This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
outputs. If use_bias is True (and a bias_initializer is provided),
a bias vector is created and added to the outputs. Finally, if
activation is not None, it is applied to the outputs as well.
Arguments
inputs
Tensor input.
filters
Integer, the dimensionality of the output space (i.e. the number
of 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).
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, depth, height, width, channels) while channels_first
corresponds to inputs with shape
(batch, channels, depth, height, width).
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. Set it to None to maintain a
linear activation.
use_bias
Boolean, whether the layer uses a bias.
kernel_initializer
An initializer for the convolution kernel.
bias_initializer
An initializer for the bias vector. If None, the default
initializer will be used.
kernel_regularizer
Optional regularizer for the convolution kernel.
bias_regularizer
Optional regularizer for the bias vector.
activity_regularizer
Optional regularizer function for the output.
kernel_constraint
Optional projection function to be applied to the
kernel after being updated by an Optimizer (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint
Optional projection function to be applied to the
bias after being updated by an Optimizer.
trainable
Boolean, if True also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
name
A string, the name of the layer.
reuse
Boolean, whether to reuse the weights of a previous layer
by the same name.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]