[source]

### Conv1D

    keras.layers.Conv1D(filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

1D convolution layer (e.g. temporal convolution).

This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs. If `use_bias` is True, a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide an `input_shape` argument (tuple of integers or `None`, does not include the batch axis), e.g. `input_shape=(10, 128)` for time series sequences of 10 time steps with 128 features per step in `data_format="channels_last"`, or `(None, 128)` for variable-length sequences with 128 features per step.

Arguments

- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
- strides: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
- padding: One of `"valid"`, `"causal"` or `"same"` (case-insensitive). `"valid"` means "no padding". `"same"` results in padding the input such that the output has the same length as the original input. `"causal"` results in causal (dilated) convolutions, e.g. `output[t]` does not depend on `input[t + 1:]`. A zero padding is used such that the output has the same length as the original input. Useful when modeling temporal data where the model should not violate the temporal order. See WaveNet: A Generative Model for Raw Audio, section 2.1.
- 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, channels)` (default format for temporal data in Keras) while `"channels_first"` corresponds to inputs with shape `(batch, channels, steps)`.
- dilation_rate: an integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1.
- activation: Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).
- use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the `kernel` weights matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).

Input shape

3D tensor with shape: `(batch, steps, channels)`

Output shape

3D tensor with shape: `(batch, new_steps, filters)` `steps` value might have changed due to padding or strides.

[source]

### Conv2D

    keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

2D convolution layer (e.g. spatial convolution over images).

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If `use_bias` is True, a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide the keyword argument `input_shape` (tuple of integers, does not include the batch axis), e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures in `data_format="channels_last"`.

Arguments

- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
- padding: one of `"valid"` or `"same"` (case-insensitive). Note that `"same"` is slightly inconsistent across backends with `strides` != 1, as described here
- data_format: A string, one of `"channels_last"` 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".
- dilation_rate: an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1.
- activation: Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).
- use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the `kernel` weights matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).

Input shape

4D tensor with shape: `(batch, channels, rows, cols)` if `data_format` is `"channels_first"` or 4D tensor with shape: `(batch, rows, cols, channels)` if `data_format` is `"channels_last"`.

Output shape

4D tensor with shape: `(batch, filters, new_rows, new_cols)` if `data_format` is `"channels_first"` or 4D tensor with shape: `(batch, new_rows, new_cols, filters)` if `data_format` is `"channels_last"`. `rows` and `cols` values might have changed due to padding.

[source]

### SeparableConv1D

    keras.layers.SeparableConv1D(filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None)

Depthwise separable 1D convolution.

Separable convolutions consist in first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels. The `depth_multiplier` argument controls how many output channels are generated per input channel in the depthwise step.

Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.

Arguments

- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of single integer, specifying the length of the 1D convolution window.
- strides: An integer or tuple/list of single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
- padding: one of `"valid"` or `"same"` (case-insensitive).
- data_format: A string, one of `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, steps, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, steps)`.
- dilation_rate: An integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1.
- depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `filters_in * depth_multiplier`.
- activation: Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).
- use_bias: Boolean, whether the layer uses a bias vector.
- depthwise_initializer: Initializer for the depthwise kernel matrix (see initializers).
- pointwise_initializer: Initializer for the pointwise kernel matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- depthwise_regularizer: Regularizer function applied to the depthwise kernel matrix (see regularizer).
- pointwise_regularizer: Regularizer function applied to the pointwise kernel matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- depthwise_constraint: Constraint function applied to the depthwise kernel matrix (see constraints).
- pointwise_constraint: Constraint function applied to the pointwise kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).

Input shape

3D tensor with shape: `(batch, channels, steps)` if `data_format` is `"channels_first"` or 3D tensor with shape: `(batch, steps, channels)` if `data_format` is `"channels_last"`.

Output shape

3D tensor with shape: `(batch, filters, new_steps)` if `data_format` is `"channels_first"` or 3D tensor with shape: `(batch, new_steps, filters)` if `data_format` is `"channels_last"`. `new_steps` values might have changed due to padding or strides.

[source]

### SeparableConv2D

    keras.layers.SeparableConv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None)

Depthwise separable 2D convolution.

Separable convolution performs first a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels. The `depth_multiplier` argument controls how many output channels are generated per input channel in the depthwise step.

Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.

