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
, e.g.
(10, 128)
for sequences of 10 vectors of 128dimensional vectors,
or (None, 128)
for variablelength sequences of 128dimensional vectors.
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"
(caseinsensitive)."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 oninput[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] (https://arxiv.org/abs/1609.03499).  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 anystrides
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.
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 sample 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"
(caseinsensitive). Note that"same"
is slightly inconsistent across backends withstrides
!= 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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".  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.
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"
(caseinsensitive).  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 anystrides
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.
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"
(caseinsensitive).  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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".  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 anystrides
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.
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 sample 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"
(caseinsensitive).  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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".  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] (https://arxiv.org/abs/1603.07285v1)
 [Deconvolutional Networks] (http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
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 sample 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"
(caseinsensitive).  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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".  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.
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 sample 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"
(caseinsensitive).  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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".  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] (https://arxiv.org/abs/1603.07285v1)
 [Deconvolutional Networks] (http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
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)
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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
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)
Cropping3D
keras.layers.Cropping3D(cropping=((1, 1), (1, 1), (1, 1)), data_format=None)
Cropping layer for 3D data (e.g. spatial or spatiotemporal).
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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
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)
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)
.
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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".  interpolation: A string, one of
nearest
orbilinear
. Note that CNTK does not support yet thebilinear
upscaling and that with Theano, onlysize=(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)
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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
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)
ZeroPadding1D
keras.layers.ZeroPadding1D(padding=1)
Zeropadding 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)
ZeroPadding2D
keras.layers.ZeroPadding2D(padding=(1, 1), data_format=None)
Zeropadding 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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
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)
ZeroPadding3D
keras.layers.ZeroPadding3D(padding=(1, 1, 1), data_format=None)
Zeropadding layer for 3D data (spatial or spatiotemporal).
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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
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)