def bins_vgg16(self, opt=1, loss=0):
initial_model = self.__load_vgg16__()
outputs = []
if (opt == 1):
preds = layers.Flatten()(initial_model.output)
preds.set_shape((None, 25088))
for i in range(c.N_PROPS):
outputs.append(
layers.Dense(c.N_BINS + 1,
activation='softmax',
input_shape=(None, 25088),
trainable=True)(preds))
elif (opt == 2):
for i in range(c.N_PROPS):
preds = Conv2D(30,
5,
strides=(1, 1),
padding='valid',
data_format='channels_last')(
initial_model.output)
preds = Activation('tanh')(preds)
preds = SpatialDropout2D(0.4)(preds)
preds = Flatten()(preds)
preds.set_shape((None, 1470))
outputs.append(preds)
else:
for i in range(c.N_PROPS):
preds = Conv2D(30,
5,
strides=(1, 1),
padding='valid',
data_format='channels_last')(
initial_model.output)
preds = Activation('tanh')(preds)
preds = Conv2D(16,
3,
strides=(1, 1),
padding='valid',
data_format='channels_last')(preds)
preds = SpatialDropout2D(0.4)(preds)
preds = Flatten()(preds)
preds.set_shape((None, 1296))
outputs.append(preds)
model = keras.Model(initial_model.input, outputs)
# Compile the model
if (loss == 1):
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
else:
model.compile(loss=OCC.loss,
optimizer='adam',
metrics=['accuracy', m.bins_mean_squared_error])
return model
def vgg16_v3(self):
initial_model = self.__load_vgg16__()
preds = Conv2D(30,
5,
strides=(1, 1),
padding='valid',
data_format='channels_last')(initial_model.output)
preds = Activation('tanh')(preds)
preds = Conv2D(16,
3,
strides=(1, 1),
padding='valid',
data_format='channels_last')(preds)
preds = SpatialDropout2D(0.4)(preds)
preds = Flatten()(preds)
preds.set_shape((None, 1296))
preds = Dense(10,
activation='sigmoid',
input_shape=(None, 1296),
trainable=True)(preds)
model = keras.Model(initial_model.input, preds)
model.compile(loss='mean_squared_error',
optimizer='adam',
metrics=['accuracy'])
return model
