Computes the gradients of convolution with respect to the input.
tf.compat.v1.nn.conv2d_backprop_input(
input_sizes, filter=None, out_backprop=None, strides=None, padding=None,
use_cudnn_on_gpu=True, data_format='NHWC', dilations=[1, 1, 1, 1],
name=None, filters=None
)
Args |
|---|
input_sizes
|
A Tensor of type int32.
An integer vector representing the shape of input,
where input is a 4-D [batch, height, width, channels] tensor.
|
filter
|
A Tensor. Must be one of the following types:
half, bfloat16, float32, float64.
4-D with shape
[filter_height, filter_width, in_channels, out_channels].
|
out_backprop
|
A Tensor. Must have the same type as filter.
4-D with shape [batch, out_height, out_width, out_channels].
Gradients w.r.t. the output of the convolution.
|
strides
|
A list of ints.
The stride of the sliding window for each dimension of the input
of the convolution. Must be in the same order as the dimension specified
with format.
|
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. 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]].
</td>
</tr><tr>
<td>use_cudnn_on_gpu</td>
<td>
An optionalbool. Defaults toTrue.
</td>
</tr><tr>
<td>data_format</td>
<td>
An optionalstringfrom:"NHWC", "NCHW".
Defaults to"NHWC".
Specify the data format of the input and output data. With the
default format "NHWC", the data is stored in the order of:
[batch, in_height, in_width, in_channels].
Alternatively, the format could be "NCHW", the data storage order of:
[batch, in_channels, in_height, in_width].
</td>
</tr><tr>
<td>dilations</td>
<td>
An optional list ofints. Defaults to[1, 1, 1, 1].
1-D tensor of length 4. The dilation factor for each dimension ofinput. 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 ofdata_format, see above for details. Dilations in the batch
and depth dimensions must be 1.
</td>
</tr><tr>
<td>name</td>
<td>
A name for the operation (optional).
</td>
</tr><tr>
<td>filters`
|
Alias for filter.
|
Returns |
|---|
A Tensor. Has the same type as filter.
|
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