def build_xception(self):
"""
Load pre-trained Xception from keras applications
"""
# Input image to extract features from
img = Input(shape=(self.img_rows, self.img_cols, 3))
# Get the vgg network from Keras applications
xception = Xception(weights="imagenet", include_top=False)
# Output the first three pooling layers
xception.outputs = [
xception.layers[i].output for i in self.xception_layers
]
# Create model and compile
model = Model(inputs=img, outputs=xception(img))
model.trainable = False
model.compile(loss='mse', optimizer='adam')
return model
cooldown=0,
min_lr=0)
save_model = ModelCheckpoint('xception{epoch:02d}-{val_ctg_out_1_acc:.2f}.h5',
period=2)
if os.path.exists('dog_xception.h5'):
model = load_model('dog_xception.h5')
else:
# create the base pre-trained model
input_tensor = Input(shape=(299, 299, 3))
base_model = Xception(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None)
plot_model(base_model, to_file='xception_model.png')
base_model.layers.pop()
base_model.outputs = [base_model.layers[-1].output]
base_model.layers[-1].outbound_nodes = []
base_model.output_layers = [base_model.layers[-1]]
feature = base_model
img1 = Input(shape=(299, 299, 3), name='img_1')
img2 = Input(shape=(299, 299, 3), name='img_2')
feature1 = feature(img1)
feature2 = feature(img2)
# let's add a fully-connected layer
category_predict1 = Dense(100, activation='softmax',
name='ctg_out_1')(Dropout(0.5)(feature1))
category_predict2 = Dense(100, activation='softmax',
name='ctg_out_2')(Dropout(0.5)(feature2))
