def _new_model(self):
inputs = layers.Input(shape=self.input_size)
conv1 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(inputs)
conv1 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(conv1)
pool1 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(pool1)
conv2 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conv2)
pool2 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(pool2)
conv3 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conv3)
pool3 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv3)
conv4 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(pool3)
conv4 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(conv4)
up5 = layers.Conv3DTranspose(64, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv4)
conc5 = layers.concatenate([up5, conv3])
conv5 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conc6)
conv5 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conv5)
up6 = layers.Conv3DTranspose(32, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv5)
conc6 = layers.concatenate([up6, conv2])
conv6 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conc6)
conv6 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conv6)
up7 = layers.Conv3DTranspose(16, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv6)
conc7 = layers.concatenate([up7, conv1])
conv7 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(conc7)
conv7 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(conv7)
outputs = layers.Conv3D(1, (1, 1, 1), activation='sigmoid')(conv7)
self.model = Model(inputs=inputs, outputs=outputs)
def build_cnn(optimizer='adam', lr=0.00002):
"""Main class for setting up a CNN. Returns the compiled model."""
importlib.reload(config)
C = config.Config()
proj = layers.Input(C.proj_dims)
#x = layers.Permute((2,1,3))(img)
x = layers.Reshape((C.proj_dims[0], -1))(proj)
x = layers.Dense(1024, activation='tanh')(
x) #, kernel_regularizer=regularizers.l1(0.01)
x = layers.BatchNormalization()(x)
#x = layers.Reshape((C.proj_dims[0],32,-1))(x)
#x = layers.Conv2D(128, 3, activation='relu', padding='same')(x)
#x = layers.Reshape((C.proj_dims[0],-1))(x)
x = layers.Dense(1024, activation='tanh')(x)
x = layers.BatchNormalization()(x)
x = layers.Reshape((C.proj_dims[0], 32, 32, -1))(x)
x = layers.Conv3D(64, 3, activation='relu', padding='same')(x)
#x = layers.UpSampling3D((1,2,2))(x)
x = layers.MaxPooling3D((2, 1, 1))(x)
x = layers.Conv3D(64, 3, activation='relu', padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Conv3DTranspose(1, 3, activation='sigmoid', padding='same')(x)
img = layers.Reshape(C.world_dims)(x)
#x = layers.Lambda(norm)(x)
#x = layers.Permute((2,1,3))(x)
#x = layers.Conv2D(64, (2,2), activation='relu', padding='same')(x)
#x = layers.Conv2D(64, (2,2), padding='same')(x)
model = Model(proj, img)
model.compile(optimizer=RMSprop(lr=lr, decay=0.1), loss='mse')
if False:
x = layers.Reshape((C.proj_dims[0], -1))(proj)
x = layers.Dense(1024, activation='tanh')(x)
x = layers.BatchNormalization()(x)
x = layers.Dense(1024, activation='tanh')(x)
x = layers.BatchNormalization()(x)
x = layers.Reshape((C.proj_dims[0], 32, 32, -1))(x)
x = layers.Conv3D(64, (3, 3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling3D((1, 2, 2))(x)
x = layers.Conv3D(64, (3, 3, 3), activation='relu', padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Conv3DTranspose(1, (1, 3, 3),
activation='sigmoid',
padding='same')(x)
return model
def SkipDeconvBlock(x, skip_x, channels):
out = layers.Conv3DTranspose(channels // 2, (2, 2, 2),
padding='valid', strides=2)(x)
out = layers.LeakyReLU()(out)
cat = layers.Concatenate(axis=-1)([out, skip_x])
out = layers.Conv3D(channels, (3, 3, 3), padding='same', strides=1)(cat)
out = layers.LeakyReLU()(out)
out = layers.Add()([out, cat])
return out
def CNN():
num_channels = 1
num_mask_channels = 1
img_shape = (None, None, None, 1)
inputs = Input(shape = img_shape)
conv1 = layers.Conv3D(32, 3, padding='same')(inputs)
conv1 = layers.BatchNormalization()(conv1)
conv1 = Activation('relu')(conv1)
conv1 = layers.Conv3D(32, 3, padding='same')(conv1)
conv1 = layers.BatchNormalization()(conv1)
conv1 = Activation('relu')(conv1)
pool1 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = layers.Conv3D(64, 3, padding='same')(pool1)
conv2 = layers.BatchNormalization()(conv2)
conv2 = Activation('relu')(conv2)
conv2 = layers.Conv3D(64, 3, padding='same')(conv2)
conv2 = layers.BatchNormalization()(conv2)
conv2 = Activation('relu')(conv2)
pool2 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = layers.Conv3D(128, 3, padding='same')(pool2)
conv3 = layers.BatchNormalization()(conv3)
conv3 = Activation('relu')(conv3)
conv3 = layers.Conv3D(128, 3, padding='same')(conv3)
conv3 = layers.BatchNormalization()(conv3)
conv3 = Activation('relu')(conv3)
conv3 = layers.UpSampling3D(size=(2, 2, 2))(conv3)
up4 = layers.concatenate([conv3, conv2])
conv4 = layers.Conv3DTranspose(64, 3, padding='same')(up4)
conv4 = layers.BatchNormalization()(conv4)
conv4 = Activation('relu')(conv4)
conv4 = layers.Conv3DTranspose(64, 3, padding='same')(conv4)
conv4 = layers.BatchNormalization()(conv4)##conv ou crop
conv4 = Activation('relu')(conv4)
