[source]

### MaxPooling1D

    keras.layers.MaxPooling1D(pool_size=2, strides=None, padding='valid', data_format='channels_last')

Max pooling operation for temporal data.

Arguments

- pool_size: Integer, size of the max pooling windows.
- strides: Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to `pool_size`.
- padding: One of `"valid"` or `"same"` (case-insensitive).
- 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, steps, features)` while `channels_first` corresponds to inputs with shape `(batch, features, steps)`.

Input shape

- If `data_format='channels_last'`: 3D tensor with shape: `(batch_size, steps, features)`
- If `data_format='channels_first'`: 3D tensor with shape: `(batch_size, features, steps)`

Output shape

- If `data_format='channels_last'`: 3D tensor with shape: `(batch_size, downsampled_steps, features)`
- If `data_format='channels_first'`: 3D tensor with shape: `(batch_size, features, downsampled_steps)`

[source]

### MaxPooling2D

    keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None)

Max pooling operation for spatial data.

Arguments

- pool_size: integer or tuple of 2 integers, factors by which to downscale (vertical, horizontal). (2, 2) will halve the input in both spatial dimension. 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. If None, it will default to `pool_size`.
- padding: One of `"valid"` or `"same"` (case-insensitive).
- 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)`

[source]

### MaxPooling3D

    keras.layers.MaxPooling3D(pool_size=(2, 2, 2), strides=None, padding='valid', data_format=None)

Max pooling operation for 3D data (spatial or spatio-temporal).

Arguments

- pool_size: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). (2, 2, 2) will halve the size of the 3D input in each dimension.
- strides: tuple of 3 integers, or None. Strides values.
- padding: One of `"valid"` or `"same"` (case-insensitive).
- 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. 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'`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
- If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`

Output shape

- If `data_format='channels_last'`: 5D tensor with shape: `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`
- If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)`

[source]

### AveragePooling1D

    keras.layers.AveragePooling1D(pool_size=2, strides=None, padding='valid', data_format='channels_last')

Average pooling for temporal data.

Arguments

- pool_size: Integer, size of the average pooling windows.
- strides: Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to `pool_size`.
- padding: One of `"valid"` or `"same"` (case-insensitive).
- 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, steps, features)` while `channels_first` corresponds to inputs with shape `(batch, features, steps)`.

Input shape

- If `data_format='channels_last'`: 3D tensor with shape: `(batch_size, steps, features)`
- If `data_format='channels_first'`: 3D tensor with shape: `(batch_size, features, steps)`

Output shape

- If `data_format='channels_last'`: 3D tensor with shape: `(batch_size, downsampled_steps, features)`
- If `data_format='channels_first'`: 3D tensor with shape: `(batch_size, features, downsampled_steps)`

[source]

### AveragePooling2D

    keras.layers.AveragePooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None)

Average pooling operation for spatial data.

Arguments

- pool_size: integer or tuple of 2 integers, factors by which to downscale (vertical, horizontal). (2, 2) will halve the input in both spatial dimension. 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. If None, it will default to `pool_size`.
- padding: One of `"valid"` or `"same"` (case-insensitive).
- 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)`

[source]

### AveragePooling3D

    keras.layers.AveragePooling3D(pool_size=(2, 2, 2), strides=None, padding='valid', data_format=None)

Average pooling operation for 3D data (spatial or spatio-temporal).

Arguments

- pool_size: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). (2, 2, 2) will halve the size of the 3D input in each dimension.
- strides: tuple of 3 integers, or None. Strides values.
- padding: One of `"valid"` or `"same"` (case-insensitive).
- 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. 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'`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
- If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`

Output shape

- If `data_format='channels_last'`: 5D tensor with shape: `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`
- If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)`

[source]

### GlobalMaxPooling1D

    keras.layers.GlobalMaxPooling1D(data_format='channels_last')

Global max pooling operation for temporal data.

Arguments

- 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, steps, features)` while `channels_first` corresponds to inputs with shape `(batch, features, steps)`.

Input shape

- If `data_format='channels_last'`: 3D tensor with shape: `(batch_size, steps, features)`
- If `data_format='channels_first'`: 3D tensor with shape: `(batch_size, features, steps)`

Output shape

2D tensor with shape: `(batch_size, features)`

[source]

### GlobalAveragePooling1D

    keras.layers.GlobalAveragePooling1D(data_format='channels_last')

Global average pooling operation for temporal data.

Arguments

- 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, steps, features)` while `channels_first` corresponds to inputs with shape `(batch, features, steps)`.

Input shape

- If `data_format='channels_last'`: 3D tensor with shape: `(batch_size, steps, features)`
- If `data_format='channels_first'`: 3D tensor with shape: `(batch_size, features, steps)`

Output shape

2D tensor with shape: `(batch_size, features)`

[source]

### GlobalMaxPooling2D

    keras.layers.GlobalMaxPooling2D(data_format=None)

Global max pooling operation for spatial data.

Arguments

- 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

2D tensor with shape: `(batch_size, channels)`

[source]

### GlobalAveragePooling2D

    keras.layers.GlobalAveragePooling2D(data_format=None)

Global average pooling operation for spatial data.

Arguments

- 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

2D tensor with shape: `(batch_size, channels)`

[source]

### GlobalMaxPooling3D

    keras.layers.GlobalMaxPooling3D(data_format=None)

Global Max pooling operation for 3D data.

Arguments

- 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. 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'`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
- If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`

Output shape

2D tensor with shape: `(batch_size, channels)`

[source]

### GlobalAveragePooling3D

    keras.layers.GlobalAveragePooling3D(data_format=None)

Global Average pooling operation for 3D data.

Arguments

- 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. 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'`: 5D tensor with shape: `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
- If `data_format='channels_first'`: 5D tensor with shape: `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`

Output shape

2D tensor with shape: `(batch_size, channels)`

GitHub « Previous Next »