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- def build_model():
- model = keras.Sequential([
- keras.layers.Conv2D(16, kernel_size=(2, 2), strides=(1,1), activation=tf.nn.relu6,
- input_shape=input_shape),
- keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
- keras.layers.Conv2D(128, (2, 2), activation=tf.nn.relu6),
- keras.layers.MaxPooling2D(pool_size=(2, 2)),
- keras.layers.Flatten(),
- keras.layers.Dense(1000, activation=tf.nn.relu6),
- keras.layers.Dense(1,activation=tf.nn.relu)
- ])
- model.compile(loss=keras.losses.kullback_leibler_divergence,
- optimizer=tf.train.AdamOptimizer(0.001, beta1=0.9, beta2=0.99, epsilon=1e-8),
- metrics=['accuracy'])
- return model
- model = build_model()
- model.summary()
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