$OpDocNNAddBias
$OpDocNNAddBias
Bias tensor that must be one-dimensional (i.e., it must have rank 1).
Data format of the input and output tensors. With the default format NWCFormat, the
bias
tensor will be added to the last dimension of the value
tensor. Alternatively, the
format could be NCWFormat, and the bias
tensor would be added to the third-to-last
dimension.
Result as a new tensor.
$OpDocConv2D
$OpDocConv2D
4-D tensor with shape [filterHeight, filterWidth, inChannels, outChannels]
.
Stride of the sliding window along the second dimension of this tensor.
Stride of the sliding window along the third dimension of this tensor.
Padding mode to use.
Format of the input and output data.
The dilation factor for each dimension of input. 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 dataFormat
. Dilations in the batch and depth dimensions must
be set to 1
.
Boolean value indicating whether or not to use CuDNN for the created op, if its placed on a GPU, as opposed to the TensorFlow implementation.
Result as a new 4-D tensor whose dimension order depends on the value of dataFormat
.
$OpDocNNCrelu
$OpDocNNCrelu
Result as a new tensor.
$OpDocNNDropout
$OpDocNNDropout
Probability (i.e., number in the interval (0, 1]
) that each element is kept.
If true
, the outputs will be divided by the keep probability.
INT32 rank-1 tensor representing the shape for the randomly generated keep/drop flags.
Optional random seed, used to generate a random seed pair for the random number generator, when combined with the graph-level seed.
Result as a new tensor that has the same shape as input
.
$OpDocNNElu
$OpDocNNElu
Result as a new tensor.
$OpDocNNInTopK
$OpDocNNL2Normalize
$OpDocNNL2Normalize
Tensor containing the axes along which to normalize.
Lower bound value for the norm. The created op will use sqrt(epsilon)
as the divisor, if
norm < sqrt(epsilon)
.
Result as a new tensor.
$OpDocNNLinear
$OpDocNNLinear
Weights tensor.
Bias tensor.
Result as a new tensor.
$OpDocNNLogSoftmax
$OpDocNNLogSoftmax
Axis along which to perform the log-softmax. Defaults to -1
denoting the last axis.
Result as a new tensor.
$OpDocMaxPool
$OpDocMaxPool
The size of the pooling window for each dimension of the input tensor.
Stride of the sliding window along the second dimension of input
.
Stride of the sliding window along the third dimension of input
.
Padding mode to use.
Format of the input and output data.
Result as a new 4-D tensor whose dimension order depends on the value of dataFormat
.
$OpDocNNRelu
$OpDocNNRelu
Slope of the negative section, also known as leakage parameter. If other than 0.0f
, the negative
part will be equal to alpha * x
instead of 0
. Defaults to 0
.
Result as a new tensor.
$OpDocNNRelu6
$OpDocNNRelu6
Result as a new tensor.
$OpDocNNSelu
$OpDocNNSelu
Result as a new tensor.
$OpDocNNSoftmax
$OpDocNNSoftmax
Axis along which to perform the softmax. Defaults to -1
denoting the last axis.
Result as a new tensor.
$OpDocNNSoftplus
$OpDocNNSoftplus
Result as a new tensor.
$OpDocNNSoftsign
$OpDocNNSoftsign
Result as a new tensor.
$OpDocNNTopK
$OpDocNNTopK
Scalar INT32 tensor containing the number of top elements to look for along the last axis of
input
.
If true
, the resulting k
elements will be sorted by their values in descending order.
Tuple containing the created op outputs: (i) values
: the k
largest elements along each last
dimensional slice, and (ii) indices
: the indices of values
within the last axis of input
.