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tf.contrib.layers.conv3d_transpose

View source on GitHub

Adds a convolution3d_transpose with an optional batch normalization layer.

Aliases:

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.