[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 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 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]

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

[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

[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 two 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 two different symmetric padding values for 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)