tf.nn.conv2d_transpose | TensorFlow v2.16.1 (original) (raw)
tf.nn.conv2d_transpose
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The transpose of conv2d.
tf.nn.conv2d_transpose(
input,
filters,
output_shape,
strides,
padding='SAME',
data_format='NHWC',
dilations=None,
name=None
)
This operation is sometimes called "deconvolution" after (Zeiler et al., 2010), but is really the transpose (gradient) ofatrous_conv2d rather than an actual deconvolution.
| Args | |
|---|---|
| input | 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. |
| filters | A 4-D Tensor with the same type as input and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of input. |
| output_shape | A 1-D Tensor representing the output shape of the deconvolution op. |
| strides | An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details. |
| padding | Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. Seeherefor more information. When explicit padding is used and data_format is"NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]. |
| data_format | A string. 'NHWC' and 'NCHW' are supported. |
| dilations | An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1. |
| name | Optional name for the returned tensor. |
| Returns |
|---|
| A Tensor with the same type as input. |
| Raises | |
|---|---|
| ValueError | If input/output depth does not match filter's shape, or if padding is other than 'VALID' or 'SAME'. |
| References |
|---|
| Deconvolutional Networks:Zeiler et al., 2010(pdf) |
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Last updated 2024-04-26 UTC.