MaxPooling1D
keras.layers.MaxPooling1D(pool_size=2, strides=None, padding='valid')
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).
Input shape
3D tensor with shape: (batch_size, steps, features)
.
Output shape
3D tensor with shape: (batch_size, downsampled_steps, features)
.
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) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_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)
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) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)
. It defaults to theimage_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)
AveragePooling1D
keras.layers.AveragePooling1D(pool_size=2, strides=None, padding='valid')
Average pooling 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).
Input shape
3D tensor with shape: (batch_size, steps, features)
.
Output shape
3D tensor with shape: (batch_size, downsampled_steps, features)
.
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) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_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)
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) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)
. It defaults to theimage_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)
GlobalMaxPooling1D
keras.layers.GlobalMaxPooling1D()
Global max pooling operation for temporal data.
Input shape
3D tensor with shape: (batch_size, steps, features)
.
Output shape
2D tensor with shape:
(batch_size, features)
GlobalAveragePooling1D
keras.layers.GlobalAveragePooling1D()
Global average pooling operation for temporal data.
Input shape
3D tensor with shape: (batch_size, steps, features)
.
Output shape
2D tensor with shape:
(batch_size, features)
GlobalMaxPooling2D
keras.layers.GlobalMaxPooling2D(data_format=None)
Global max pooling operation for spatial data.
Arguments
- data_format: A string,
one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_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)
GlobalAveragePooling2D
keras.layers.GlobalAveragePooling2D(data_format=None)
Global average pooling operation for spatial data.
Arguments
- data_format: A string,
one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_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)