## Keras Convolutional Layers## Conv1DIt refers to a one-dimensional convolutional layer. For example, temporal convolution which generates a convolution kernel for creating a tensor of outputs. The convolution kernel is convolved with input layer over a single temporal (spatial) dimension. A bias vector will be developed and included to the outputs, if When we utilize the conv1D layer as the initial layer in our model, it provides us with an
**filters:**Filters refers to an integer in which the output space dimensionality or the number of output filters present in the convolution.**kernel_size:**It is an integer or tuple/list of an individual integer that specifies the length of the 1D convolution window.**strides:**It is an integer or tuple/list of an individual integer that specifies the stride length of the convolution. Determining any stride value != 1 is incompatible with specifying any**dilation_rate**value != 1.**padding:**It is one of "**valid**", "**casual**" or "**same**", where**valid**implies to no padding,**same**means padding the input in such a way that it generates the output having the same length as that of the original input and**casual**results in dilated output, i.e.,**output[t]**is independent of**output[t + 1:]**. To get the output length similar to the input, a zero-padding can be used. The concept of padding is useful while modeling a temporal data in order to make sure that the model does not violate temporal**data_format:**It is a string of "channels_last" or "channels_first", which is the order of input dimensions. Here the**"channels_last"**links to the input shape**(batch, steps, channels)**, which is the default format for temporal data in Keras. However, the**"channels_first"**is used to relate the input shape**(batch, channels, steps)**.**dilation_rate:**It is an integer or tuple/ list of an individual integer that relates to the dilation rate of a dilated convolution. It currently relates any**dilation_rate**value != 1 is incompatible by specifying any**strides**value != 1.**activation:**It refers to an activation function to be used. When nothing is specified, then by defaults, it is a linear activation**a(x) = x**, or we can say no activation function is applied.**use_bias:**It represents a Boolean that shows whether the layer utilizes a bias vector.**kernel_initializer:**It can be defined as an initializer for the**kernel**weights matrix.**bias_initializer:**It refers to an initializer for bias vector.**kernel_regularizer:**It refers to a regularizer function, which is applied to the**kernel**weights matrix.**bias_regularizer:**It can be defined as a regularizer function, which is applied to the bias vector.**activity_regularizer:**It refers to a regularizer function that is applied to the activation (output of the layer).**kernel_constraint:**It is a constraint function applied to the kernel matrix.**bias_constraint:**It can be defined as a constraint function applied to the bias vector.
It refers to a 3D tensor of shape
The output shape is a 3D tensor of shape ## Conv2DIt refers to a two-dimensional convolution layer, like a spatial convolution on images. It develops a convolution kernel, which can be convolved with the input layer for the generation of the tensors output. If we set
**filter:**It is an integer that signifies the output space dimensionality or a total number of output filters present in a convolution.**kernel_size:**It can either be an integer or tuple/list of 2 integers to represent the height and width of a 2D convolution window. It can also exist as a single integer that signifies the same value for rest all of the spatial domain.**strides:**It is either an integer or a tuple/list of 2 integers that represents the convolution strides along with height and width. It might exist as a single integer that indicates the same value for the spatial dimension. If we signify any stride value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**padding:**One of**"valid"**or**"same,"**where the same reflects some inconsistency across the backend with**strides**!=1.**data_format:**It is a string of "channels_last" or "channels_first", which is the order of input dimensions. Here the**"channels_last"**describes the input shape**(batch, height, width, channels)**, and the**"channels_first"**describes the input shape**(batch, channels, height, width)**. It defaults to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder, then it is residing at "channels_last".**dilation_rate:**It can be an integer or tuple/ list of 2 integers that relates to the dilation rate to be used for dilated convolution. It might have an individual integer that indicates the same value for a spatial dimension. If we specify any stride value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**activation:**It refers to an activation function to be used. When nothing is specified, then by defaults, it is a linear activation**a(x) = x**, or we can say no activation function is applied.**use_bias:**It represents a Boolean that shows whether the layer utilizes a bias vector.**kernel_initializer:**It can be defined as an initializer for the**kernel**weights matrix.**bias_initializer:**It refers to an initializer for bias vector.**kernel_regularizer:**It refers to a regularizer function, which is applied to the**kernel**weights matrix.**bias_regularizer:**It can be defined as a regularizer function, which is applied to the bias vector.**activity_regularizer:**It refers to a regularizer function that is applied to the activation (output of the layer).**kernel_constraint:**It is a constraint function applied to the kernel matrix.**bias_constraint:**It can be defined as a constraint function applied to the bias vector.
