def createSimpleDNN(self):
print("Createing the NERModel[{}]...".format(self.model_name))
self.buildTrainingData()
# Building convolutional network
net = input_data(shape=[None, self.config.sent_size], name='input')
#net = tflearn.input_data([None, self.config.window*2+1])
net = tflearn.embedding(net,
input_dim=self.vocab.dict_size,
weights_init=self.config.w_initializer,
output_dim=self.config.wv_size)
net = tflearn.fully_connected(net,
200,
activation=self.config.activation_1)
net = tflearn.dropout(net, self.config.drop_prob)
net = tflearn.fully_connected(net,
self.no_classes,
activation='softmax')
# With TFLearn estimators
adam = Adam(learning_rate=self.config.lrate, beta1=0.99)
net = tflearn.regression(
net,
#optimizer = self.config.optimizer,
optimizer=adam,
learning_rate=self.config.lrate,
loss='categorical_crossentropy')
# Define model
self.model = tflearn.DNN(net)
def _nn_creation(first_layer_width, last_layer_width, depth, epsilon=1e-8, learning_rate=0.001, dropout_val=0.99,
stddev_init=0.001, hidden_act='relu', outlayer_act='linear', weight_decay=0.001,
validation_fun=_rmse_valid, cost_fun=_rmse, gpu_mem_prop=1):
"""
Creates a neural network with the given parameters.
:param first_layer_width:
:param last_layer_width:
:param depth:
:param epsilon:
:param learning_rate:
:param dropout_val:
:param stddev_init:
:param hidden_act:
:param outlayer_act:
:param weight_decay:
:param validation_fun:
:param cost_fun:
:param gpu_mem_prop:
:return: created neural network
"""
# Weights initialization
winit = tfl.initializations.truncated_normal(stddev=stddev_init, dtype=tf.float32, seed=None)
# GPU memory utilisation proportion
tfl.init_graph(num_cores=16, gpu_memory_fraction=gpu_mem_prop, soft_placement=True)
# Creating NN input
network = input_data(shape=[None, first_layer_width], name='input')
# Calculating width coef
width_coef = (last_layer_width - first_layer_width) / (depth + 1)
# Creating hidden layers
for i in range(1, depth+1):
# Computing current width
curr_width = math.floor(width_coef * (i+1) + first_layer_width)
# Creating current layer
network = fully_connected(network, curr_width, activation=hidden_act, name='fc' + str(i-1), weights_init=winit,
weight_decay=weight_decay)
print("size : " + str(curr_width))
# Applying dropout
network = dropout(network, dropout_val)
# Adding outlayer
network = fully_connected(network, 1, activation=outlayer_act, name='outlayer', weights_init=winit)
# Adam optimizer creation
adam = Adam(learning_rate=learning_rate, epsilon=epsilon)
# Model evaluation layer creation
network = regression(network, optimizer=adam,
loss=cost_fun, metric=validation_fun, name='target')
return network
def main(argv=None):
print("Reading image dataset...")
