contrib.layers.conv2d_transpose
tf.contrib.layers.conv2d_transpose
tf.contrib.layers.conv2d_transpose
tf.contrib.layers.convolution2d_transpose
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-DTensor
of typefloat
and shape[batch, height, width, in_channels]
forNHWC
data format or[batch, in_channels, height, width]
forNCHW
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) andNCHW
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 ofbiases
. Ifnormalizer_fn
is provided thenbiases_initializer
andbiases_regularizer
are ignored andbiases
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
: Ifdata_format
is neitherNHWC
norNCHW
. -
ValueError
: IfC
dimension ofinputs
is None.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/layers/conv2d_transpose