tf.contrib.layers.conv2d_transpose

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Adds a convolution2d_transpose with an optional batch normalization layer.

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
)

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.

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.

A tensor representing the output of the operation.

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.