Arguments

- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
- padding: one of `"valid"` or `"same"` (case-insensitive).
- data_format: A string, one of `"channels_last"` 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".
- dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1.
- depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `filters_in * depth_multiplier`.
- activation: Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).
- use_bias: Boolean, whether the layer uses a bias vector.
- depthwise_initializer: Initializer for the depthwise kernel matrix (see initializers).
- pointwise_initializer: Initializer for the pointwise kernel matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- depthwise_regularizer: Regularizer function applied to the depthwise kernel matrix (see regularizer).
- pointwise_regularizer: Regularizer function applied to the pointwise kernel matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- depthwise_constraint: Constraint function applied to the depthwise kernel matrix (see constraints).
- pointwise_constraint: Constraint function applied to the pointwise kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).

Input shape

4D tensor with shape: `(batch, channels, rows, cols)` if `data_format` is `"channels_first"` or 4D tensor with shape: `(batch, rows, cols, channels)` if `data_format` is `"channels_last"`.

Output shape

4D tensor with shape: `(batch, filters, new_rows, new_cols)` if `data_format` is `"channels_first"` or 4D tensor with shape: `(batch, new_rows, new_cols, filters)` if `data_format` is `"channels_last"`. `rows` and `cols` values might have changed due to padding.

[source]

### DepthwiseConv2D

    keras.layers.DepthwiseConv2D(kernel_size, strides=(1, 1), padding='valid', depth_multiplier=1, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, depthwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, bias_constraint=None)

Depthwise 2D convolution.

Depthwise convolution performs just the first step of a depthwise spatial convolution (which acts on each input channel separately). The `depth_multiplier` argument controls how many output channels are generated per input channel in the depthwise step.

Arguments

- kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
- padding: one of `"valid"` or `"same"` (case-insensitive).
- depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `filters_in * depth_multiplier`.
- data_format: A string, one of `"channels_last"` 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'.
- dilation_rate: an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1.
- activation: Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. 'linear' activation: `a(x) = x`).
- use_bias: Boolean, whether the layer uses a bias vector.
- depthwise_initializer: Initializer for the depthwise kernel matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- depthwise_regularizer: Regularizer function applied to the depthwise kernel matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its 'activation'). (see regularizer).
- depthwise_constraint: Constraint function applied to the depthwise kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).

Input shape

4D tensor with shape: `(batch, channels, rows, cols)` if `data_format` is `"channels_first"` or 4D tensor with shape: `(batch, rows, cols, channels)` if `data_format` is `"channels_last"`.

Output shape

4D tensor with shape: `(batch, channels * depth_multiplier, new_rows, new_cols)` if `data_format` is `"channels_first"` or 4D tensor with shape: `(batch, new_rows, new_cols, channels * depth_multiplier)` if `data_format` is `"channels_last"`. `rows` and `cols` values might have changed due to padding.

[source]

### Conv2DTranspose

    keras.layers.Conv2DTranspose(filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

Transposed convolution layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.

When using this layer as the first layer in a model, provide the keyword argument `input_shape` (tuple of integers, does not include the batch axis), e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures in `data_format="channels_last"`.

Arguments

- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
- padding: one of `"valid"` or `"same"` (case-insensitive).
- output_padding: An integer or tuple/list of 2 integers, specifying the amount of padding along the height and width of the output tensor. Can be a single integer to specify the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to `None` (default), the output shape is inferred.
- data_format: A string, one of `"channels_last"` 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".
- dilation_rate: an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1.
- activation: Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).
- use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the `kernel` weights matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).

Input shape

4D tensor with shape: `(batch, channels, rows, cols)` if `data_format` is `"channels_first"` or 4D tensor with shape: `(batch, rows, cols, channels)` if `data_format` is `"channels_last"`.

Output shape

4D tensor with shape: `(batch, filters, new_rows, new_cols)` if `data_format` is `"channels_first"` or 4D tensor with shape: `(batch, new_rows, new_cols, filters)` if `data_format` is `"channels_last"`. `rows` and `cols` values might have changed due to padding. If `output_padding` is specified:

    new_rows = ((rows - 1) * strides[0] + kernel_size[0]
                - 2 * padding[0] + output_padding[0])
    new_cols = ((cols - 1) * strides[1] + kernel_size[1]
                - 2 * padding[1] + output_padding[1])

References

- A guide to convolution arithmetic for deep learning
- Deconvolutional Networks

[source]

### Conv3D

    keras.layers.Conv3D(filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format=None, dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

3D convolution layer (e.g. spatial convolution over volumes).

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If `use_bias` is True, a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide the keyword argument `input_shape` (tuple of integers, does not include the batch axis), e.g. `input_shape=(128, 128, 128, 1)` for 128x128x128 volumes with a single channel, in `data_format="channels_last"`.

Arguments

- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 3 integers, specifying the strides of the convolution along each spatial dimension. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
- padding: one of `"valid"` or `"same"` (case-insensitive).
- data_format: A string, one of `"channels_last"` 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".
- dilation_rate: an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1.
- activation: Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).
- use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the `kernel` weights matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).

