model_name,
target,
opt,
learn_r,
epch,
dropout_keep_rate,
save_model=False):
def block35(net, scale=1.0, activation="relu"):
tower_conv = relu(
batch_normalization(
conv_2d(net,
32,
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(net,
32,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
32,
3,
bias=False,
activation=None,
name='Conv2d_0b_3x3')))
tower_conv2_0 = relu(
batch_normalization(
conv_2d(net,
32,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2_0,
48,
3,
bias=False,
activation=None,
name='Conv2d_0b_3x3')))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
64,
3,
bias=False,
activation=None,
name='Conv2d_0c_3x3')))
tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2],
mode='concat',
axis=3)
tower_out = relu(
batch_normalization(
conv_2d(tower_mixed,
net.get_shape()[3],
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block17(net, scale=1.0, activation="relu"):
tower_conv = relu(
batch_normalization(
conv_2d(net,
192,
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
tower_conv_1_0 = relu(
batch_normalization(
conv_2d(net,
128,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv_1_1 = relu(
batch_normalization(
conv_2d(tower_conv_1_0,
160, [1, 7],
bias=False,
activation=None,
name='Conv2d_0b_1x7')))
tower_conv_1_2 = relu(
batch_normalization(
conv_2d(tower_conv_1_1,
192, [7, 1],
bias=False,
activation=None,
name='Conv2d_0c_7x1')))
tower_mixed = merge([tower_conv, tower_conv_1_2],
mode='concat',
axis=3)
tower_out = relu(
batch_normalization(
conv_2d(tower_mixed,
net.get_shape()[3],
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block8(net, scale=1.0, activation="relu"):
tower_conv = relu(
batch_normalization(
conv_2d(net,
192,
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(net,
192,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
224, [1, 3],
bias=False,
activation=None,
name='Conv2d_0b_1x3')))
tower_conv1_2 = relu(
batch_normalization(
conv_2d(tower_conv1_1,
256, [3, 1],
bias=False,
name='Conv2d_0c_3x1')))
tower_mixed = merge([tower_conv, tower_conv1_2], mode='concat', axis=3)
tower_out = relu(
batch_normalization(
conv_2d(tower_mixed,
net.get_shape()[3],
1,
bias=False,
activation=None,
name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
# default = 0.8
dropout_keep_prob = dropout_keep_rate
network = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
conv1a_3_3 = relu(
batch_normalization(
conv_2d(network,
32,
3,
strides=2,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(
batch_normalization(
conv_2d(conv1a_3_3,
32,
3,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(
batch_normalization(
conv_2d(conv2a_3_3,
64,
3,
bias=False,
activation=None,
name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3,
3,
strides=2,
padding='VALID',
name='MaxPool_3a_3x3')
conv3b_1_1 = relu(
batch_normalization(
conv_2d(maxpool3a_3_3,
80,
1,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(
batch_normalization(
conv_2d(conv3b_1_1,
192,
3,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3,
3,
strides=2,
padding='VALID',
name='MaxPool_5a_3x3')
tower_conv = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
96,
1,
bias=False,
activation=None,
name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
48,
1,
bias=False,
activation=None,
name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
64,
5,
bias=False,
activation=None,
name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(
batch_normalization(
conv_2d(maxpool5a_3_3,
64,
1,
bias=False,
activation=None,
name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2_0,
96,
3,
bias=False,
activation=None,
name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
96,
3,
bias=False,
activation=None,