conv4 = layers.Conv3DTranspose(64, 1, padding='same')(conv4)
conv4 = layers.UpSampling3D(size=(2, 2, 2))(conv4)
up5 = layers.concatenate([conv4, conv1])
conv5 = layers.Conv3DTranspose(32, 3, padding='same')(up5)
conv5 = layers.BatchNormalization()(conv5)
conv5 = Activation('relu')(conv5)
conv5 = layers.Conv3DTranspose(32, 3, padding='same')(conv5)
conv5 = layers.BatchNormalization()(conv5)##conv ou crop
conv5 = Activation('relu')(conv5)
conv5 = layers.Conv3DTranspose(1, 1, padding='same', activation='relu')(conv5)
model = Model(inputs=inputs, outputs=conv5)
#model.summary()
return(model)
def generate_model():
init = keras.initializers.glorot_uniform()
training = K.variable(True, name="Training")
input_ = layers.Input(shape=(SIZE, SIZE, SIZE, 1),
dtype="float32",
name="img")
# input_ = layers.InputLayer(input_tensor=input_)
out = layers.Conv3D(
filters=8,
kernel_size=5,
activation="relu",
kernel_initializer=init,
padding="same",
)(input_)
out = layers.Conv3D(
filters=8,
kernel_size=5,
activation="relu",
kernel_initializer=init,
padding="same",
)(out)
conv1 = out
out = layers.MaxPooling3D(pool_size=2, strides=2)(out)
out = layers.Dropout(rate=0.3)(out)
out = layers.Conv3D(
filters=SIZE // 8,
kernel_size=5,
activation="relu",
kernel_initializer=init,
padding="same",
)(out)
out = layers.Conv3D(
filters=SIZE // 8,
kernel_size=5,
activation="relu",
kernel_initializer=init,
padding="same",
)(out)
conv2 = out
out = layers.MaxPooling3D(pool_size=2, strides=2)(out)
out = layers.Dropout(rate=0.3)(out)
out = layers.Conv3D(
filters=SIZE // 4,
kernel_size=5,
activation="relu",
kernel_initializer=init,
padding="same",
)(out)
out = layers.Conv3D(
filters=SIZE // 4,
kernel_size=5,
activation="relu",
kernel_initializer=init,
padding="same",
)(out)
conv3 = out
out = layers.MaxPooling3D(pool_size=2, strides=2)(out)
out = layers.Dropout(rate=0.3)(out)
out = layers.Conv3DTranspose(
filters=SIZE // 4,
kernel_size=5,
strides=2,
kernel_initializer=init,
padding="same",
use_bias=False,
)(out)
out = layers.concatenate([out, conv3], axis=-1)
out = layers.Conv3D(
filters=SIZE // 4,
kernel_size=5,
activation="relu",
kernel_initializer=init,
padding="same",
)(out)
out = layers.Dropout(rate=0.3)(out)
out = layers.Conv3DTranspose(
filters=SIZE // 8,
kernel_size=5,
strides=2,
kernel_initializer=init,
padding="same",
use_bias=False,
)(out)
out = layers.concatenate([out, conv2], axis=-1)
out = layers.Conv3D(
filters=SIZE // 8,
kernel_size=5,
activation="relu",
kernel_initializer=init,
padding="same",
)(out)
out = layers.Dropout(rate=0.3)(out)
out = layers.Conv3DTranspose(
filters=8,
kernel_size=5,
strides=2,
kernel_initializer=init,
padding="same",
use_bias=False,
)(out)
out = layers.concatenate([out, conv1], axis=-1)
out = layers.Conv3D(
filters=8,
kernel_size=5,
activation="relu",
kernel_initializer=init,
padding="same",
)(out)
out = layers.Dropout(rate=0.3)(out)
out = layers.Conv3D(filters=1,
kernel_size=1,
kernel_initializer=init,
padding="same")(out)
# out = layers.Activation("sigmoid")(out)
out = layers.Dense(1, activation="sigmoid")(out)
model = keras.models.Model(input_, out)
model.compile("adam", loss="binary_crossentropy", metrics=['accuracy'])
return model
def _new_model(self):
inputs = layers.Input(shape=self.input_size)
conv1 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(inputs)
conv1 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(conv1)
pool1 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(pool1)
conv2 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conv2)
pool2 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(pool2)
conv3 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conv3)
pool3 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv3)
conv4 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(pool3)
conv4 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(conv4)
pool4 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv4)
conv5 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(pool4)
conv5 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(conv5)
pool5 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv5)
conv6 = layers.Conv3D(512, (3, 3, 3),
activation='relu',
padding='same')(pool5)
conv6 = layers.Conv3D(512, (3, 3, 3),
activation='relu',
padding='same')(conv6)
up7 = layers.Conv3DTranspose(256, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv6)
conc7 = layers.concatenate([up7, conv5])
conv7 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(conc7)
conv7 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(conv7)
up8 = layers.Conv3DTranspose(128, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv7)
conc8 = layers.concatenate([up8, conv4])
conv8 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(conc8)
conv8 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(conv8)
up9 = layers.Conv3DTranspose(64, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv8)
conc9 = layers.concatenate([up9, conv3])
conv9 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conc9)
conv9 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conv9)
up10 = layers.Conv3DTranspose(32, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv9)