Input shape

5D tensor with shape: `(batch, channels, conv_dim1, conv_dim2, conv_dim3)` if `data_format` is `"channels_first"` or 5D tensor with shape: `(batch, conv_dim1, conv_dim2, conv_dim3, channels)` if `data_format` is `"channels_last"`.

Output shape

5D tensor with shape: `(batch, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)` if `data_format` is `"channels_first"` or 5D tensor with shape: `(batch, new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)` if `data_format` is `"channels_last"`. `new_conv_dim1`, `new_conv_dim2` and `new_conv_dim3` values might have changed due to padding.

[source]

### Conv3DTranspose

    keras.layers.Conv3DTranspose(filters, kernel_size, strides=(1, 1, 1), padding='valid', output_padding=None, data_format=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

Transposed convolution layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.

When using this layer as the first layer in a model, provide the keyword argument `input_shape` (tuple of integers, does not include the batch axis), e.g. `input_shape=(128, 128, 128, 3)` for a 128x128x128 volume with 3 channels if `data_format="channels_last"`.

Arguments

- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 3 integers, specifying the strides of the convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
- padding: one of `"valid"` or `"same"` (case-insensitive).
- output_padding: An integer or tuple/list of 3 integers, specifying the amount of padding along the depth, height, and width. Can be a single integer to specify the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to `None` (default), the output shape is inferred.
- data_format: A string, one of `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, depth, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, depth, 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".
- dilation_rate: an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1.
- activation: Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).
- use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the `kernel` weights matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to the `kernel` weights matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).

Input shape

5D tensor with shape: `(batch, channels, depth, rows, cols)` if `data_format` is `"channels_first"` or 5D tensor with shape: `(batch, depth, rows, cols, channels)` if `data_format` is `"channels_last"`.

Output shape

5D tensor with shape: `(batch, filters, new_depth, new_rows, new_cols)` if `data_format` is `"channels_first"` or 5D tensor with shape: `(batch, new_depth, new_rows, new_cols, filters)` if `data_format` is `"channels_last"`. `depth` and `rows` and `cols` values might have changed due to padding. If `output_padding` is specified::

    new_depth = ((depth - 1) * strides[0] + kernel_size[0]
                 - 2 * padding[0] + output_padding[0])
    new_rows = ((rows - 1) * strides[1] + kernel_size[1]
                - 2 * padding[1] + output_padding[1])
    new_cols = ((cols - 1) * strides[2] + kernel_size[2]
                - 2 * padding[2] + output_padding[2])

References

- A guide to convolution arithmetic for deep learning
- Deconvolutional Networks

[source]

### Cropping1D

    keras.layers.Cropping1D(cropping=(1, 1))

Cropping layer for 1D input (e.g. temporal sequence).

It crops along the time dimension (axis 1).

Arguments

- cropping: int or tuple of int (length 2) How many units should be trimmed off at the beginning and end of the cropping dimension (axis 1). If a single int is provided, the same value will be used for both.

Input shape

3D tensor with shape `(batch, axis_to_crop, features)`

Output shape

3D tensor with shape `(batch, cropped_axis, features)`

[source]

### Cropping2D

    keras.layers.Cropping2D(cropping=((0, 0), (0, 0)), data_format=None)

Cropping layer for 2D input (e.g. picture).

It crops along spatial dimensions, i.e. height and width.

Arguments

- cropping: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
  - If int: the same symmetric cropping is applied to height and width.
  - If tuple of 2 ints: interpreted as two different symmetric cropping values for height and width: `(symmetric_height_crop, symmetric_width_crop)`.
  - If tuple of 2 tuples of 2 ints: interpreted as `((top_crop, bottom_crop), (left_crop, right_crop))`
- data_format: A string, one of `"channels_last"` 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

4D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, rows, cols, channels)` - If `data_format` is `"channels_first"`: `(batch, channels, rows, cols)`

Output shape

4D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, cropped_rows, cropped_cols, channels)` - If `data_format` is `"channels_first"`: `(batch, channels, cropped_rows, cropped_cols)`

Examples

    # Crop the input 2D images or feature maps
    model = Sequential()
    model.add(Cropping2D(cropping=((2, 2), (4, 4)),
                         input_shape=(28, 28, 3)))
    # now model.output_shape == (None, 24, 20, 3)
    model.add(Conv2D(64, (3, 3), padding='same'))
    model.add(Cropping2D(cropping=((2, 2), (2, 2))))
    # now model.output_shape == (None, 20, 16, 64)

[source]

### Cropping3D

    keras.layers.Cropping3D(cropping=((1, 1), (1, 1), (1, 1)), data_format=None)

Cropping layer for 3D data (e.g. spatial or spatio-temporal).