name='Conv2d_5b_b2_0c_3x3')))
tower_pool3_0 = avg_pool_2d(maxpool5a_3_3,
3,
strides=1,
padding='same',
name='AvgPool_5b_b3_0a_3x3')
tower_conv3_1 = relu(
batch_normalization(
conv_2d(tower_pool3_0,
64,
1,
bias=False,
activation=None,
name='Conv2d_5b_b3_0b_1x1')))
tower_5b_out = merge(
[tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1],
mode='concat',
axis=3)
net = repeat(tower_5b_out, 10, block35, scale=0.17)
tower_conv = relu(
batch_normalization(
conv_2d(net,
384,
3,
bias=False,
strides=2,
activation=None,
padding='VALID',
name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1_0,
256,
3,
bias=False,
activation=None,
name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(
batch_normalization(
conv_2d(tower_conv1_1,
384,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net,
3,
strides=2,
padding='VALID',
name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(
batch_normalization(
conv_2d(tower_conv,
384,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(
batch_normalization(
conv_2d(tower_conv1,
288,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='COnv2d_1a_3x3')))
tower_conv2 = relu(
batch_normalization(
conv_2d(net,
256,
1,
bias=False,
activation=None,
name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(
batch_normalization(
conv_2d(tower_conv2,
288,
3,
bias=False,
name='Conv2d_0b_3x3',
activation=None)))
tower_conv2_2 = relu(
batch_normalization(
conv_2d(tower_conv2_1,
320,
3,
bias=False,
strides=2,
padding='VALID',
activation=None,
name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net,
3,
strides=2,
padding='VALID',
name='MaxPool_1a_3x3')
net = merge([tower_conv0_1, tower_conv1_1, tower_conv2_2, tower_pool],
mode='concat',
axis=3)
net = repeat(net, 9, block8, scale=0.2)
net = block8(net, activation=None)
net = relu(
batch_normalization(
conv_2d(net,
1536,
1,
bias=False,
activation=None,
name='Conv2d_7b_1x1')))
net = avg_pool_2d(net,
net.get_shape().as_list()[1:3],
strides=2,
padding='VALID',
name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, outnode, activation='softmax')
str_model_name = "{}_{}_{}_{}_{}_{}".format(model_name, target, opt,
learn_r, epch,
dropout_keep_rate)
network = tflearn.regression(loss,
optimizer=opt,
loss='categorical_crossentropy',
learning_rate=learn_r,
name='targets')
model = None
if save_model:
model = tflearn.DNN(
network,
checkpoint_path='../tflearnModels/{}'.format(str_model_name),
best_checkpoint_path='../tflearnModels/bestModels/best_{}'.format(
str_model_name),
max_checkpoints=1,
tensorboard_verbose=0,
tensorboard_dir="../tflearnLogs/{}/".format(str_model_name))
else:
model = tflearn.DNN(network)
return model
def __init__(self, img_size, label_size, channels):
# Image input for training
self.img_size = img_size
# Label input for training
self.label_size = label_size
# Channels input for training
self.channels = channels
# GoogleNet algorithm
self.network = input_data(shape=[None, img_size, img_size, channels],
name='data')
self.conv1_7x7_s2 = conv_2d(self.network,
64,
7,
strides=2,
activation='relu',
name='conv1_7x7_s2')
self.pool1_3x3_s2 = max_pool_2d(self.conv1_7x7_s2,
3,
strides=2,
name='pool1_3x3_s2')
# Special case
self.pool1_3x3_s2 = local_response_normalization(self.pool1_3x3_s2,
name='pool1_norm1')
self.conv2_3x3_reduce = conv_2d(self.pool1_3x3_s2,
64,
1,
activation='relu',
name='conv2_3x3_reduce')
self.conv2_3x3 = conv_2d(self.conv2_3x3_reduce,
192,
3,
activation='relu',
name='conv2_3x3')
# Special case
self.conv2_3x3 = local_response_normalization(self.conv2_3x3,
name='conv2_norm2')
self.pool2_3x3_s2 = max_pool_2d(self.conv2_3x3,
kernel_size=3,
strides=2,
name='pool2_3x3_s2')
self.inception_3a_1x1 = conv_2d(self.pool2_3x3_s2,
64,
