MaxPooling1D

keras.layers.convolutional.MaxPooling1D(pool_length=2, stride=None, border_mode='valid')

Max pooling operation for temporal data.

Input shape

3D tensor with shape: (samples, steps, features).

Output shape

3D tensor with shape: (samples, downsampled_steps, features).

Arguments

pool_length: factor by which to downscale. 2 will halve the input.
stride: integer or None. Stride value.
border_mode: 'valid' or 'same'.
- Note: 'same' will only work with TensorFlow for the time being.

[source]

AveragePooling1D

keras.layers.convolutional.AveragePooling1D(pool_length=2, stride=None, border_mode='valid')

Average pooling for temporal data.

Input shape

3D tensor with shape: (samples, steps, features).

Output shape

3D tensor with shape: (samples, downsampled_steps, features).

Arguments

pool_length: factor by which to downscale. 2 will halve the input.
stride: integer or None. Stride value.
border_mode: 'valid' or 'same'.
- Note: 'same' will only work with TensorFlow for the time being.

[source]

MaxPooling2D

keras.layers.convolutional.MaxPooling2D(pool_size=(2, 2), strides=None, border_mode='valid', dim_ordering='th')

Max pooling operation for spatial data.

Input shape

4D tensor with shape: (samples, channels, rows, cols) if dim_ordering='th' or 4D tensor with shape: (samples, rows, cols, channels) if dim_ordering='tf'.

Output shape

4D tensor with shape: (nb_samples, channels, pooled_rows, pooled_cols) if dim_ordering='th' or 4D tensor with shape: (samples, pooled_rows, pooled_cols, channels) if dim_ordering='tf'.

Arguments

pool_size: tuple of 2 integers, factors by which to downscale (vertical, horizontal). (2, 2) will halve the image in each dimension.
strides: tuple of 2 integers, or None. Strides values.
border_mode: 'valid' or 'same'.
- Note: 'same' will only work with TensorFlow for the time being.
dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 3.

[source]

AveragePooling2D

keras.layers.convolutional.AveragePooling2D(pool_size=(2, 2), strides=None, border_mode='valid', dim_ordering='th')

Average pooling operation for spatial data.

Input shape

4D tensor with shape: (samples, channels, rows, cols) if dim_ordering='th' or 4D tensor with shape: (samples, rows, cols, channels) if dim_ordering='tf'.

Output shape

4D tensor with shape: (nb_samples, channels, pooled_rows, pooled_cols) if dim_ordering='th' or 4D tensor with shape: (samples, pooled_rows, pooled_cols, channels) if dim_ordering='tf'.

Arguments

pool_size: tuple of 2 integers, factors by which to downscale (vertical, horizontal). (2, 2) will halve the image in each dimension.
strides: tuple of 2 integers, or None. Strides values.
border_mode: 'valid' or 'same'.
- Note: 'same' will only work with TensorFlow for the time being.
dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 3.

[source]

MaxPooling3D

keras.layers.convolutional.MaxPooling3D(pool_size=(2, 2, 2), strides=None, border_mode='valid', dim_ordering='th')

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

Note: this layer will only work with Theano for the time being.

Input shape

5D tensor with shape: (samples, channels, len_pool_dim1, len_pool_dim2, len_pool_dim3) if dim_ordering='th' or 5D tensor with shape: (samples, len_pool_dim1, len_pool_dim2, len_pool_dim3, channels) if dim_ordering='tf'.

Output shape

5D tensor with shape: (nb_samples, channels, pooled_dim1, pooled_dim2, pooled_dim3) if dim_ordering='th' or 5D tensor with shape: (samples, pooled_dim1, pooled_dim2, pooled_dim3, channels) if dim_ordering='tf'.

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.
border_mode: 'valid' or 'same'.
dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 4.

[source]

Convolution1D

keras.layers.convolutional.Convolution1D(nb_filter, filter_length, init='uniform', activation='linear', weights=None, border_mode='valid', subsample_length=1, W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None, input_dim=None, input_length=None)

Convolution operator for filtering neighborhoods of one-dimensional inputs. When using this layer as the first layer in a model, either provide the keyword argument input_dim (int, e.g. 128 for sequences of 128-dimensional vectors), or input_shape (tuple of integers, e.g. (10, 128) for sequences of 10 vectors of 128-dimensional vectors).

Input shape

3D tensor with shape: (samples, steps, input_dim).

Output shape

3D tensor with shape: (samples, new_steps, nb_filter). steps value might have changed due to padding.