If the
If the ## SeparableConv1DIt is a Depthwise separable 1D convolution. Firstly, it accomplishes a depthwise spatial convolution on an single channel and then pointwise convolution to mix the resultant channels output. The argument The Separable Convolutions can be easily understood by means of factorizing a convolution kernel into two smaller kernels.
**filter:**It is an integer that signifies the output space dimensionality or a total number of output filters present in a convolution.**kernel_size:**It can either be an integer or tuple/list of single integer to represent the length of a 1D convolution window.**strides:**It is either an integer or a tuple/list of a single integer that represents the convolution strides length. If we specify any stride value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**padding:**One of**"valid"**or**"same,"**where the same shows some inconsistency across the backend with**strides**!=1.**data_format:**It is in either mode, i.e.,**'channels_first'**that corresponds to input shape:**(batch, channels, steps)**or**'channels_last'**corresponds to**(batch, steps, channels)**.**dilation_rate:**It can be an integer or tuple/ list of a single integer that relates to the dilation rate to be used for dilated convolution. If we specify any**stride**value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**depth_multiplier:**It represents the total number of depthwise convolution channels each of the respective input channels, which is equivalent to**filters_in * depth_multiplier**.**activation:**It refers to an activation function to be used. When nothing is specified, then by defaults, it is a linear activation**a(x) = x**, or we can say no activation function is applied.**use_bias:**It represents a Boolean that shows whether the layer utilizes a bias vector.**depthwise_initializer:**It refers to an initializer for the depthwise kernel matrix.**pointwise_initializer:**It refers to an initializer for the pointwise kernel matrix.**bias_initializer:**It refers to an initializer for bias vector.**depthwise_regularizer:**It refers to a regularizer function that is applied to the depthwise kernel matrix.**pointwise_regularizer:**It refers to a regularizer function that is applied to the pointwise kernel matrix.**bias_regularizer:**It can be defined as a regularizer function, which is applied to the bias vector.**activity_regularizer:**It refers to a regularizer function that is applied to the activation (output of the layer).**depthwise_constraint:**It can be defined as a constraint function applied to the depthwise kernel matrix.**pointwise_constraint:**It can be defined as a constraint function applied to the pointwise kernel matrix.**bias_constraint:**It can be defined as a constraint function applied to the bias vector.
If the
If the ## SeparableConv2DIt is a depthwise separable 2D convolution. Firstly, it performs a depthwise spatial convolution on an individual channel and then pointwise convolution to mix the output of the resultant channel. The argument The Separable Convolutions can be easily understood by means of factorizing a convolution kernel into two smaller kernels or as an extension of an Inception block.