train_images = flowers.read_dataset("./Images")
batch_reader = dataset.BatchDatset(train_images)
network = Network()
adam = Adam(learning_rate=3e-3, beta1=0.9, beta2=0.99, epsilon=1.0)
reg = regression(network, optimizer=adam)
l_image, color_images = batch_reader.next_batch(16)
model = tflearn.DNN(reg, checkpoint_path='model.tfl.ckpt')
model.fit(l_image, color_images[:, :, :, 1:3])
model.save("model.tfl")
for itr in xrange(MAX_ITERATION):
model.load("model.tfl")
l_image, color_images = batch_reader.next_batch(16)
model = tflearn.DNN(reg, checkpoint_path='model.tfl.ckpt')
model.fit(l_image, color_images[:, :, :, 1:3], show_metric=False)
model.save("model.tfl")
def cnn(self):
images = input_data(shape=[None, 48, 48, 1], name='input')
images = conv_2d(images, 64, 3, padding='same', activation=self.activation)
images = batch_normalization(images)
#max pooling
images = max_pool_2d(images, 3, strides=2)
images = conv_2d(images, 128, 3, padding='same', activation=self.activation)
images = max_pool_2d(images, 3, strides=2)
images = conv_2d(images, 256, 3, padding='same', activation=self.activation)
#maxpooling
images = max_pool_2d(images, 3, strides=2)
images = dropout(images, keep_prob=self.dropout)
images = fully_connected(images, 4096, activation=self.activation)
images = dropout(images, keep_prob=self.dropout)
#fully connected layers
images = fully_connected(images, 1024, activation=self.activation)
images = fully_connected(images, 7, activation='softmax')
if self.optimizer == 'momentum':
optimizers = Momentum(
learning_rate=self.learning_rate,
momentum=self.optimizer_param,
lr_decay=self.learning_rate_decay,
decay_step=self.decay_step
)
elif self.optimizer == 'adam':
optimizers = Adam(
learning_rate=self.learning_rate,
beta1=self.optimizer_param,
beta2=self.learning_rate_decay,
)
else:
print("Error Optimizer")
network = regression(
images,
optimizer=optimizers,
loss='categorical_crossentropy',
learning_rate=self.learning_rate,
name='output'
)
return network
def conv_nn(self):
convnet = input_data(shape=[None, self.img_size, self.img_size, self.image_channels], name='input')
# TODO given layers are currently x2 the given layer amount
for _ in range(self.layers):
convnet = conv_2d(convnet, 32, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.5)
adam = Adam(learning_rate=self.learning_rate, beta1=self.beta)
convnet = fully_connected(convnet, len(self.label_folders), activation='softmax')
convnet = regression(convnet, optimizer=adam, shuffle_batches=False, learning_rate=self.learning_rate,
loss='categorical_crossentropy', batch_size=self.batch_size,
name='targets')
if not os.path.isdir(self.data_folder + 'checkpoints/' + self.model_name + '/'):
os.mkdir(self.data_folder + 'checkpoints/' + self.model_name + '/')
self.model = tflearn.DNN(convnet, tensorboard_dir='log',
checkpoint_path=self.data_folder + 'checkpoints/' + self.model_name + '/')
def build_DCGAN(self):
gen_input = input_data(shape=[None, self.z_dim], name='input_gen_noise')
input_disc_noise = input_data(shape=[None, self.z_dim], name='input_disc_noise')
input_disc_real = input_data(shape=[None, self.img_size, self.img_size, 1], name='input_disc_real')
disc_fake = self.discriminator(self.generator(input_disc_noise))
disc_real = self.discriminator(input_disc_real, reuse=True)
disc_net = tf.concat([disc_fake, disc_real], axis=0)
gen_net = self.generator(gen_input, reuse=True)
stacked_gan_net = self.discriminator(gen_net, reuse=True)
disc_vars = tflearn.get_layer_variables_by_scope('Discriminator')
disc_target = tflearn.multi_target_data(['target_disc_fake', 'target_disc_real'],
shape=[None, 2])
adam = Adam(learning_rate=self.learning_rate, beta1=self.beta)
disc_model = regression(disc_net, optimizer=adam,
placeholder=disc_target,
loss='categorical_crossentropy',
trainable_vars=disc_vars,
name='target_disc', batch_size=self.batch_size,
op_name='DISC')
gen_vars = tflearn.get_layer_variables_by_scope('Generator')
gan_model = regression(stacked_gan_net, optimizer=adam,
loss='categorical_crossentropy',
trainable_vars=gen_vars,
name='target_gen', batch_size=self.batch_size,
op_name='GEN')
self.model = tflearn.DNN(gan_model, tensorboard_dir='log',
checkpoint_path=self.data_folder + 'checkpoints/' + self.model_name + '/')
self.gen_net = gen_net
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 128, 3, activation='relu')
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 24, activation='softmax')
adam = Adam(learning_rate=0.001)
network = regression(network, optimizer=adam,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network)
model.fit({'input': X}, {'target': Y}, n_epoch=100,
validation_set=({'input': testX}, {'target': testY}),
show_metric=True, run_id='convnet', batch_size=100)
model.save('convnet.tflearn')
def build_modelB(optimizer=HYPERPARAMS.optimizer,