Arguments

nb_filter: Number of convolution kernels to use (dimensionality of the output).
filter_length: The extension (spatial or temporal) of each filter.
init: name of initialization function for the weights of the layer (see initializations), or alternatively, Theano function to use for weights initialization. This parameter is only relevant if you don't pass a weights argument.
activation: name of activation function to use (see activations), or alternatively, elementwise Theano function. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
weights: list of numpy arrays to set as initial weights.
border_mode: 'valid' or 'same'.
subsample_length: factor by which to subsample output.
W_regularizer: instance of WeightRegularizer (eg. L1 or L2 regularization), applied to the main weights matrix.
b_regularizer: instance of WeightRegularizer, applied to the bias.
activity_regularizer: instance of ActivityRegularizer, applied to the network output.
W_constraint: instance of the constraints module (eg. maxnorm, nonneg), applied to the main weights matrix.
b_constraint: instance of the constraints module, applied to the bias.
input_dim: Number of channels/dimensions in the input. Either this argument or the keyword argument input_shapemust be provided when using this layer as the first layer in a model.
input_length: Length of input sequences, when it is constant. This argument is required if you are going to connect Flatten then Dense layers upstream (without it, the shape of the dense outputs cannot be computed).

[source]

Convolution2D

keras.layers.convolutional.Convolution2D(nb_filter, nb_row, nb_col, init='glorot_uniform', activation='linear', weights=None, border_mode='valid', subsample=(1, 1), dim_ordering='th', W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None)

Convolution operator for filtering windows of two-dimensional inputs. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the sample axis), e.g. input_shape=(3, 128, 128) for 128x128 RGB pictures.

Input shape

4D tensor with shape: (samples, channels, rows, cols) if dim_ordering='th' or 4D tensor with shape: (samples, rows, cols, channels) if dim_ordering='tf'.

Output shape

4D tensor with shape: (samples, nb_filter, new_rows, new_cols) if dim_ordering='th' or 4D tensor with shape: (samples, new_rows, new_cols, nb_filter) if dim_ordering='tf'. rows and cols values might have changed due to padding.

Arguments

nb_filter: Number of convolution filters to use.
nb_row: Number of rows in the convolution kernel.
nb_col: Number of columns in the convolution kernel.
init: name of initialization function for the weights of the layer (see initializations), or alternatively, Theano function to use for weights initialization. This parameter is only relevant if you don't pass a weights argument.
activation: name of activation function to use (see activations), or alternatively, elementwise Theano function. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
weights: list of numpy arrays to set as initial weights.
border_mode: 'valid' or 'same'.
subsample: tuple of length 2. Factor by which to subsample output. Also called strides elsewhere.
W_regularizer: instance of WeightRegularizer (eg. L1 or L2 regularization), applied to the main weights matrix.
b_regularizer: instance of WeightRegularizer, applied to the bias.
activity_regularizer: instance of ActivityRegularizer, applied to the network output.
W_constraint: instance of the constraints module (eg. maxnorm, nonneg), applied to the main weights matrix.
b_constraint: instance of the constraints module, applied to the bias.
dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 3.

[source]

Convolution3D

keras.layers.convolutional.Convolution3D(nb_filter, kernel_dim1, kernel_dim2, kernel_dim3, init='glorot_uniform', activation='linear', weights=None, border_mode='valid', subsample=(1, 1, 1), dim_ordering='th', W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None)

Convolution operator for filtering windows of three-dimensional inputs. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the sample axis), e.g. input_shape=(3, 10, 128, 128) for 10 frames of 128x128 RGB pictures.

Note: this layer will only work with Theano for the time being.

Input shape

5D tensor with shape: (samples, channels, conv_dim1, conv_dim2, conv_dim3) if dim_ordering='th' or 5D tensor with shape: (samples, conv_dim1, conv_dim2, conv_dim3, channels) if dim_ordering='tf'.

Output shape

5D tensor with shape: (samples, nb_filter, new_conv_dim1, new_conv_dim2, new_conv_dim3) if dim_ordering='th' or 5D tensor with shape: (samples, new_conv_dim1, new_conv_dim2, new_conv_dim3, nb_filter) if dim_ordering='tf'. new_conv_dim1, new_conv_dim2 and new_conv_dim3 values might have changed due to padding.

Arguments

nb_filter: Number of convolution filters to use.
kernel_dim1: Length of the first dimension in the covolution kernel.
kernel_dim2: Length of the second dimension in the convolution kernel.
kernel_dim3: Length of the third dimension in the convolution kernel.
init: name of initialization function for the weights of the layer (see initializations), or alternatively, Theano function to use for weights initialization. This parameter is only relevant if you don't pass a weights argument.
activation: name of activation function to use (see activations), or alternatively, elementwise Theano function. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
weights: list of numpy arrays to set as initial weights.
border_mode: 'valid' or 'same'.
subsample: tuple of length 3. Factor by which to subsample output. Also called strides elsewhere.
- Note: 'subsample' is implemented by slicing the output of conv3d with strides=(1,1,1).
W_regularizer: instance of WeightRegularizer (eg. L1 or L2 regularization), applied to the main weights matrix.
b_regularizer: instance of WeightRegularizer, applied to the bias.
activity_regularizer: instance of ActivityRegularizer, applied to the network output.
W_constraint: instance of the constraints module (eg. maxnorm, nonneg), applied to the main weights matrix.
b_constraint: instance of the constraints module, applied to the bias.
dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 4.