**filter:**It is an integer that signifies the output space dimensionality or the total number of output filters present in a convolution.**kernel_size:**It can either be an integer or tuple/list of 2 integers to represent the height and width of a 2D convolution window. It can also exist as a single integer that signifies the same value for rest all of the spatial domain.**strides:**It is either an integer or a tuple/list of 2 integers that represents the convolution strides along with the height and width. It can also exist as a single integer that signifies the same value for rest all of the spatial domain. If we specify any stride value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**padding:**One of**"valid"**or**"same,"**where the same shows some inconsistency across the backend with**strides**!=1.**data_format:**It is in either mode, i.e.,**'channels_first'**that corresponds to input shape:**(batch, channels, height, width)**or**'channels_last'**corresponds to**(batch, height, width, channels)**. It defaults to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder, then it is residing at "channels_last".**dilation_rate:**It can be an integer or tuple/ list of 2 integers that relates to the dilation rate to be used for dilated convolution. If we specify any**stride**value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**depth_multiplier:**It represents the total number of depthwise convolution channels for each of the respective input channels, which is equivalent to**filters_in * depth_multiplier**.**activation:**It refers to an activation function to be used. When nothing is specified, then by defaults, it is a linear activation**a(x) = x**, or we can say no activation function is applied.**use_bias:**It represents a Boolean that shows whether the layer utilizes a bias vector.**depthwise_initializer:**It refers to an initializer for the depthwise kernel matrix.**pointwise_initializer:**It refers to an initializer for the pointwise kernel matrix.**bias_initializer:**It refers to an initializer for bias vector.**depthwise_regularizer:**It refers to a regularizer function that is applied to the depthwise kernel matrix.**pointwise_regularizer:**It refers to a regularizer function that is applied to the pointwise kernel matrix.**bias_regularizer:**It can be defined as a regularizer function, which is applied to the bias vector.**activity_regularizer:**It refers to a regularizer function that is applied to the activation (output of the layer).**depthwise_constraint:**It can be defined as a constraint function applied to the depthwise kernel matrix.**pointwise_constraint:**It can be defined as a constraint function applied to the pointwise kernel matrix.**bias_constraint:**It can be defined as a constraint function applied to the bias vector.
If the
If the ## DepthwiseConv2DIt is a depthwise 2D convolution layer that firstly performs a similar action as that of the depthwise spatial convolution in which it separately performs on each input channel. The argument
**kernel_size:**It can either be an integer or tuple/list of 2 integers to represent the height and width of a 2D convolution window. It can also exist as a single integer that signifies the same value for all of the spatial domain.**strides:**It is either an integer or a tuple/list of 2 integers that represents the convolution strides along with the height and width. It can exist as a single integer that signifies the same value for rest all of the spatial domain. If we specify any stride value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**padding:**One of**"valid"**or**"same,"**where the same shows some inconsistency across the backend with**strides**!=1.**data_format:**It is in either mode, i.e.,**'channels_first'**that corresponds to input shape:**(batch, channels, height, width)**or**'channels_last'**corresponds to**(batch, height, width, channels)**. It defaults to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder, then it is residing at "channels_last".**dilation_rate:**It can be an integer or tuple/ list of 2 integers that relates to the dilation rate to be used for dilated convolution. If we specify any**stride**value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**depth_multiplier:**It represents the total number of depthwise convolution channels for each of the respective input channels, which is equivalent to**filters_in * depth_multiplier**.**activation:**It refers to an activation function to be used. When nothing is specified, then by defaults, it is a linear activation**a(x) = x**, or we can say no activation function is applied.**use_bias:**It represents a Boolean that shows whether the layer utilizes a bias vector.**depthwise_initializer:**It refers to an initializer for the depthwise kernel matrix.**bias_initializer:**It refers to an initializer for bias vector.**depthwise_regularizer:**It refers to a regularizer function that is applied to the depthwise kernel matrix.**bias_regularizer:**It can be defined as a regularizer function, which is applied to the bias vector.**activity_regularizer:**It refers to a regularizer function that is applied to the activation (output of the layer).**depthwise_constraint:**It can be defined as a constraint function applied to the depthwise kernel matrix.**bias_constraint:**It can be defined as a constraint function applied to the bias vector.