optimizer_param=HYPERPARAMS.optimizer_param,
learning_rate=HYPERPARAMS.learning_rate,
keep_prob=HYPERPARAMS.keep_prob,
learning_rate_decay=HYPERPARAMS.learning_rate_decay,
decay_step=HYPERPARAMS.decay_step):
images_network = input_data(
shape=[None, NETWORK.input_size, NETWORK.input_size, 1], name='input1')
images_network = conv_2d(images_network,
64,
3,
activation=NETWORK.activation)
#images_network = local_response_normalization(images_network)
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 3, strides=2)
images_network = conv_2d(images_network,
128,
3,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 3, strides=2)
images_network = conv_2d(images_network,
256,
3,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 3, strides=2)
images_network = dropout(images_network, keep_prob=keep_prob)
images_network = fully_connected(images_network,
4096,
activation=NETWORK.activation)
images_network = dropout(images_network, keep_prob=keep_prob)
images_network = fully_connected(images_network,
1024,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_fully_connected_layers:
images_network = batch_normalization(images_network)
if NETWORK.use_landmarks or NETWORK.use_hog_and_landmarks:
if NETWORK.use_hog_sliding_window_and_landmarks:
landmarks_network = input_data(shape=[None, 2728], name='input2')
elif NETWORK.use_hog_and_landmarks:
landmarks_network = input_data(shape=[None, 208], name='input2')
else:
landmarks_network = input_data(shape=[None, 68, 2], name='input2')
landmarks_network = fully_connected(landmarks_network,
1024,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_fully_connected_layers:
landmarks_network = batch_normalization(landmarks_network)
landmarks_network = fully_connected(landmarks_network,
128,
activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_fully_connected_layers:
landmarks_network = batch_normalization(landmarks_network)
images_network = fully_connected(images_network,
128,
activation=NETWORK.activation)
network = merge([images_network, landmarks_network], 'concat', axis=1)
else:
network = images_network
network = fully_connected(network,
NETWORK.output_size,
activation='softmax')
if optimizer == 'momentum':
optimizer = Momentum(learning_rate=learning_rate,
momentum=optimizer_param,
lr_decay=learning_rate_decay,
decay_step=decay_step)
elif optimizer == 'adam':
optimizer = Adam(learning_rate=learning_rate,
beta1=optimizer_param,
beta2=learning_rate_decay)
else:
print("Unknown optimizer: {}".format(optimizer))
network = regression(network,
optimizer=optimizer,
loss=NETWORK.loss,
learning_rate=learning_rate,
name='output')
return network
def build_model(optimizer=HYPERPARAMS.optimizer, optimizer_param=HYPERPARAMS.optimizer_param,
learning_rate=HYPERPARAMS.learning_rate, keep_prob=HYPERPARAMS.keep_prob,
learning_rate_decay=HYPERPARAMS.learning_rate_decay, decay_step=HYPERPARAMS.decay_step):
images_input = input_data(shape=[None, NETWORK.input_size, NETWORK.input_size, 1], name='input1')
images_network = conv_2d(images_input, 16, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = conv_2d(images_network, 16, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 2, strides=2) #24*24*16
images_network = conv_2d(images_network, 32, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = conv_2d(images_network, 32, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 2, strides=2) #12*12*32
images_network=tf.pad(images_network,[[0,0],[18,18],[18,18],[0,0]],'CONSTANT')
images_network = merge([images_network, images_input], 'concat', axis=3) #48*48*33
images_network = conv_2d(images_network, 64, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = conv_2d(images_network, 64, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = conv_2d(images_network, 64, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 2, strides=2) #24*24*64
images_network = conv_2d(images_network, 128, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = conv_2d(images_network, 128, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = conv_2d(images_network, 128, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 2, strides=2) #12*12*128
#
images_network = conv_2d(images_network, 128, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = conv_2d(images_network, 128, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = conv_2d(images_network, 128, 3, activation='relu')
if NETWORK.use_batchnorm_after_conv_layers:
images_network = batch_normalization(images_network)
images_network = max_pool_2d(images_network, 2, strides=2) #6*6*128
images_network = fully_connected(images_network, 1024, activation='relu')
images_network = dropout(images_network,keep_prob=keep_prob)
if NETWORK.use_batchnorm_after_fully_connected_layers:
images_network = batch_normalization(images_network)
images_network = fully_connected(images_network, 1024, activation='relu')