1,
activation='relu',
name='inception_3a_1x1')
self.inception_3a_3x3_reduce = conv_2d(self.pool2_3x3_s2,
96,
1,
activation='relu',
name='inception_3a_3x3_reduce')
self.inception_3a_3x3 = conv_2d(self.inception_3a_3x3_reduce,
128,
filter_size=3,
activation='relu',
name='inception_3a_3x3')
self.inception_3a_5x5_reduce = conv_2d(self.pool2_3x3_s2,
16,
filter_size=1,
activation='relu',
name='inception_3a_5x5_reduce')
self.inception_3a_5x5 = conv_2d(self.inception_3a_5x5_reduce,
32,
filter_size=5,
activation='relu',
name='inception_3a_5x5')
self.inception_3a_pool = max_pool_2d(self.pool2_3x3_s2,
kernel_size=3,
strides=1,
name='inception_3a_pool')
self.inception_3a_pool_proj = conv_2d(self.inception_3a_pool,
32,
filter_size=1,
activation='relu',
name='inception_3a_pool_proj')
# Merge the inception 3a
self.inception_3a_output = merge([
self.inception_3a_1x1, self.inception_3a_3x3,
self.inception_3a_5x5, self.inception_3a_pool_proj
],
mode='concat',
axis=3,
name='inception_3a_output')
self.inception_3b_1x1 = conv_2d(self.inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_1x1')
self.inception_3b_3x3_reduce = conv_2d(self.inception_3a_output,
128,
filter_size=1,
activation='relu',
name='inception_3b_3x3_reduce')
self.inception_3b_3x3 = conv_2d(self.inception_3b_3x3_reduce,
192,
filter_size=3,
activation='relu',
name='inception_3b_3x3')
self.inception_3b_5x5_reduce = conv_2d(self.inception_3a_output,
32,
filter_size=1,
activation='relu',
name='inception_3b_5x5_reduce')
self.inception_3b_5x5 = conv_2d(self.inception_3b_5x5_reduce,
96,
filter_size=5,
name='inception_3b_5x5')
self.inception_3b_pool = max_pool_2d(self.inception_3a_output,
kernel_size=3,
strides=1,
name='inception_3b_pool')
self.inception_3b_pool_proj = conv_2d(self.inception_3b_pool,
64,
filter_size=1,
activation='relu',
name='inception_3b_pool_proj')
# Merge the inception 3b
self.inception_3b_output = merge([
self.inception_3b_1x1, self.inception_3b_3x3,
self.inception_3b_5x5, self.inception_3b_pool_proj
],
mode='concat',
axis=3,
name='inception_3b_output')
self.pool3_3x3_s2 = max_pool_2d(self.inception_3b_output,
kernel_size=3,
strides=2,
name='pool3_3x3_s2')
self.inception_4a_1x1 = conv_2d(self.pool3_3x3_s2,
192,
filter_size=1,
activation='relu',
name='inception_4a_1x1')
self.inception_4a_3x3_reduce = conv_2d(self.pool3_3x3_s2,
96,
filter_size=1,
activation='relu',
name='inception_4a_3x3_reduce')
self.inception_4a_3x3 = conv_2d(self.inception_4a_3x3_reduce,
208,
filter_size=3,
activation='relu',
name='inception_4a_3x3')
self.inception_4a_5x5_reduce = conv_2d(self.pool3_3x3_s2,
16,
filter_size=1,
activation='relu',
name='inception_4a_5x5_reduce')
self.inception_4a_5x5 = conv_2d(self.inception_4a_5x5_reduce,
48,
filter_size=5,
activation='relu',
name='inception_4a_5x5')
self.inception_4a_pool = max_pool_2d(self.pool3_3x3_s2,
kernel_size=3,
strides=1,
name='inception_4a_pool')
self.inception_4a_pool_proj = conv_2d(self.inception_4a_pool,
64,
filter_size=1,
activation='relu',
name='inception_4a_pool_proj')
self.inception_4a_output = merge([
self.inception_4a_1x1, self.inception_4a_3x3,
self.inception_4a_5x5, self.inception_4a_pool_proj
],
mode='concat',
axis=3,
name='inception_4a_output')
self.inception_4b_1x1 = conv_2d(self.inception_4a_output,
160,
filter_size=1,
activation='relu',
name='inception_4b_1x1')
self.inception_4b_3x3_reduce = conv_2d(self.inception_4a_output,
112,
filter_size=1,
activation='relu',
name='inception_4b_3x3_reduce')
self.inception_4b_3x3 = conv_2d(self.inception_4b_3x3_reduce,
224,
filter_size=3,
activation='relu',
name='inception_4b_3x3')
self.inception_4b_5x5_reduce = conv_2d(self.inception_4a_output,
24,
filter_size=1,
activation='relu',
