def xception_model(input_shape=(197, 197, 3),
num_classes=1,
learning_rate=0.0001,
weight_path=None):
base_model = Xception(include_top=False,
weights=None,
input_shape=input_shape)
for i in range(10):
base_model.layers.pop()
base_model.outputs = [base_model.layers[-1].output]
base_model.layers[-1].outbound_nodes = []
model = Sequential()
model.add(BatchNormalization(input_shape=input_shape))
model.add(base_model)
model.add(AveragePooling2D(pool_size=6, strides=2))
model.add(Flatten())
model.add(HadamardClassifier(output_dim=num_classes))
model.add(Activation('softmax'))
if weight_path:
if os.path.isfile(weight_path):
model.load_weights(weight_path)
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(lr=learning_rate),
metrics=['accuracy'])
return model
def Xception_model():
input_tensor = Input(shape=(dimension, dimension, number_of_channels))
model = Xception(input_tensor=input_tensor,
weights='imagenet',
include_top=True)
model.layers.pop()
model.outputs = [model.layers[-1].output]
model.layers[-1].outbound_nodes = []
x = Dense(number_of_classes, activation='softmax')(model.output)
model = Model(model.input, x)
# the first 24 layers are not trained
for layer in model.layers[:24]:
layer.trainable = False
return model
def Xception_model():
input_tensor = Input(shape=(dimension, dimension, number_of_channels))
model = Xception(input_tensor=input_tensor,
weights='imagenet',
include_top=True)
model.layers.pop()
model.outputs = [model.layers[-1].output]
model.layers[-1].outbound_nodes = []
x = Dense(number_of_classes, activation='softmax')(model.output)
model = Model(model.input, x)
# the first 24 layers are not trained
for layer in model.layers[:24]:
layer.trainable = False
lrate = 0.001
decay = 0.000001
adam = Adam(lr=lrate, decay=decay)
model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=['accuracy'])
print(model.summary())
return model
