Uses of Package
org.tensorflow.op.nn

Packages that use org.tensorflow.op.nn

Package	Description
org.tensorflow.op
org.tensorflow.op.nn

Classes in org.tensorflow.op.nn used by org.tensorflow.op

Class and Description
AvgPool Performs average pooling on the input.
AvgPool.Options Optional attributes for AvgPool
AvgPool3d Performs 3D average pooling on the input.
AvgPool3d.Options Optional attributes for AvgPool3d
AvgPool3dGrad Computes gradients of average pooling function.
AvgPool3dGrad.Options Optional attributes for AvgPool3dGrad
BatchNormWithGlobalNormalization Batch normalization.
BatchNormWithGlobalNormalizationGrad Gradients for batch normalization.
BiasAdd Adds `bias` to `value`.
BiasAdd.Options Optional attributes for BiasAdd
BiasAddGrad The backward operation for "BiasAdd" on the "bias" tensor.
BiasAddGrad.Options Optional attributes for BiasAddGrad
ComputeAccidentalHits Computes the ids of the positions in sampled_candidates that match true_labels.
ComputeAccidentalHits.Options Optional attributes for ComputeAccidentalHits
Conv2d Computes a 2-D convolution given 4-D `input` and `filter` tensors.
Conv2d.Options Optional attributes for Conv2d
Conv2dBackpropFilter Computes the gradients of convolution with respect to the filter.
Conv2dBackpropFilter.Options Optional attributes for Conv2dBackpropFilter
Conv2dBackpropInput Computes the gradients of convolution with respect to the input.
Conv2dBackpropInput.Options Optional attributes for Conv2dBackpropInput
Conv3d Computes a 3-D convolution given 5-D `input` and `filter` tensors.
Conv3d.Options Optional attributes for Conv3d
Conv3dBackpropFilter Computes the gradients of 3-D convolution with respect to the filter.
Conv3dBackpropFilter.Options Optional attributes for Conv3dBackpropFilter
Conv3dBackpropInput Computes the gradients of 3-D convolution with respect to the input.
Conv3dBackpropInput.Options Optional attributes for Conv3dBackpropInput
CtcBeamSearchDecoder Performs beam search decoding on the logits given in input.
CtcBeamSearchDecoder.Options Optional attributes for CtcBeamSearchDecoder
CtcGreedyDecoder Performs greedy decoding on the logits given in inputs.
CtcGreedyDecoder.Options Optional attributes for CtcGreedyDecoder
CtcLoss Calculates the CTC Loss (log probability) for each batch entry.
CtcLoss.Options Optional attributes for CtcLoss
CudnnRnnCanonicalToParams Converts CudnnRNN params from canonical form to usable form.
CudnnRnnCanonicalToParams.Options Optional attributes for CudnnRnnCanonicalToParams
CudnnRnnParamsSize Computes size of weights that can be used by a Cudnn RNN model.
CudnnRnnParamsSize.Options Optional attributes for CudnnRnnParamsSize
CudnnRnnParamsToCanonical Retrieves CudnnRNN params in canonical form.
CudnnRnnParamsToCanonical.Options Optional attributes for CudnnRnnParamsToCanonical
DataFormatDimMap Returns the dimension index in the destination data format given the one in
DataFormatDimMap.Options Optional attributes for DataFormatDimMap
DataFormatVecPermute Returns the permuted vector/tensor in the destination data format given the
DataFormatVecPermute.Options Optional attributes for DataFormatVecPermute
DepthToSpace DepthToSpace for tensors of type T.
DepthToSpace.Options Optional attributes for DepthToSpace
DepthwiseConv2dNative Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
DepthwiseConv2dNative.Options Optional attributes for DepthwiseConv2dNative
DepthwiseConv2dNativeBackpropFilter Computes the gradients of depthwise convolution with respect to the filter.
DepthwiseConv2dNativeBackpropFilter.Options Optional attributes for DepthwiseConv2dNativeBackpropFilter
DepthwiseConv2dNativeBackpropInput Computes the gradients of depthwise convolution with respect to the input.
DepthwiseConv2dNativeBackpropInput.Options Optional attributes for DepthwiseConv2dNativeBackpropInput
Dilation2d Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
Dilation2dBackpropFilter Computes the gradient of morphological 2-D dilation with respect to the filter.
Dilation2dBackpropInput Computes the gradient of morphological 2-D dilation with respect to the input.
Elu Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
FixedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
FixedUnigramCandidateSampler.Options Optional attributes for FixedUnigramCandidateSampler
FractionalAvgPool Performs fractional average pooling on the input.
FractionalAvgPool.Options Optional attributes for FractionalAvgPool
FractionalMaxPool Performs fractional max pooling on the input.