If the
If the ## Conv2DTransposeIt is a Transpose convolution layer, which is sometimes incorrectly known as Deconvolution. But in reality, it does not perform Deconvolution. The Conv2DTranspose layer is mainly required when the transformation moves in the opposite direction to that of a normal convolution, or simply we can say when the transformation goes from something that has an output shape of some convolution to the one that has input shape of convolution. The layer can be used as an initial layer by using an argument
**filter:**It is an integer that signifies the output space dimensionality or a total number of output filters present in a convolution.**kernel_size:**It can either be an integer or tuple/list of 2 integers to represent the height and width of a 2D convolution window. It can also exist as a single integer that signifies the same value for all of the spatial domain.**strides:**It is either an integer or a tuple/list of 2 integers that represents the convolution strides along with the height and width. It can exist as a single integer that signifies the same value for rest all of the spatial domain. If we specify any stride value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**padding:**One of**"valid"**or**"same,"**where the same shows some inconsistency across the backend with**strides**!=1.**output_padding:**It can either be an integer or tuple/list of 2 integers to represent the height and width of a 2D convolution window. It can also exist as a single integer that signifies the same value for all of the spatial domain. The amount of output data padding along any specified dimension should be given less than the stride along the same dimension. By default, it is set to None, which states that the output shape is inferred.**data_format:**It is in either mode, i.e.,**'channels_first'**that corresponds to input shape:**(batch, channels, height, width)**or**'channels_last'**corresponds to**(batch, height, width, channels)**. It defaults to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder, then it is residing at "channels_last".**dilation_rate:**It can be an integer or tuple/ list of 2 integers that relates to the dilation rate to be used for dilated convolution. If we specify any**stride**value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**activation:**It refers to an activation function to be used. When nothing is specified, then by defaults, it is a linear activation**a(x) = x**, or we can say no activation function is applied.**use_bias:**It represents a Boolean that shows whether the layer utilizes a bias vector.**kernel_initializer:**It can be defined as an initializer for the**kernel**weights matrix.**bias_initializer:**It refers to an initializer for bias vector.**kernel_regularizer:**It refers to a regularizer function, which is applied to the**kernel**weights matrix.**bias_regularizer:**It can be defined as a regularizer function, which is applied to the bias vector.**activity_regularizer:**It refers to a regularizer function that is applied to the activation (output of the layer).**kernel_constraint:**It is a constraint function applied to the kernel matrix.**bias_constraint:**It can be defined as a constraint function applied to the bias vector.
If the
If the ## Conv3DIt is a 3D convolution layer; for example, spatial convolution over volumes helps in the creation of the convolution kernel, which is convolved with the input layer in order to generate outputs of a tensor. It creates a bias vector if the The layer can be used as the first layer in the model by using the
**filter:**It is an integer that signifies the output space dimensionality or a total number of output filters present in a convolution.**kernel_size:**It can either be an integer or tuple/list of 3 integers to represent the depth, height, and width of a 3D convolution window. It can also exist as a single integer that signifies the same value for all of the spatial domain.**strides:**It is either an integer or a tuple/list of 3 integers that represents the convolution strides along with the depth, height, and width. It can exist as a single integer that signifies the same value for rest all of the spatial domain. If we specify any stride value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**padding:**One of**"valid"**or**"same,"**where the same shows some inconsistency across the backend with**strides**!=1.**data_format:**It is in either mode, i.e.**'channels_first'**that corresponds to input shape:**(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)**or**'channels_last'**corresponds to**(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)**. It defaults to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder, then it is residing at "channels_last".**dilation_rate:**It can be an integer or tuple/ list of 3 integers that relates to the dilation rate to be used for dilated convolution. If we specify any**stride**value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**activation:**It refers to an activation function to be used. When nothing is specified, then by defaults, it is a linear activation**a(x) = x**, or we can say no activation function is applied.**use_bias:**It represents a Boolean that shows whether the layer utilizes a bias vector.**kernel_initializer:**It can be defined as an initializer for the**kernel**weights matrix.**bias_initializer:**It refers to an initializer for bias vector.**kernel_regularizer:**It refers to a regularizer function, which is applied to the**kernel**weights matrix.**bias_regularizer:**It can be defined as a regularizer function, which is applied to the bias vector.**activity_regularizer:**It refers to a regularizer function that is applied to the activation (output of the layer).**kernel_constraint:**It is a constraint function applied to the kernel matrix.**bias_constraint:**It can be defined as a constraint function applied to the bias vector.