[source]

AveragePooling3D

keras.layers.convolutional.AveragePooling3D(pool_size=(2, 2, 2), strides=None, border_mode='valid', dim_ordering='th')

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

Note: this layer will only work with Theano for the time being.

Input shape

5D tensor with shape: (samples, channels, len_pool_dim1, len_pool_dim2, len_pool_dim3) if dim_ordering='th' or 5D tensor with shape: (samples, len_pool_dim1, len_pool_dim2, len_pool_dim3, channels) if dim_ordering='tf'.

Output shape

5D tensor with shape: (nb_samples, channels, pooled_dim1, pooled_dim2, pooled_dim3) if dim_ordering='th' or 5D tensor with shape: (samples, pooled_dim1, pooled_dim2, pooled_dim3, channels) if dim_ordering='tf'.

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.
border_mode: 'valid' or 'same'.
dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 4.

[source]

UpSampling1D

keras.layers.convolutional.UpSampling1D(length=2)

Repeat each temporal step length times along the time axis.

Input shape

3D tensor with shape: (samples, steps, features).

Output shape

3D tensor with shape: (samples, upsampled_steps, features).

_Arguments_:
length: integer. Upsampling factor.

[source]

UpSampling2D

keras.layers.convolutional.UpSampling2D(size=(2, 2), dim_ordering='th')

Repeat the rows and columns of the data by size[0] and size[1] respectively.

Input shape

4D tensor with shape: (samples, channels, rows, cols) if dim_ordering='th' or 4D tensor with shape: (samples, rows, cols, channels) if dim_ordering='tf'.

Output shape

4D tensor with shape: (samples, channels, upsampled_rows, upsampled_cols) if dim_ordering='th' or 4D tensor with shape: (samples, upsampled_rows, upsampled_cols, channels) if dim_ordering='tf'.

Arguments

size: tuple of 2 integers. The upsampling factors for rows and columns.
dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 3.

[source]

UpSampling3D

keras.layers.convolutional.UpSampling3D(size=(2, 2, 2), dim_ordering='th')

Repeat the first, second and third dimension of the data by size[0], size[1] and size[2] respectively.

Note: this layer will only work with Theano for the time being.

Input shape

5D tensor with shape: (samples, channels, dim1, dim2, dim3) if dim_ordering='th' or 5D tensor with shape: (samples, dim1, dim2, dim3, channels) if dim_ordering='tf'.

Output shape

5D tensor with shape: (samples, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3) if dim_ordering='th' or 5D tensor with shape: (samples, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels) if dim_ordering='tf'.

Arguments

size: tuple of 3 integers. The upsampling factors for dim1, dim2 and dim3.
dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension (the depth) is at index 1, in 'tf' mode is it at index 4.

[source]

ZeroPadding1D

keras.layers.convolutional.ZeroPadding1D(padding=1)

Zero-padding layer for 1D input (e.g. temporal sequence).

Input shape

3D tensor with shape (samples, axis_to_pad, features)

Output shape

3D tensor with shape (samples, padded_axis, features)

Arguments

padding: int How many zeros to add at the beginning and end of the padding dimension (axis 1).

[source]

ZeroPadding2D

keras.layers.convolutional.ZeroPadding2D(padding=(1, 1), dim_ordering='th')

Zero-padding layer for 2D input (e.g. picture).

Input shape

4D tensor with shape: (samples, depth, first_axis_to_pad, second_axis_to_pad)

Output shape

4D tensor with shape: (samples, depth, first_padded_axis, second_padded_axis)

Arguments

padding: tuple of int (length 2) How many zeros to add at the beginning and end of the 2 padding dimensions (axis 3 and 4).

[source]

ZeroPadding3D

keras.layers.convolutional.ZeroPadding3D(padding=(1, 1, 1), dim_ordering='th')

Zero-padding layer for 3D data (spatial or spatio-temporal).

Note: this layer will only work with Theano for the time being.

Input shape

5D tensor with shape: (samples, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)

Output shape

5D tensor with shape: (samples, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)

Arguments

padding: tuple of int (length 3) How many zeros to add at the beginning and end of the 3 padding dimensions (axis 3, 4 and 5).