name='inception_4b_5x5_reduce')
self.inception_4b_5x5 = conv_2d(self.inception_4b_5x5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4b_5x5')
self.inception_4b_pool = max_pool_2d(self.inception_4a_output,
kernel_size=3,
strides=1,
name='inception_4b_pool')
self.inception_4b_pool_proj = conv_2d(self.inception_4b_pool,
64,
filter_size=1,
activation='relu',
name='inception_4b_pool_proj')
self.inception_4b_output = merge([
self.inception_4b_1x1, self.inception_4b_3x3,
self.inception_4b_5x5, self.inception_4b_pool_proj
],
mode='concat',
axis=3,
name='inception_4b_output')
self.inception_4c_1x1 = conv_2d(self.inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_1x1')
self.inception_4c_3x3_reduce = conv_2d(self.inception_4b_output,
128,
filter_size=1,
activation='relu',
name='inception_4c_3x3_reduce')
self.inception_4c_3x3 = conv_2d(self.inception_4c_3x3_reduce,
256,
filter_size=3,
activation='relu',
name='inception_4c_3x3')
self.inception_4c_5x5_reduce = conv_2d(self.inception_4b_output,
24,
filter_size=1,
activation='relu',
name='inception_4c_5x5_reduce')
self.inception_4c_5x5 = conv_2d(self.inception_4c_5x5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4c_5x5')
self.inception_4c_pool = max_pool_2d(self.inception_4b_output,
kernel_size=3,
strides=1,
name='inception_4c_pool')
self.inception_4c_pool_proj = conv_2d(self.inception_4c_pool,
64,
filter_size=1,
activation='relu',
name='inception_4c_pool_proj')
self.inception_4c_output = merge([
self.inception_4c_1x1, self.inception_4c_3x3,
self.inception_4c_5x5, self.inception_4c_pool_proj
],
mode='concat',
axis=3,
name='inception_4c_output')
self.inception_4d_1x1 = conv_2d(self.inception_4c_output,
112,
filter_size=1,
activation='relu',
name='inception_4d_1x1')
self.inception_4d_3x3_reduce = conv_2d(self.inception_4c_output,
144,
filter_size=1,
activation='relu',
name='inception_4d_3x3_reduce')
self.inception_4d_3x3 = conv_2d(self.inception_4d_3x3_reduce,
288,
filter_size=3,
activation='relu',
name='inception_4d_3x3')
self.inception_4d_5x5_reduce = conv_2d(self.inception_4c_output,
32,
filter_size=1,
activation='relu',
name='inception_4d_5x5_reduce')
self.inception_4d_5x5 = conv_2d(self.inception_4d_5x5_reduce,
64,
filter_size=5,
activation='relu',
name='inception_4d_5x5')
self.inception_4d_pool = max_pool_2d(self.inception_4c_output,
kernel_size=3,
strides=1,
name='inception_4d_pool')
self.inception_4d_pool_proj = conv_2d(self.inception_4d_pool,
64,
filter_size=1,
activation='relu',
name='inception_4d_pool_proj')
self.inception_4d_output = merge([
self.inception_4d_1x1, self.inception_4d_3x3,
self.inception_4d_5x5, self.inception_4d_pool_proj
],
mode='concat',
axis=3,
name='inception_4d_output')
self.inception_4e_1x1 = conv_2d(self.inception_4d_output,
256,
filter_size=1,
activation='relu',
name='inception_4e_1x1')
self.inception_4e_3x3_reduce = conv_2d(self.inception_4d_output,
160,
filter_size=1,
activation='relu',
name='inception_4e_3x3_reduce')
self.inception_4e_3x3 = conv_2d(self.inception_4e_3x3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_4e_3x3')
self.inception_4e_5x5_reduce = conv_2d(self.inception_4d_output,
32,
filter_size=1,
activation='relu',
name='inception_4e_5x5_reduce')
self.inception_4e_5x5 = conv_2d(self.inception_4e_5x5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_4e_5x5')
self.inception_4e_pool = max_pool_2d(self.inception_4d_output,
kernel_size=3,
strides=1,
name='inception_4e_pool')
self.inception_4e_pool_proj = conv_2d(self.inception_4e_pool,
128,
filter_size=1,
activation='relu',
name='inception_4e_pool_proj')
self.inception_4e_output = merge([
self.inception_4e_1x1, self.inception_4e_3x3,
self.inception_4e_5x5, self.inception_4e_pool_proj
