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Advanced Convolutional Neural Networks
Now, we can use GlobalAveragePooling2D() to average over the spatial
5×5 spatial locations and obtain a size of (32, 1280):
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
feature_batch_average = global_average_layer(feature_batch)
print(feature_batch_average.shape)
The last layer is dense with linear activation: if the prediction is positive, the class
is 1; if the prediction is negative, the class is 0:
prediction_layer = tf.keras.layers.Dense(1)
prediction_batch = prediction_layer(feature_batch_average)
print(prediction_batch.shape)
Our model is ready to be composed by combining the base_model (pretrained
MobileNetv2) with global_average_layer to get the correct shape output given as
an input to the final prediction_layer:
model = tf.keras.Sequential([
base_model,
global_average_layer,
prediction_layer
])
Now let's compile the model with an RMSProp() optimizer:
base_learning_rate = 0.0001
model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_
rate), loss='binary_crossentropy',
metrics=['accuracy'])
If we display the composed model, we notice that there are more than 2 million
frozen parameters, and only about 1,000 trainable parameters:
Figure 18: Displaying the composed model; notice that we only have 1,281 trainable parameters
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