images_network = dropout(images_network, keep_prob=keep_prob)
if NETWORK.use_batchnorm_after_fully_connected_layers:
images_network = batch_normalization(images_network)
if NETWORK.use_landmarks or NETWORK.use_hog_and_landmarks:
if NETWORK.use_hog_sliding_window_and_landmarks:
landmarks_network = input_data(shape=[None, 2728], name='input2')
elif NETWORK.use_hog_and_landmarks:
landmarks_network = input_data(shape=[None, 208], name='input2')
else:
landmarks_network = input_data(shape=[None, 68, 2], name='input2')
landmarks_network = fully_connected(landmarks_network, 1024, activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_fully_connected_layers:
landmarks_network = batch_normalization(landmarks_network)
landmarks_network = fully_connected(landmarks_network, 40, activation=NETWORK.activation)
if NETWORK.use_batchnorm_after_fully_connected_layers:
landmarks_network = batch_normalization(landmarks_network)
images_network = fully_connected(images_network, 40, activation=NETWORK.activation)
network = merge([images_network, landmarks_network], 'concat', axis=1)
else:
network = images_network
network = fully_connected(network, NETWORK.output_size, activation='softmax')
if optimizer == 'momentum':
# FIXME base_lr * (1 - iter/max_iter)^0.5, base_lr = 0.01
optimizer = Momentum(learning_rate=learning_rate, momentum=optimizer_param,
lr_decay=learning_rate_decay, decay_step=decay_step)
elif optimizer == 'adam':
optimizer = Adam(learning_rate=learning_rate, beta1=optimizer_param, beta2=learning_rate_decay)
else:
print("Unknown optimizer: {}".format(optimizer))
network = regression(network, optimizer=optimizer, loss=NETWORK.loss, learning_rate=learning_rate, name='output')
return network
#network = dropout(network, 0.25)
#network = conv_3d(network, 128, 3,3, activation='relu')
#network = conv_3d(network, 128, 2,2, activation='relu')
#network = max_pool_3d(network, 2,2)
#network = dropout(network, 0.25)
#network = conv_3d(network, 256, 3,3, activation='relu')
#network = conv_3d(network, 256, 1,1, activation='relu')
#network = max_pool_3d(network, 2,2)
#network = dropout(network, 0.25)
#network = conv_3d(network, 512, 1,1, activation='relu')
#network = conv_3d(network, 128, 2,2, activation='relu')
#network = max_pool_3d(network, 1,1)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.8)
softmax = fully_connected(network, num_classes, activation='softmax')
adam = Adam(learning_rate=0.0001, beta1=0.98, beta2=0.9999)
regression = regression(softmax, optimizer=adam,loss='categorical_crossentropy',learning_rate=0.001)
model = tflearn.DNN(regression,tensorboard_verbose=3,tensorboard_dir='log')
model.fit(X, y1, n_epoch=1,shuffle=True,validation_set=(v1,v2),show_metric=True, batch_size=1, snapshot_step=500)
#score=model.evaluate(np.array(v1),np.array(v2))
#print('Test score:',score[0],'\nTest loss:',score[1])
#print('Test loss:',score[1])
with open('D:/prob.csv','w') as f:
f.write('probability\n')
with open('D:/prob.csv','a') as f:
for data in tqdm(test_data):
img_num = data[1]
img_data = data[0]
data = img_data.reshape(20,50,50,1)
model_out = model.predict([data])[0]
#model_out=model.predict_label([data])[0]
def get_network_architecture(image_width, image_height, number_of_classes, learning_rate):
number_of_channels = 1
network = input_data(
shape=[None, image_width, image_height, number_of_channels],
data_preprocessing=img_prep,
data_augmentation=img_aug,
name='InputData'
)
"""
def conv_2d(incoming, nb_filters, filter_size, strides=1, padding='same',
activation='linear', bias='True', weights_init='uniform_scaling',
bias_init='zeros', regularizer=None, weight_decay=0.001,
trainable=True, restore=True, reuse=False, scope=None,
name='Conv2D')
network = conv_2d(network, 32, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_1')
network = max_pool_2d(network, (2, 2), strides=2, padding='same', name='MaxPool2D_1')
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_1')
network = dropout(network, 0.5, name='Dropout_1')
network = batch_normalization(network, name='BatchNormalization')
network = flatten(network, name='Flatten')
network = fully_connected(network, 512, activation='relu', name='FullyConnected_1')
network = fully_connected(network, number_of_classes, activation='softmax', name='FullyConnected_Final')
print(' {}: {}'.format('Conv2D................', network.shape))
print(' {}: {}'.format('MaxPool2D.............', network.shape))
print(' {}: {}'.format('Dropout...............', network.shape))
print(' {}: {}'.format('BatchNormalization....', network.shape))
print(' {}: {}'.format('Flatten...............', network.shape))
print(' {}: {}'.format('FullyConnected........', network.shape))
print(' {}: {}'.format('FullyConnected_Final..', network.shape))
CONV / FC -> Dropout -> BN -> activation function -> ...