conc10 = layers.concatenate([up10, conv2])
conv10 = layers.Conv3D(32, (3, 3, 3),
activation='relu',
padding='same')(conc10)
conv10 = layers.Conv3D(32, (3, 3, 3),
activation='relu',
padding='same')(conv10)
up11 = layers.Conv3DTranspose(16, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv10)
conc11 = layers.concatenate([up11, conv1])
conv11 = layers.Conv3D(16, (3, 3, 3),
activation='relu',
padding='same')(conc11)
conv11 = layers.Conv3D(16, (3, 3, 3),
activation='relu',
padding='same')(conv11)
outputs = layers.Conv3D(1, (1, 1, 1), activation='sigmoid')(conv11)
self.model = Model(inputs=inputs, outputs=outputs)
def _new_model(self):
inputs = layers.Input(shape=self.input_size)
conv1 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(inputs)
conv1 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conv1)
pool1 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(pool1)
conv2 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conv2)
pool2 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(pool2)
conv3 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(conv3)
pool3 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv3)
conv4 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(pool3)
conv4 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(conv4)
pool4 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv4)
conv5 = layers.Conv3D(512, (3, 3, 3),
activation='relu',
padding='same')(pool4)
conv5 = layers.Conv3D(512, (3, 3, 3),
activation='relu',
padding='same')(conv5)
up6 = layers.Conv3DTranspose(256, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv5)
conc6 = layers.concatenate([up6, conv4])
conv6 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(conc6)
conv6 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(conv6)
up7 = layers.Conv3DTranspose(128, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv6)
conc7 = layers.concatenate([up7, conv3])
conv7 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(conc7)
conv7 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(conv7)
up8 = layers.Conv3DTranspose(64, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv7)
conc8 = layers.concatenate([up8, conv2])
conv8 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conc8)
conv8 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conv8)
up9 = layers.Conv3DTranspose(32, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv8)
conc9 = layers.concatenate([up9, conv1])
conv9 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conc9)
conv9 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conv9)
outputs = layers.Conv3D(1, (1, 1, 1),
activation='sigmoid',
name='outputs')(conv9)
ae_up6 = layers.Conv3DTranspose(256, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv5)
ae_conv6 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(ae_up6)
ae_conv6 = layers.Conv3D(256, (3, 3, 3),
activation='relu',
padding='same')(ae_conv6)
ae_up7 = layers.Conv3DTranspose(128, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(ae_conv6)
ae_conv7 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(ae_up7)
ae_conv7 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(ae_conv7)
ae_up8 = layers.Conv3DTranspose(64, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(ae_conv7)
ae_conv8 = layers.Conv3D(64, (3, 3, 3),
activation='relu',
padding='same')(ae_up8)
ae_conv8 = layers.Conv3D(64, (3, 3, 3),
activation='relu',
padding='same')(ae_conv8)
ae_up9 = layers.Conv3DTranspose(32, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(ae_conv8)
ae_conv9 = layers.Conv3D(32, (3, 3, 3),
activation='relu',
padding='same')(ae_up9)
ae_conv9 = layers.Conv3D(32, (3, 3, 3),
activation='relu',
padding='same')(ae_conv9)
ae_outputs = layers.Conv3D(1, (1, 1, 1),
activation='sigmoid',
name='ae_outputs')(ae_conv9)
self.model = Model(inputs=inputs, outputs=[outputs, ae_outputs])
def Conv_VAE3D(n_epochs=2,
batch_size=10,
learning_rate=0.001,
decay_rate=0.0,
latent_dim=8,
name='stats.pickle'):
# Prepare session:
K.clear_session()
# Number of samples to use for training and validation:
n_train = 1500
n_val = 1000
# ENCODER: ---------------------------------------------------------------
input_img = Input(shape=(50, 50, 50, 4), name="Init_Input")
x = layers.Conv3D(32, (3, 3, 3),
padding="same",
activation='relu',
name='E_Conv1')(input_img)
x = layers.MaxPooling3D((2, 2, 2), name='E_MP1')(x)
x = layers.Conv3D(64, (3, 3, 3),
padding="same",
activation='relu',
name='E_Conv2')(x)
x = layers.MaxPooling3D((2, 2, 2), name='E_MP2')(x)
x = layers.Conv3D(64, (3, 3, 3),
padding="valid",
activation='relu',
name='E_Conv3')(x)
x = layers.MaxPooling3D((2, 2, 2), name='E_MP3')(x)
x = layers.Conv3D(128, (3, 3, 3),
padding="same",
activation='relu',
name='E_Conv4')(x)
shape_before_flattening = K.int_shape(x)
x = layers.Flatten()(x)
x = layers.Dense(32, activation='relu')(x)
encoder = Model(input_img, x)
print(encoder.summary())
# VARIATIONAL LAYER: ------------------------------------------------------
z_mean = layers.Dense(latent_dim, name='V_Mean')(x)
z_log_var = layers.Dense(latent_dim, name='V_Sig')(x)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim),
mean=0.,
stddev=1.)