FractionalMaxPool.Options Optional attributes for FractionalMaxPool
FusedBatchNorm Batch normalization.
FusedBatchNorm.Options Optional attributes for FusedBatchNorm
FusedBatchNormGrad Gradient for batch normalization.
FusedBatchNormGrad.Options Optional attributes for FusedBatchNormGrad
FusedPadConv2d Performs a padding as a preprocess during a convolution.
FusedResizeAndPadConv2d Performs a resize and padding as a preprocess during a convolution.
FusedResizeAndPadConv2d.Options Optional attributes for FusedResizeAndPadConv2d
InTopK Says whether the targets are in the top `K` predictions.
L2Loss L2 Loss.
LearnedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
LearnedUnigramCandidateSampler.Options Optional attributes for LearnedUnigramCandidateSampler
LocalResponseNormalization Local Response Normalization.
LocalResponseNormalization.Options Optional attributes for LocalResponseNormalization
LogSoftmax Computes log softmax activations.
MaxPool Performs max pooling on the input.
MaxPool.Options Optional attributes for MaxPool
MaxPool3d Performs 3D max pooling on the input.
MaxPool3d.Options Optional attributes for MaxPool3d
MaxPool3dGrad Computes gradients of max pooling function.
MaxPool3dGrad.Options Optional attributes for MaxPool3dGrad
MaxPool3dGradGrad Computes second-order gradients of the maxpooling function.
MaxPool3dGradGrad.Options Optional attributes for MaxPool3dGradGrad
MaxPoolGrad Computes gradients of the maxpooling function.
MaxPoolGrad.Options Optional attributes for MaxPoolGrad
MaxPoolGradGrad Computes second-order gradients of the maxpooling function.
MaxPoolGradGrad.Options Optional attributes for MaxPoolGradGrad
MaxPoolGradGradWithArgmax Computes second-order gradients of the maxpooling function.
MaxPoolGradGradWithArgmax.Options Optional attributes for MaxPoolGradGradWithArgmax
MaxPoolWithArgmax Performs max pooling on the input and outputs both max values and indices.
MaxPoolWithArgmax.Options Optional attributes for MaxPoolWithArgmax
NthElement Finds values of the `n`-th order statistic for the last dimension.
NthElement.Options Optional attributes for NthElement
QuantizedAvgPool Produces the average pool of the input tensor for quantized types.
QuantizedBatchNormWithGlobalNormalization Quantized Batch normalization.
QuantizedBiasAdd Adds Tensor 'bias' to Tensor 'input' for Quantized types.
QuantizedConv2d Computes a 2D convolution given quantized 4D input and filter tensors.
QuantizedConv2d.Options Optional attributes for QuantizedConv2d
QuantizedInstanceNorm Quantized Instance normalization.
QuantizedInstanceNorm.Options Optional attributes for QuantizedInstanceNorm
QuantizedMaxPool Produces the max pool of the input tensor for quantized types.
QuantizedRelu Computes Quantized Rectified Linear: `max(features, 0)`
QuantizedRelu6 Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
QuantizedReluX Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
Relu Computes rectified linear: `max(features, 0)`.
Relu6 Computes rectified linear 6: `min(max(features, 0), 6)`.
Selu Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
Softmax Computes softmax activations.
SoftmaxCrossEntropyWithLogits Computes softmax cross entropy cost and gradients to backpropagate.
Softsign Computes softsign: `features / (abs(features) + 1)`.
SpaceToBatch SpaceToBatch for 4-D tensors of type T.
SpaceToDepth SpaceToDepth for tensors of type T.
SpaceToDepth.Options Optional attributes for SpaceToDepth
SparseSoftmaxCrossEntropyWithLogits Computes softmax cross entropy cost and gradients to backpropagate.
TopK Finds values and indices of the `k` largest elements for the last dimension.
TopK.Options Optional attributes for TopK

Classes in org.tensorflow.op.nn used by org.tensorflow.op.nn

Class and Description
AvgPool Performs average pooling on the input.
AvgPool.Options Optional attributes for AvgPool
AvgPool3d Performs 3D average pooling on the input.
AvgPool3d.Options Optional attributes for AvgPool3d
AvgPool3dGrad Computes gradients of average pooling function.
AvgPool3dGrad.Options Optional attributes for AvgPool3dGrad
AvgPoolGrad Computes gradients of the average pooling function.
AvgPoolGrad.Options Optional attributes for AvgPoolGrad
BatchNormWithGlobalNormalization Batch normalization.
BatchNormWithGlobalNormalizationGrad Gradients for batch normalization.
BiasAdd Adds `bias` to `value`.