],
axis=3,
mode='concat',
name='inception_4e_output')
self.pool4_3x3_s2 = max_pool_2d(self.inception_4e_output,
kernel_size=3,
strides=2,
name='pool4_3x3_s2')
self.inception_5a_1x1 = conv_2d(self.pool4_3x3_s2,
256,
filter_size=1,
activation='relu',
name='inception_5a_1x1')
self.inception_5a_3x3_reduce = conv_2d(self.pool4_3x3_s2,
160,
filter_size=1,
activation='relu',
name='inception_5a_3x3_reduce')
self.inception_5a_3x3 = conv_2d(self.inception_5a_3x3_reduce,
320,
filter_size=3,
activation='relu',
name='inception_5a_3x3')
self.inception_5a_5x5_reduce = conv_2d(self.pool4_3x3_s2,
32,
filter_size=1,
activation='relu',
name='inception_5a_5x5_reduce')
self.inception_5a_5x5 = conv_2d(self.inception_5a_5x5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5a_5x5')
self.inception_5a_pool = max_pool_2d(self.pool4_3x3_s2,
kernel_size=3,
strides=1,
name='inception_5a_pool')
self.inception_5a_pool_proj = conv_2d(self.inception_5a_pool,
128,
filter_size=1,
activation='relu',
name='inception_5a_pool_proj')
self.inception_5a_output = merge([
self.inception_5a_1x1, self.inception_5a_3x3,
self.inception_5a_5x5, self.inception_5a_pool_proj
],
axis=3,
mode='concat',
name="inception_5a_output")
self.inception_5b_1x1 = conv_2d(self.inception_5a_output,
384,
filter_size=1,
activation='relu',
name='inception_5b_1x1')
self.inception_5b_3x3_reduce = conv_2d(self.inception_5a_output,
192,
filter_size=1,
activation='relu',
name='inception_5b_3x3_reduce')
self.inception_5b_3x3 = conv_2d(self.inception_5b_3x3_reduce,
384,
filter_size=3,
activation='relu',
name='inception_5b_3x3')
self.inception_5b_5x5_reduce = conv_2d(self.inception_5a_output,
48,
filter_size=1,
activation='relu',
name='inception_5b_5x5_reduce')
self.inception_5b_5x5 = conv_2d(self.inception_5b_5x5_reduce,
128,
filter_size=5,
activation='relu',
name='inception_5b_5x5')
self.inception_5b_pool = max_pool_2d(self.inception_5a_output,
kernel_size=3,
strides=1,
name='inception_5b_pool')
self.inception_5b_pool_proj = conv_2d(self.inception_5b_pool,
128,
filter_size=1,
activation='relu',
name='inception_5b_pool_proj')
self.inception_5b_output = merge([
self.inception_5b_1x1, self.inception_5b_3x3,
self.inception_5b_5x5, self.inception_5b_pool_proj
],
axis=3,
mode='concat',
name='inception_5b_output')
self.pool5_7x7_s1 = avg_pool_2d(self.inception_5b_output,
kernel_size=7,
strides=1,
name='pool5_7x7_s1')
self.pool5_7x7_s1 = dropout(self.pool5_7x7_s1,
0.4,
name="dropout_7x7_s1")
# Output
self.loss3_classifier = fully_connected(self.pool5_7x7_s1,
label_size,
activation='softmax',
name='loss3_classifier')
self.network = regression(self.loss3_classifier,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
self.model = tflearn.DNN(self.network, tensorboard_verbose=0)
def inception_v3(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
network = input_data(shape=[None, width, height,3], name='input')
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3,name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3,mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, output,activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network,
max_checkpoints=0, tensorboard_verbose=0,tensorboard_dir='log')
return model
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
# fc
loss = fully_connected(pool5_7_7, 17, activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
# to train
model = tflearn.DNN(network, checkpoint_path='model_googlenet',
max_checkpoints=1, tensorboard_verbose=2)
model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True,
show_metric=True, batch_size=64, snapshot_step=200,
snapshot_epoch=False, run_id='googlenet_oxflowers17')
def _model4():