Arguments

- cropping: int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
  - If int: the same symmetric cropping is applied to depth, height, and width.
  - If tuple of 3 ints: interpreted as three different symmetric cropping values for depth, height, and width: `(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`.
  - If tuple of 3 tuples of 2 ints: interpreted as `((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))`
- data_format: A string, one of `"channels_last"` 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

5D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, depth)` - If `data_format` is `"channels_first"`: `(batch, depth, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)`

Output shape

5D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, first_cropped_axis, second_cropped_axis, third_cropped_axis, depth)` - If `data_format` is `"channels_first"`: `(batch, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)`

[source]

### UpSampling1D

    keras.layers.UpSampling1D(size=2)

Upsampling layer for 1D inputs.

Repeats each temporal step `size` times along the time axis.

Arguments

- size: integer. Upsampling factor.

Input shape

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

Output shape

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

[source]

### UpSampling2D

    keras.layers.UpSampling2D(size=(2, 2), data_format=None, interpolation='nearest')

Upsampling layer for 2D inputs.

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

Arguments

- size: int, or tuple of 2 integers. The upsampling factors for rows and columns.
- data_format: A string, one of `"channels_last"` 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".
- interpolation: A string, one of `nearest` or `bilinear`. Note that CNTK does not support yet the `bilinear` upscaling and that with Theano, only `size=(2, 2)` is possible.

Input shape

4D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, rows, cols, channels)` - If `data_format` is `"channels_first"`: `(batch, channels, rows, cols)`

Output shape

4D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, upsampled_rows, upsampled_cols, channels)` - If `data_format` is `"channels_first"`: `(batch, channels, upsampled_rows, upsampled_cols)`

[source]

### UpSampling3D

    keras.layers.UpSampling3D(size=(2, 2, 2), data_format=None)

Upsampling layer for 3D inputs.

Repeats the 1st, 2nd and 3rd dimensions of the data by size[0], size[1] and size[2] respectively.

Arguments

- size: int, or tuple of 3 integers. The upsampling factors for dim1, dim2 and dim3.
- data_format: A string, one of `"channels_last"` 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

5D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, dim1, dim2, dim3, channels)` - If `data_format` is `"channels_first"`: `(batch, channels, dim1, dim2, dim3)`

Output shape

5D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)` - If `data_format` is `"channels_first"`: `(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)`

[source]

### ZeroPadding1D

    keras.layers.ZeroPadding1D(padding=1)

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

Arguments

- padding: int, or tuple of int (length 2), or dictionary.

  - If int:

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

  - If tuple of int (length 2):

  How many zeros to add at the beginning and at the end of the padding dimension (`(left_pad, right_pad)`).

Input shape

3D tensor with shape `(batch, axis_to_pad, features)`

Output shape

3D tensor with shape `(batch, padded_axis, features)`

[source]

### ZeroPadding2D

    keras.layers.ZeroPadding2D(padding=(1, 1), data_format=None)

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

This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor.

Arguments

- padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
  - If int: the same symmetric padding is applied to height and width.
  - If tuple of 2 ints: interpreted as two different symmetric padding values for height and width: `(symmetric_height_pad, symmetric_width_pad)`.
  - If tuple of 2 tuples of 2 ints: interpreted as `((top_pad, bottom_pad), (left_pad, right_pad))`
- data_format: A string, one of `"channels_last"` 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

4D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, rows, cols, channels)` - If `data_format` is `"channels_first"`: `(batch, channels, rows, cols)`

Output shape

4D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, padded_rows, padded_cols, channels)` - If `data_format` is `"channels_first"`: `(batch, channels, padded_rows, padded_cols)`

[source]

### ZeroPadding3D

    keras.layers.ZeroPadding3D(padding=(1, 1, 1), data_format=None)

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

Arguments

- padding: int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
  - If int: the same symmetric padding is applied to height and width.
  - If tuple of 3 ints: interpreted as three different symmetric padding values for depth, height, and width: `(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
  - If tuple of 3 tuples of 2 ints: interpreted as `((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))`
- data_format: A string, one of `"channels_last"` 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

5D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth)` - If `data_format` is `"channels_first"`: `(batch, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)`

Output shape

5D tensor with shape: - If `data_format` is `"channels_last"`: `(batch, first_padded_axis, second_padded_axis, third_axis_to_pad, depth)` - If `data_format` is `"channels_first"`: `(batch, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)`

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