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  1. flatten = keras.layers.Flatten()(pool1)
  2. dense1 = keras.layers.Dense(1200, activation='relu')(flatten)
  3. output = keras.layers.Dense(1, activation = tf.nn.relu,name="output")(dense1)
  4. model = tf.keras.Model(inputs=input_layer, outputs=output)
  5.  
  6. model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.000002),
  7.                                               loss="mean_squared_error",
  8.                                               metrics=["acc"])
  9.  
  10. metrics = model.fit(dataset_array_trainning, label_array_trainning,
  11.                     batch_size=10,
  12.                     epochs=15,
  13.                     validation_data=(dataset_array_testing, label_array_testing))
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