# Taken from TFLearn examples and based on Googles Inception. DO NOT RUN!!!!
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
# 3a
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
# 3b
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1')
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
# 4a
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
# 4b
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
# 4c
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output')
# 4d
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
# 4e
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
# 5a
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
# fc
loss = fully_connected(pool5_7_7, len(yTest[0]), activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
# to train
model = tflearn.DNN(network, checkpoint_path='model_googlenet',
max_checkpoints=1, tensorboard_verbose=2)
model.load(_path)
pred = model.predict(xTest)
df = pd.DataFrame(pred)
df.to_csv(_path + ".csv")
newList = pred.copy()
newList = convert2(newList)
if _CSV: makeCSV(newList)
pred = convert2(pred)
pred = convert3(pred)
yTest = convert3(yTest)
print(metrics.confusion_matrix(yTest, pred))
print(metrics.classification_report(yTest, pred))
print('Accuracy', accuracy_score(yTest, pred))
print()
if _wrFile: writeTest(pred)
conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3'))) maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3') conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1'))) conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3'))) maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3') tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5'))) tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3'))) tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3') tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1'))) tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3) net = repeat(tower_5b_out, 10, block35, scale=0.17) tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
def _model5():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
def block35(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 32, 3, bias=False, activation=None,name='Conv2d_0b_3x3')))
tower_conv2_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 48,3, bias=False, activation=None, name='Conv2d_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 64,3, bias=False, activation=None, name='Conv2d_0c_3x3')))
tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block17(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv_1_0 = relu(batch_normalization(conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv_1_1 = relu(batch_normalization(conv_2d(tower_conv_1_0, 160,[1,7], bias=False, activation=None,name='Conv2d_0b_1x7')))
tower_conv_1_2 = relu(batch_normalization(conv_2d(tower_conv_1_1, 192, [7,1], bias=False, activation=None,name='Conv2d_0c_7x1')))
tower_mixed = merge([tower_conv,tower_conv_1_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
def block8(net, scale=1.0, activation="relu"):
tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1')))
tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3)
tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
net += scale * tower_out
if activation:
if isinstance(activation, str):
net = activations.get(activation)(net)
elif hasattr(activation, '__call__'):
net = activation(net)
else:
raise ValueError("Invalid Activation.")
return net
num_classes = len(yTest[0])
dropout_keep_prob = 0.8
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3')))
conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3')
conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3')))
maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3')
tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1')))
tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5')))
tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3')))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3')))
tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3')
tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1')))
tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3)
net = repeat(tower_5b_out, 10, block35, scale=0.17)
'''
tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,3, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,3, bias=False, name='Conv2d_0b_3x3',activation=None)))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 3, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID', name='MaxPool_1a_3x3')
'''
tower_conv = relu(batch_normalization(conv_2d(net, 384, 1, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3')))
tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3')))
tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3')))
tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID',name='MaxPool_1a_3x3')
net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 20, block17, scale=0.1)
tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,1, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3')))
tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1')))
tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,1, bias=False, name='Conv2d_0b_3x3',activation=None)))
tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 1, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3')))
tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID', name='MaxPool_1a_3x3')
####
net = merge([tower_conv0_1, tower_conv1_1,tower_conv2_2, tower_pool], mode='concat', axis=3)
net = repeat(net, 9, block8, scale=0.2)
net = block8(net, activation=None)
net = relu(batch_normalization(conv_2d(net, 1536, 1, bias=False, activation=None, name='Conv2d_7b_1x1')))
net = avg_pool_2d(net, net.get_shape().as_list()[1:3],strides=2, padding='VALID', name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, num_classes,activation='softmax')
network = tflearn.regression(loss, optimizer='RMSprop',
loss='categorical_crossentropy',
learning_rate=0.0001)
model = tflearn.DNN(network, checkpoint_path='inception_resnet_v2',
max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir="./tflearn_logs/")
model.load(_path)
pred = model.predict(xTest)
df = pd.DataFrame(pred)
df.to_csv(_path + ".csv")
newList = pred.copy()
newList = convert2(newList)
if _CSV: makeCSV(newList)
pred = convert2(pred)
pred = convert3(pred)
yTest = convert3(yTest)
print(metrics.confusion_matrix(yTest, pred))
print(metrics.classification_report(yTest, pred))
print('Accuracy', accuracy_score(yTest, pred))
print()
if _wrFile: writeTest(pred)
def create_Inception(num_classes):
