x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) predictions = Dense(1, activation='sigmoid')(x)
# Freeze base layers for layer in base_model.layers: layer.trainable = False crax rat
# Building the model base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) x = base_model
model = Model(inputs=base_model.input, outputs=predictions) activation='relu')(x) predictions = Dense(1