If the
If the ## Conv3D TransposeIt is a transposed convolution layer, which is sometimes also called as Deconvolution. This layer is mainly required when the transformation moves in the opposite direction to that of a normal convolution, or simply we can say when the transformation goes from something that has an output shape of some convolution to the one that has input shape of convolution. The layer can be used as an initial layer by using an argument
**filter:**It is an integer that signifies the output space dimensionality or a total number of output filters present in a convolution.**kernel_size:**It can either be an integer or tuple/list of 3 integers to represent the depth, height, and width of a 3D convolution window. It can also exist as a single integer that signifies the same value for all of the spatial domain.**strides:**It is either an integer or a tuple/list of 3 integers that represents the convolution strides along with the depth, height, and width. It can exist as a single integer that signifies the same value for rest all of the spatial domain. If we specify any stride value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**padding:**One of**"valid"**or**"same,"**where the same shows some inconsistency across the backend with**strides**!=1.**output_padding:**It can either be an integer or tuple/list of 3 integers to represent the depth, height, and width of a 3D convolution window. It can also exist as a single integer that signifies the same value for all of the spatial domain. The amount of output data padding along any specified dimension should be given less than the stride along the same dimension. By default, it is set to None, which states that the output shape is inferred.**data_format:**It is in either mode, i.e.,**'channels_first'**that corresponds to input shape:**(batch, channels, depth, height, width)**or**'channels_last'**corresponds to**(batch, depth, height, width, channels)**. It defaults to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder, then it is residing at "channels_last".**dilation_rate:**It can be an integer or tuple/ list of 3 integers that relates to the dilation rate to be used for dilated convolution. If we specify any**stride**value!=1, it relates to its incompatibility with specifying the**dilation_rate**value!=1.**activation:**It refers to an activation function to be used. When nothing is specified, then by defaults, it is a linear activation**a(x) = x**, or we can say no activation function is applied.**use_bias:**It represents a Boolean that shows whether the layer utilizes a bias vector.**kernel_initializer:**It can be defined as an initializer for the**kernel**weights matrix.**bias_initializer:**It refers to an initializer for bias vector.**kernel_regularizer:**It refers to a regularizer function, which is applied to the**kernel**weights matrix.**bias_regularizer:**It can be defined as a regularizer function, which is applied to the bias vector.**activity_regularizer:**It refers to a regularizer function that is applied to the activation (output of the layer).**kernel_constraint:**It is a constraint function applied to the kernel matrix.**bias_constraint:**It can be defined as a constraint function applied to the bias vector.
If the
If the ## Cropping1DIt is a cropping layer for 1Dimension input, for example, a temporal sequence that crops alongside axis 1, i.e., time dimension.
**cropping:**It is a tuple, which is of int length 2 ensures a total number of units to be trimmed at the beginning and end of axis 1(cropping dimension). In case if you provide a single int, then the same value will be utilized at the beginning and end.
It is a 3D tensor of shape
It is a 3D tensor of shape ## Cropping2DIt is a 2Dimension cropping layer for example picture that yields along the spatial dimensions such as height and width.
**cropping:**It is a int, or tuple of 2 ints, or a tuple of 2 tuples of 2 int, such that**if int**, which is the same cropping symmetric is applied to height and width and**if tuple of 2 int**is interpreted as two different symmetric cropping value for height and width:**(symmetric_height_crop, symmetric_width_crop)****data_format:**It is in either mode, i.e.**'channels_first'**that corresponds to input shape:**(batch, channels, height, width)**or**'channels_last'**corresponding to**(batch, height, width, channels)**. It is default to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder then it is residing at "channels_last".