# Building 'modfied google network'
network = input_data(shape=[None, FLAGS.sample_size, FLAGS.sample_size, 1])
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
# 3a
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
# 3b
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1')
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
# 4a
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
# 4b
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
# 4c
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output')
# 4d
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
# 4e
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
# 5a
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.5)
# fc
loss = fully_connected(pool5_7_7, num_classes, activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
return network
def construct_inceptionv1onfire (x,y, training=False):
# 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')
# if training then add training hyperparameters
if(training):
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
else:
network = loss;
model = tflearn.DNN(network, checkpoint_path='inceptiononv1onfire',
max_checkpoints=1, tensorboard_verbose=2)
return model
name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output,
kernel_size=3,
strides=1,
name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool,
128,
filter_size=1,
activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([
inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1
],
axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
# fc
loss = fully_connected(pool5_7_7, 17, activation='softmax')
network = regression(loss,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
# to train
model = tflearn.DNN(network,
checkpoint_path='model_googlenet',
max_checkpoints=1,
tensorboard_verbose=2)
def construct_inceptionv1onfire (x,y):
# Build network as per architecture in [Dunnings/Breckon, 2018]
network = input_data(shape=[None, y, x, 3])
conv1_7_7 = conv_2d(network, 64, 5, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 128,3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
pool5_7_7 = avg_pool_2d(inception_4a_output, kernel_size=5, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, 2,activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(network, checkpoint_path='inceptiononv1onfire',
max_checkpoints=1, tensorboard_verbose=2)
return model
'''
def _model4():
# Taken from TFLearn examples and based on Googles Inception. DO NOT RUN!!!!
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
network = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
# 3a
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
# 3b
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1')
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
# 4a
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
# 4b
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
# 4c
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output')
# 4d
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
# 4e
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
# 5a
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
# fc
loss = fully_connected(pool5_7_7, len(Y[0]), activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
# to train
model = tflearn.DNN(network, checkpoint_path='model_googlenet',
max_checkpoints=1, tensorboard_verbose=2)
model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), shuffle=True,
show_metric=True, batch_size=batchNum, snapshot_step=200,
snapshot_epoch=False, run_id='googlenet_oxflowers17')
if modelStore: model.save(_id + '-model.tflearn')
def googlenet(width,
height,
frame_count,
lr,
output=6,
model_name='sentnet_color.model'):
tflearn.init_graph()
with tf.device('/gpu:0'):
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
def build_le_network(self, network):
""" Build google net. (google inception v3)
Args:
network:
Returns:
"""
from tflearn.layers.merge_ops import merge
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2,
name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu',
name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3,
activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1,
activation='relu',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5,
activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1,
activation='relu',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge(
[inception_3a_1_1, inception_3a_3_3, inception_3a_5_5,
inception_3a_pool_1_1], mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1,
activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128,
filter_size=1, activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3,
activation='relu', name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32,
filter_size=1, activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5,
name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3,
strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,
activation='relu',
name='inception_3b_pool_1_1')
# merge the inception_3b_*
inception_3b_output = merge(
[inception_3b_1_1, inception_3b_3_3, inception_3b_5_5,
inception_3b_pool_1_1], mode='concat', axis=3,
name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2,
name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1,
activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1,
activation='relu',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3,
activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1,
activation='relu',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5,
activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1,
name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1,
activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge(
[inception_4a_1_1, inception_4a_3_3, inception_4a_5_5,
inception_4a_pool_1_1], mode='concat', axis=3,
name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1,
activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112,
filter_size=1, activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3,
activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24,
filter_size=1, activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5,
activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3,
strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1,
activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge(
[inception_4b_1_1, inception_4b_3_3, inception_4b_5_5,
inception_4b_pool_1_1], mode='concat', axis=3,
name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1,
activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128,
filter_size=1, activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3,
activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24,
filter_size=1, activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5,
activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3,
strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1,
activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge(
[inception_4c_1_1, inception_4c_3_3, inception_4c_5_5,
inception_4c_pool_1_1], mode='concat', axis=3,
name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1,
activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144,
filter_size=1, activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3,
activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32,
filter_size=1, activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5,
activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3,
strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1,
activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge(
[inception_4d_1_1, inception_4d_3_3, inception_4d_5_5,
inception_4d_pool_1_1], mode='concat', axis=3,
name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1,
activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160,
filter_size=1, activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3,
activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32,
filter_size=1, activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5,
activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3,
strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1,
activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge(
[inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,
inception_4e_pool_1_1], axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2,
name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1,
activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1,
activation='relu',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3,
activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1,
activation='relu',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5,
activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1,
name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1,
activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge(
[inception_5a_1_1, inception_5a_3_3, inception_5a_5_5,
inception_5a_pool_1_1], axis=3, mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1,
activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192,
filter_size=1, activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3,
activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48,
filter_size=1, activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5,
activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3,
strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1,
activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge(
[inception_5b_1_1, inception_5b_3_3, inception_5b_5_5,
inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, 2, activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
return network
def build_net(network, X, Y, num_classes, num_epochs, checkpoint_path, size_batch, Xval=None, Yval=None, dec_step=100,
train=True):
tn = tflearn.initializations.truncated_normal(seed=100)
xav = tflearn.initializations.xavier(seed=100)
nor = tflearn.initializations.normal(seed=100)
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
# 3a
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu',
name='inception_3a_pool_1_1')
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1],
mode='concat', axis=3)
# 3b
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu',
name='inception_3b_pool_1_1')
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1],
mode='concat', axis=3, name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
# 4a
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu',
name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1],
mode='concat', axis=3, name='inception_4a_output')
# 4b
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu',
name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1],
mode='concat', axis=3, name='inception_4b_output')
# 4c
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu',
name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1],
mode='concat', axis=3, name='inception_4c_output')
# 4d
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu',
name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1],
mode='concat', axis=3, name='inception_4d_output')
# 4e
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu',
name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3,
mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
# 5a
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu',
name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3,
mode='concat')
# 5b
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu',
name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3,
mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
# fc
loss = fully_connected(pool5_7_7, num_classes, activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
# Train
model = tflearn.DNN(network, tensorboard_verbose=0, checkpoint_path=checkpoint_path)
if train:
start_time = time.time()
if Xval is None or Yval is None:
model.fit(X, Y, n_epoch=num_epochs,
validation_set=0.0,
show_metric=True, run_id='hsi_cnn_model', shuffle=True, batch_size=size_batch)
else:
model.fit(X, Y, n_epoch=num_epochs,
validation_set=(Xval, Yval),
show_metric=True, run_id='hsi_cnn_model', shuffle=True, batch_size=size_batch)
print("\n\n-------------train time: %s seconds\n\n" % (time.time() - start_time))
return model
(X, Y), (X_test, Y_test) = cifar10.load_data()
X, Y = shuffle(X, Y)
Y = to_categorical(Y, 10)
Y_test = to_categorical(Y_test, 10)
# Building 'Network In Network'
network = input_data(shape=[None, 32, 32, 3])
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 160, 1, activation='relu')
network = conv_2d(network, 96, 1, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = avg_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 3, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 10, 1, activation='relu')
network = avg_pool_2d(network, 8)
network = flatten(network)
network = regression(network, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
# Training
model = tflearn.DNN(network)
model.fit(X, Y, n_epoch=50, shuffle=True, validation_set=(X_test, Y_test),
show_metric=True, batch_size=128, run_id='cifar10_net_in_net')
