seti18.py

INPUT_SHAPE = (192, 192, 3)
# load pre-trained resnet model
resnet = keras.applications.resnet50.ResNet50(include_top=False, weights='imagenet', 
                                              input_shape=INPUT_SHAPE)

# set all layers to be trainable 
resnet.trainable = True
for layer in resnet.layers:
    resnet.trainable = True

# add dense and output layers    
base_resnet = resnet
base_out = base_resnet.output
pool_out = keras.layers.Flatten()(base_out)
hidden1 = keras.layers.Dense(1024, activation='relu')(pool_out)
drop1 = keras.layers.Dropout(rate=0.2)(hidden1)
hidden2 = keras.layers.Dense(512, activation='relu')(drop1)
drop2 = keras.layers.Dropout(rate=0.2)(hidden2)

out = keras.layers.Dense(7, activation='softmax')(drop2)

model = keras.Model(inputs=base_resnet.input, outputs=out)
model.compile(optimizer=keras.optimizers.RMSprop(lr=1e-6),
                loss='categorical_crossentropy',
                metrics=[categorical_accuracy])
model.summary()

# Output
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_3 (InputLayer)            (None, 192, 192, 3)  0                                            
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D)       (None, 198, 198, 3)  0           input_3[0][0]                    
__________________________________________________________________________________________________
conv1 (Conv2D)                  (None, 96, 96, 64)   9472        conv1_pad[0][0]                  
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization)   (None, 96, 96, 64)   256         conv1[0][0]                      
__________________________________________________________________________________________________
...
...
__________________________________________________________________________________________________
dense_8 (Dense)                 (None, 512)          524800      dropout_5[0][0]                  
__________________________________________________________________________________________________
dropout_6 (Dropout)             (None, 512)          0           dense_8[0][0]                    
__________________________________________________________________________________________________
dense_9 (Dense)                 (None, 7)            3591        dropout_6[0][0]                  
==================================================================================================
Total params: 99,614,599
Trainable params: 99,561,479
Non-trainable params: 53,120
__________________________________________________________________________________________________