tf.contrib.layers.conv2d_transpose

Aliases:

• tf.contrib.layers.conv2d_transpose
• tf.contrib.layers.convolution2d_transpose

tf.contrib.layers.conv2d_transpose(
    inputs,
    num_outputs,
    kernel_size,
    stride=1,
    padding='SAME',
    data_format=DATA_FORMAT_NHWC,
    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
)

Defined in tensorflow/contrib/layers/python/layers/layers.py.

See the guide: Layers (contrib) > Higher level ops for building neural network layers

Adds a convolution2d_transpose with an optional batch normalization layer.

The function creates a variable called weights, representing the kernel, that is convolved with the input. If normalizer_fn is None, a second variable called 'biases' is added to the result of the operation.

Args:

• inputs: A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
• num_outputs: Integer, the number of output filters.
• kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of of the filters. Can be an int if both values are the same.
• stride: A list of length 2: [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. NHWC (default) and NCHW 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 2.
• ValueError: If data_format is neither NHWC nor NCHW.
• ValueError: If C dimension of inputs is None.