BiasAdd.Options Optional attributes for BiasAdd
BiasAddGrad The backward operation for "BiasAdd" on the "bias" tensor.
BiasAddGrad.Options Optional attributes for BiasAddGrad
ComputeAccidentalHits Computes the ids of the positions in sampled_candidates that match true_labels.
ComputeAccidentalHits.Options Optional attributes for ComputeAccidentalHits
Conv2d Computes a 2-D convolution given 4-D `input` and `filter` tensors.
Conv2d.Options Optional attributes for Conv2d
Conv2dBackpropFilter Computes the gradients of convolution with respect to the filter.
Conv2dBackpropFilter.Options Optional attributes for Conv2dBackpropFilter
Conv2dBackpropInput Computes the gradients of convolution with respect to the input.
Conv2dBackpropInput.Options Optional attributes for Conv2dBackpropInput
Conv3d Computes a 3-D convolution given 5-D `input` and `filter` tensors.
Conv3d.Options Optional attributes for Conv3d
Conv3dBackpropFilter Computes the gradients of 3-D convolution with respect to the filter.
Conv3dBackpropFilter.Options Optional attributes for Conv3dBackpropFilter
Conv3dBackpropInput Computes the gradients of 3-D convolution with respect to the input.
Conv3dBackpropInput.Options Optional attributes for Conv3dBackpropInput
CtcBeamSearchDecoder Performs beam search decoding on the logits given in input.
CtcBeamSearchDecoder.Options Optional attributes for CtcBeamSearchDecoder
CtcGreedyDecoder Performs greedy decoding on the logits given in inputs.
CtcGreedyDecoder.Options Optional attributes for CtcGreedyDecoder
CtcLoss Calculates the CTC Loss (log probability) for each batch entry.
CtcLoss.Options Optional attributes for CtcLoss
CudnnRnn A RNN backed by cuDNN.
CudnnRnn.Options Optional attributes for CudnnRnn
CudnnRnnBackprop Backprop step of CudnnRNN.
CudnnRnnBackprop.Options Optional attributes for CudnnRnnBackprop
CudnnRnnCanonicalToParams Converts CudnnRNN params from canonical form to usable form.
CudnnRnnCanonicalToParams.Options Optional attributes for CudnnRnnCanonicalToParams
CudnnRnnParamsSize Computes size of weights that can be used by a Cudnn RNN model.
CudnnRnnParamsSize.Options Optional attributes for CudnnRnnParamsSize
CudnnRnnParamsToCanonical Retrieves CudnnRNN params in canonical form.
CudnnRnnParamsToCanonical.Options Optional attributes for CudnnRnnParamsToCanonical
DataFormatDimMap Returns the dimension index in the destination data format given the one in
DataFormatDimMap.Options Optional attributes for DataFormatDimMap
DataFormatVecPermute Returns the permuted vector/tensor in the destination data format given the
DataFormatVecPermute.Options Optional attributes for DataFormatVecPermute
DepthToSpace DepthToSpace for tensors of type T.
DepthToSpace.Options Optional attributes for DepthToSpace
DepthwiseConv2dNative Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
DepthwiseConv2dNative.Options Optional attributes for DepthwiseConv2dNative
DepthwiseConv2dNativeBackpropFilter Computes the gradients of depthwise convolution with respect to the filter.
DepthwiseConv2dNativeBackpropFilter.Options Optional attributes for DepthwiseConv2dNativeBackpropFilter
DepthwiseConv2dNativeBackpropInput Computes the gradients of depthwise convolution with respect to the input.
DepthwiseConv2dNativeBackpropInput.Options Optional attributes for DepthwiseConv2dNativeBackpropInput
Dilation2d Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
Dilation2dBackpropFilter Computes the gradient of morphological 2-D dilation with respect to the filter.
Dilation2dBackpropInput Computes the gradient of morphological 2-D dilation with respect to the input.
Elu Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
EluGrad Computes gradients for the exponential linear (Elu) operation.
FixedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
FixedUnigramCandidateSampler.Options Optional attributes for FixedUnigramCandidateSampler
FractionalAvgPool Performs fractional average pooling on the input.
FractionalAvgPool.Options Optional attributes for FractionalAvgPool
FractionalAvgPoolGrad Computes gradient of the FractionalAvgPool function.
FractionalAvgPoolGrad.Options Optional attributes for FractionalAvgPoolGrad
FractionalMaxPool Performs fractional max pooling on the input.
FractionalMaxPool.Options Optional attributes for FractionalMaxPool
FractionalMaxPoolGrad Computes gradient of the FractionalMaxPool function.