If the
If the
## Cropping3DIt is a 3D cropping layer just like spatio-temporal or spatial.
**cropping:**It is an int, or a tuple of 3 ints, or a tuple of 3 tuples of 2ints, such that;**If int**is the same symmetric cropping that is applied to depth, height and width,**If tuple of 3 ints**is interpreted as three distinct values of symmetric cropping for depth, height and width:**(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)**and If tuple of 3 tuples of 2 ints is interpreted as**((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))**.**data_format:**It is a string of either mode, i.e.**'channels_first'**that corresponds to input shape:**(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)**or**'channels_last'**corresponding to**(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)**. It is default to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder then it is residing at "channels_last".
If the
If the ## UpSampling1DIt is an Upsampling layer for 1 Dimensional inputs that repeat each individual temporal steps in terms of
**size:**It is an integer, which is an Upsampling factor.
It is a 3D tensor of shape:
It is a 3D tensor with shape: ## UpSampling2DIt is an Upsampling layer for 2D input that repeats the rows of the data by size [0] and columns of the data by size [1].
**size:**It is an int or tuple of 2 integers, which is an upsampling factor for rows and columns.**data_format:**It is a string of either mode, i.e.,**'channels_first'**that corresponds to input shape:**(batch, channels, height, width)**or**'channels_last'**corresponding to**(batch, height, width, channels)**. It defaults to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder, then it is residing at "channels_last".**interpolation:**It is a string one of**nearest**or**bilinear**. It should be illustrated that CNTK does not support yet the**bilinear**upscaling and that with Theano, only size=(2, 2) is possible.
If
If ## UpSampling3DIt refers to an Upsampling layer for 3 dimensional input that repeats 1
**size:**It is an int or tuple of 3 integers, which is an upsampling factor for dim1, dim2, and dim3.**data_format:**It is a string of either mode, i.e.**'channels_first'**that corresponds to input shape:**(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)**or**'channels_last'**corresponding to**(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)**. It defaults to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder, then it is residing at "channels_last".
If
If ## ZeroPadding1DIt refers to a zero-padding layer for one-dimensional input. For example, a temporal sequence.
**padding:**It is an int, or tuple of int (length 2) or dictionary, such that**If int**demonstrates a total number of zeros to be added at the beginning as well as at the end of the padding dimension(axis 1), whereas in case of**If a tuple of int**(length 2) the zeros are added at the beginning and end of the padding dimension ((left_pad, right_pad)).
It is a 3D tensor of shape
It refers to a 3 dimensional tensor of shape ## ZeroPadding2DIt refers to a two-dimensional input zero-padding layer (for example, picture) that supports the addition of zero rows and columns containing zeros at the top, bottom, left, and right of a tensor image.
**padding:**It is an int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints; where**If int**is the similar symmetric padding being applied to height and width,**If a tuple of 2 ints**is taken as two distinct values symmetric padding values for height and width of:**(symmetric_height_pad, symmetric_width_pad)**, whereas**If a tuple of 2 tuples of 2 ints**is understood as**((top_pad, bottom_pad), (left_pad, right_pad))**.**data_format:**It is a string of either mode, i.e.,**'channels_first'**that corresponds to input shape:**(batch, channels, height, width)**or**'channels_last'**corresponding to**(batch, height, width, channels)**. It defaults to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder, then it is residing at "channels_last".
If
If ## ZeroPadding3DIt is a three-dimensional zero-padding layer. For example, spatial or Spatio-temporal.
**padding:**It is an int, or a tuple of 3 ints, or a tuple of 3 tuples of 2ints, such that;**If int**is the same symmetric padding that is applied to depth, height and width,**If tuple of 3 ints**is interpreted as three distinct values of symmetric padding values for depth, height and width:**(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)**and**If tuple of 3 tuples of 2 ints**is interpreted as**((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))**.**data_format:**It is a string of either mode, i.e.**'channels_first'**that corresponds to input shape:**(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)**or**'channels_last'**corresponding to**(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)**. It is default to the**image_data_format**value that is found in Keras config at**~/.keras/keras.json**. If you cannot find it in that folder then it is residing at "channels_last".
If the
If the Next TopicPooling Layers |