tf.keras.layers.MaxPool2D     View source on GitHub    Max pooling operation for 2D spatial data. Inherits From: Layer, Module  View aliases  Main aliases 
tf.keras.layers.MaxPooling2D Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.layers.MaxPool2D, tf.compat.v1.keras.layers.MaxPooling2D  
tf.keras.layers.MaxPool2D(
    pool_size=(2, 2), strides=None, padding='valid', data_format=None,
    **kwargs
)
 Downsamples the input representation by taking the maximum value over the window defined by pool_size for each dimension along the features axis. The window is shifted by strides in each dimension. The resulting output when using "valid" padding option has a shape(number of rows or columns) of: output_shape = (input_shape - pool_size + 1) / strides) The resulting output shape when using the "same" padding option is: output_shape = input_shape / strides For example, for stride=(1,1) and padding="valid": 
x = tf.constant([[1., 2., 3.],
                 [4., 5., 6.],
                 [7., 8., 9.]])
x = tf.reshape(x, [1, 3, 3, 1])
max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
   strides=(1, 1), padding='valid')
max_pool_2d(x)
<tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
  array([[[[5.],
           [6.]],
          [[8.],
           [9.]]]], dtype=float32)>
 For example, for stride=(2,2) and padding="valid": 
x = tf.constant([[1., 2., 3., 4.],
                 [5., 6., 7., 8.],
                 [9., 10., 11., 12.]])
x = tf.reshape(x, [1, 3, 4, 1])
max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
   strides=(1, 1), padding='valid')
max_pool_2d(x)
<tf.Tensor: shape=(1, 2, 3, 1), dtype=float32, numpy=
  array([[[[ 6.],
           [ 7.],
           [ 8.]],
          [[10.],
           [11.],
           [12.]]]], dtype=float32)>
 Usage Example: 
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.MaxPooling2D(pool_size=(2, 2),
   input_shape=(4,4,1)))
model.compile('adam', 'mean_squared_error')
model.predict(input_image, steps=1)
array([[[[2.],
         [4.]],
        [[4.],
         [4.]]]], dtype=float32)
 For example, for stride=(1,1) and padding="same": 
x = tf.constant([[1., 2., 3.],
                 [4., 5., 6.],
                 [7., 8., 9.]])
x = tf.reshape(x, [1, 3, 3, 1])
max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
   strides=(1, 1), padding='same')
max_pool_2d(x)
<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
  array([[[[5.],
           [6.],
           [6.]],
          [[8.],
           [9.],
           [9.]],
          [[8.],
           [9.],
           [9.]]]], dtype=float32)>

 


 Arguments
  pool_size   integer or tuple of 2 integers, window size over which to take the maximum. (2, 2) will take the max value over a 2x2 pooling window. If only one integer is specified, the same window length will be used for both dimensions.  
  strides   Integer, tuple of 2 integers, or None. Strides values. Specifies how far the pooling window moves for each pooling step. If None, it will default to pool_size.  
  padding   One of "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.  
  data_format   A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".    Input shape:  If data_format='channels_last': 4D tensor with shape (batch_size, rows, cols, channels). If data_format='channels_first': 4D tensor with shape (batch_size, channels, rows, cols).  Output shape:  If data_format='channels_last': 4D tensor with shape (batch_size, pooled_rows, pooled_cols, channels). If data_format='channels_first': 4D tensor with shape (batch_size, channels, pooled_rows, pooled_cols). 
 


 Returns   A tensor of rank 4 representing the maximum pooled values. See above for output shape.