return z_mean + K.exp(z_log_var) * epsilon
z = layers.Lambda(sampling, name='V_Var')([z_mean, z_log_var])
variation = Model(input_img, z)
print(variation.summary())
# DECODER: ---------------------------------------------------------------
decoder_input = layers.Input(shape=(latent_dim, ), name='D_Input')
x = layers.Dense(np.prod(shape_before_flattening[1:]),
activation='relu',
name='D_Dense')(decoder_input)
x = layers.Reshape(shape_before_flattening[1:], name='D_UnFlatten')(x)
x = layers.Conv3DTranspose(32,
3,
padding='same',
activation='relu',
name='D_DeConv1')(x)
x = layers.UpSampling3D((2, 2, 2))(x)
x = layers.Conv3D(4,
3,
padding='same',
activation='sigmoid',
name='D_Conv1')(x)
x = layers.UpSampling3D((5, 5, 5))(x)
x = layers.Conv3D(4,
3,
padding='same',
activation='sigmoid',
name='D_Conv2')(x)
decoder = Model(decoder_input, x)
print(decoder.summary())
# CALLBACKS: --------------------------------------------------------------
class TimeHistory(keras.callbacks.Callback):
start = []
end = []
times = []
def on_epoch_begin(self, batch, logs=None):
self.start = time.time()
def on_epoch_end(self, batch, logs=None):
self.end = time.time()
self.times.append(self.end - self.start)
# CUSTOM LAYERS: ----------------------------------------------------------
class CustomVariationalLayer(keras.layers.Layer):
def vae_loss(self, x, z_decoded):
x = K.flatten(x)
z_decoded = K.flatten(z_decoded)
xent_loss = keras.metrics.binary_crossentropy(x, z_decoded)
kl_loss = -5e-4 * K.mean(
1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return K.mean(xent_loss + kl_loss) #xent_loss) # + kl_loss)
def call(self, inputs):
x = inputs[0]
z_decoded = inputs[1]
loss = self.vae_loss(x, z_decoded)
self.add_loss(loss, inputs=inputs)
return x
# DEFINE FINAL MODEL: ----------------------------------------------------
z_encoded = variation(input_img)
z_decoded = decoder(z_encoded)
# Construct Final Model:
y = CustomVariationalLayer()([input_img, z_decoded])
vae = Model(input_img, y)
print(vae.summary())
# Define Optimizer:
vae_optimizer = keras.optimizers.Adam(lr=learning_rate,
beta_1=0.9,
beta_2=0.999,
decay=decay_rate,
amsgrad=False)
vae.compile(optimizer=vae_optimizer,
loss=None) # Not using custom vae loss function defined above
# Define time callback:
time_callback = TimeHistory()
steps = n_train // batch_size
val_steps = n_val // batch_size
# FIT MODEL: --------------------------------------------------------------
history = vae.fit_generator(
gen_batches(batch_size),
shuffle=True,
epochs=n_epochs,
steps_per_epoch=steps,
callbacks=[time_callback],
validation_data=gen_batches_validation(batch_size),
validation_steps=val_steps)
# OUTPUTS: -------------------------------------------------------------
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
times = time_callback.times
data = {
'train_loss': loss_values,
'val_loss': val_loss_values,
'epoch_time': times
}
pickle_out = open(name, "wb")
pickle.dump(data, pickle_out)
pickle_out.close()
K.clear_session()
return (history_dict)