Convolutional filters: { 32, 64, 128 }
Convolutional filter sizes: { 1, 3, 5, 11 }
Convolutional strides: 1
Activation: ReLu
Pooling kernel sizes: { 2, 3, 4, 5 }
Pooling kernel strides: 2
Dropout probability: 0.5
- Higher probability of keeping in earlier stages
- Lower probability of keeping in later stages
"""
print('\nNetwork architecture:')
print(' {}: {}'.format('InputData.............', network.shape))
network = conv_2d(network, 16, (7, 7), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_1')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_1')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 16, (7, 7), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_2')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_2')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_1')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_1')
print(' {}: {}'.format('Dropout...............', network.shape))
network = conv_2d(network, 32, (5, 5), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_3')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_3')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 32, (5, 5), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_4')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_4')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_2')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_2')
print(' {}: {}'.format('Dropout...............', network.shape))
network = conv_2d(network, 64, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_5')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_5')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 64, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_6')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_6')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_3')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_3')
print(' {}: {}'.format('Dropout...............', network.shape))
network = conv_2d(network, 128, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_7')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_7')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 128, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_8')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_8')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_4')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_4')
print(' {}: {}'.format('Dropout...............', network.shape))
network = conv_2d(network, 256, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_9')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_9')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = conv_2d(network, 256, (3, 3), strides=1, padding='same', activation='relu', regularizer='L2', name='Conv2D_10')
print(' {}: {}'.format('Conv2D................', network.shape))
network = batch_normalization(network, name='BatchNormalization_10')
print(' {}: {}'.format('BatchNormalization....', network.shape))
network = avg_pool_2d(network, (2, 2), strides=2, padding='same', name='AvgPool2D_5')
print(' {}: {}'.format('AvgPool2D.............', network.shape))
network = dropout(network, 0.5, name='Dropout_5')
print(' {}: {}'.format('Dropout...............', network.shape))
network = flatten(network, name='Flatten')
print(' {}: {}'.format('Flatten...............', network.shape))
network = fully_connected(network, 512, activation='relu', name='FullyConnected_1')
print(' {}: {}'.format('FullyConnected........', network.shape))
network = dropout(network, 0.5, name='Dropout_6')
print(' {}: {}'.format('Dropout...............', network.shape))
network = fully_connected(network, number_of_classes, activation='softmax', name="FullyConnected_Final")
print(' {}: {}'.format('FullyConnected_Final..', network.shape))
optimizer = Adam(learning_rate=learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')
# optimizer = SGD(learning_rate=learning_rate, lr_decay=0.01, decay_step=100, staircase=False, use_locking=False, name='SGD')
# optimizer = RMSProp(learning_rate=learning_rate, decay=0.9, momentum=0.9, epsilon=1e-10, use_locking=False, name='RMSProp')
# optimizer = Momentum(learning_rate=learning_rate, momentum=0.9, lr_decay=0.01, decay_step=100, staircase=False, use_locking=False, name='Momentum')
metric = Accuracy(name='Accuracy')
# metric = R2(name='Standard Error')
# metric = WeightedR2(name='Weighted Standard Error')
# metric = Top_k(k=6, name='Top K')
network = regression(
network,
optimizer=optimizer,
loss='categorical_crossentropy',
metric=metric,
learning_rate=learning_rate,
name='Regression'
)
return network