FractionalMaxPoolGrad.Options Optional attributes for FractionalMaxPoolGrad
FusedBatchNorm Batch normalization.
FusedBatchNorm.Options Optional attributes for FusedBatchNorm
FusedBatchNormGrad Gradient for batch normalization.
FusedBatchNormGrad.Options Optional attributes for FusedBatchNormGrad
FusedPadConv2d Performs a padding as a preprocess during a convolution.
FusedResizeAndPadConv2d Performs a resize and padding as a preprocess during a convolution.
FusedResizeAndPadConv2d.Options Optional attributes for FusedResizeAndPadConv2d
InTopK Says whether the targets are in the top `K` predictions.
InvGrad Computes the gradient for the inverse of `x` wrt its input.
L2Loss L2 Loss.
LeakyRelu Computes rectified linear: `max(features, features * alpha)`.
LeakyRelu.Options Optional attributes for LeakyRelu
LearnedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
LearnedUnigramCandidateSampler.Options Optional attributes for LearnedUnigramCandidateSampler
LocalResponseNormalization Local Response Normalization.
LocalResponseNormalization.Options Optional attributes for LocalResponseNormalization
LocalResponseNormalizationGrad Gradients for Local Response Normalization.
LocalResponseNormalizationGrad.Options Optional attributes for LocalResponseNormalizationGrad
LogSoftmax Computes log softmax activations.
MaxPool Performs max pooling on the input.
MaxPool.Options Optional attributes for MaxPool
MaxPool3d Performs 3D max pooling on the input.
MaxPool3d.Options Optional attributes for MaxPool3d
MaxPool3dGrad Computes gradients of max pooling function.
MaxPool3dGrad.Options Optional attributes for MaxPool3dGrad
MaxPool3dGradGrad Computes second-order gradients of the maxpooling function.
MaxPool3dGradGrad.Options Optional attributes for MaxPool3dGradGrad
MaxPoolGrad Computes gradients of the maxpooling function.
MaxPoolGrad.Options Optional attributes for MaxPoolGrad
MaxPoolGradGrad Computes second-order gradients of the maxpooling function.
MaxPoolGradGrad.Options Optional attributes for MaxPoolGradGrad
MaxPoolGradGradWithArgmax Computes second-order gradients of the maxpooling function.
MaxPoolGradGradWithArgmax.Options Optional attributes for MaxPoolGradGradWithArgmax
MaxPoolGradWithArgmax Computes gradients of the maxpooling function.
MaxPoolGradWithArgmax.Options Optional attributes for MaxPoolGradWithArgmax
MaxPoolWithArgmax Performs max pooling on the input and outputs both max values and indices.
MaxPoolWithArgmax.Options Optional attributes for MaxPoolWithArgmax
NthElement Finds values of the `n`-th order statistic for the last dimension.
NthElement.Options Optional attributes for NthElement
QuantizedAvgPool Produces the average pool of the input tensor for quantized types.
QuantizedBatchNormWithGlobalNormalization Quantized Batch normalization.
QuantizedBiasAdd Adds Tensor 'bias' to Tensor 'input' for Quantized types.
QuantizedConv2d Computes a 2D convolution given quantized 4D input and filter tensors.
QuantizedConv2d.Options Optional attributes for QuantizedConv2d
QuantizedInstanceNorm Quantized Instance normalization.
QuantizedInstanceNorm.Options Optional attributes for QuantizedInstanceNorm
QuantizedMaxPool Produces the max pool of the input tensor for quantized types.
QuantizedRelu Computes Quantized Rectified Linear: `max(features, 0)`
QuantizedRelu6 Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
QuantizedReluX Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
Relu Computes rectified linear: `max(features, 0)`.
Relu6 Computes rectified linear 6: `min(max(features, 0), 6)`.
Relu6Grad Computes rectified linear 6 gradients for a Relu6 operation.
ReluGrad Computes rectified linear gradients for a Relu operation.
Selu Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
SeluGrad Computes gradients for the scaled exponential linear (Selu) operation.
Softmax Computes softmax activations.
SoftmaxCrossEntropyWithLogits Computes softmax cross entropy cost and gradients to backpropagate.
Softsign Computes softsign: `features / (abs(features) + 1)`.
SoftsignGrad Computes softsign gradients for a softsign operation.
SpaceToBatch SpaceToBatch for 4-D tensors of type T.
SpaceToDepth SpaceToDepth for tensors of type T.
SpaceToDepth.Options Optional attributes for SpaceToDepth
SparseSoftmaxCrossEntropyWithLogits Computes softmax cross entropy cost and gradients to backpropagate.
TopK Finds values and indices of the `k` largest elements for the last dimension.
TopK.Options Optional attributes for TopK