Adds a convolution3d_transpose with an optional batch normalization layer.
View aliases
Main aliases
`tf.contrib.layers.convolution3d_transpose`
tf.contrib.layers.conv3d_transpose(
inputs, num_outputs, kernel_size, stride=1, padding='SAME',
data_format=DATA_FORMAT_NDHWC, activation_fn=tf.nn.relu, normalizer_fn=None,
normalizer_params=None, weights_initializer=initializers.xavier_initializer(),
weights_regularizer=None, biases_initializer=tf.zeros_initializer(),
biases_regularizer=None, reuse=None, variables_collections=None,
outputs_collections=None, trainable=True, scope=None
)
The function creates a variable called weights
, representing the
kernel, that is convolved with the input. If batch_norm_params
is None
, a
second variable called 'biases' is added to the result of the operation.
Args:
inputs: A 5-D Tensor
of type float
and shape [batch, depth, height,
width, in_channels]
for NDHWC
data format or [batch, in_channels,
depth, height, width]
for NCDHW
data format.
num_outputs: Integer, the number of output filters.
kernel_size: A list of length 3 holding the [kernel_depth, kernel_height,
kernel_width] of the filters. Can be an int if both values are the same.
stride: A list of length 3: [stride_depth, stride_height, stride_width]. Can
be an int if both strides are the same. Note that presently both strides
must have the same value.
padding: One of 'VALID' or 'SAME'.
data_format: A string. NDHWC
(default) and NCDHW
are supported.
activation_fn: Activation function. The default value is a ReLU function.
Explicitly set it to None to skip it and maintain a linear activation.
normalizer_fn: Normalization function to use instead of biases
. If
normalizer_fn
is provided then biases_initializer
and
biases_regularizer
are ignored and biases
are not created nor added.
default set to None for no normalizer function
normalizer_params: Normalization function parameters.
weights_initializer: An initializer for the weights.
weights_regularizer: Optional regularizer for the weights.
biases_initializer: An initializer for the biases. If None skip biases.
biases_regularizer: Optional regularizer for the biases.
reuse: Whether or not the layer and its variables should be reused. To be
able to reuse the layer scope must be given.
variables_collections: Optional list of collections for all the variables or
a dictionary containing a different list of collection per variable.
outputs_collections: Collection to add the outputs.
trainable: Whether or not the variables should be trainable or not.
scope: Optional scope for variable_scope.
Returns |
A tensor representing the output of the operation.
|
Raises |
ValueError
|
If 'kernel_size' is not a list of length 3.
|
ValueError
|
If data_format is neither NDHWC nor NCDHW .
|
ValueError
|
If C dimension of inputs is None.
|