tfmri.layers.MaxPooling2D

class MaxPooling2D(*args, **kwargs)[source]

Bases: keras.layers.pooling.max_pooling2d.MaxPooling2D

Max pooling operation for 2D spatial data.

Note

This layer can be used as a drop-in replacement for tf.keras.layers.MaxPooling2D. However, this one also supports complex-valued pooling. Simply pass dtype='complex64' or dtype='complex128' to the layer constructor.

Downsamples the input along its spatial dimensions (height and width) by taking the maximum value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension.

The resulting output, when using the "valid" padding option, has a spatial shape (number of rows or columns) of: output_shape = math.floor((input_shape - pool_size) / strides) + 1 (when input_shape >= pool_size)

The resulting output shape when using the "same" padding option is: output_shape = math.floor((input_shape - 1) / strides) + 1

For example, for strides=(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 strides=(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=(2, 2), padding='valid')
>>> max_pool_2d(x)
<tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
  array([[[[6.],
           [8.]]]], 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)>
Args:
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.

call(inputs)[source]

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state in __init__(), or the build() method that is called automatically before call() executes the first time.

Parameters

  • inputs –

    Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

    arguments, and inputs cannot be provided via the default value of a keyword argument.

    • NumPy array or Python scalar values in inputs get cast as tensors.

    • Keras mask metadata is only collected from inputs.

    • Layers are built (build(input_shape) method) using shape info from inputs only.

    • input_spec compatibility is only checked against inputs.

    • Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

    • The SavedModel input specification is generated using inputs only.

    • Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

  • *args – Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.

  • **kwargs –

    Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

    whether the call is meant for training or inference.

    • mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns

A tensor or list/tuple of tensors.