tf.compat.v1.layers.conv3d_transpose

Functional interface for transposed 3D convolution layer.

tf.compat.v1.layers.conv3d_transpose(
    inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid',
    data_format='channels_last', activation=None, use_bias=True,
    kernel_initializer=None, bias_initializer=tf.zeros_initializer(),
    kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,
    kernel_constraint=None, bias_constraint=None, trainable=True, name=None,
    reuse=None
)

Arguments
`inputs`	Input tensor.
`filters`	Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
`kernel_size`	A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
`strides`	A tuple or list of 3 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
`padding`	one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
`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)`.
`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.

Returns
Output tensor.

Raises
`ValueError`	if eager execution is enabled.

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/compat/v1/layers/conv3d_transpose