message: remove conv2d() layers
diff: @@ -373,24 +373,21 @@ class MaxPooling2D(Pooling2D):
  Usage Example:
  - >>> input_image = tf.constant([[[[1.], [1.], [2.], [4.], [2.], [4.], [2.]],
  - ... [[2.], [2.], [3.], [2.], [2.], [1.], [2.]],
  - ... [[4.], [1.], [1.], [1.], [1.], [2.], [2.]],
  - ... [[2.], [2.], [1.], [4.], [2.], [3.], [4.]],
  - ... [[1.], [4.], [1.], [1.], [2.], [3.], [2.]],
  - ... [[1.], [4.], [2.], [3.], [1.], [2.], [3.]],
  - ... [[3.], [4.], [1.], [2.], [3.], [1.], [4.]]]])
  + >>> input_image = tf.constant([[[[1.], [1.], [2.], [4.]],
  + ... [[2.], [2.], [3.], [2.]],
  + ... [[4.], [1.], [1.], [1.]],
  + ... [[2.], [2.], [1.], [4.]]]])
  >>> output = tf.constant([[[[1], [0]],
  ... [[0], [1]]]])
  >>> model = tf.keras.models.Sequential()
  - >>> model.add(tf.keras.layers.Conv2D(1, kernel_size=(3, 3),
  - ... input_shape=(7,7,1)))
  - >>> model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
  + >>> model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
  + ... input_shape=(4,4,1)))
  >>> model.compile('adam', 'mean_squared_error')
  - >>> model.fit(input_image, output, steps_per_epoch=1,
  - ... shuffle=False, verbose=0)
  - >>> model.predict(input_image, steps=1).shape
  - (1, 2, 2, 1)
  + >>> model.predict(input_image, steps=1)
  + array([[[[2.],
  + [4.]],
  + [[4.],
  + [4.]]]], dtype=float32)
  For example, for stride=(1,1) and padding="same":
  