module MathFlow.TF.NN where
import GHC.TypeLits
import Data.Singletons
import Data.Singletons.TH
import Data.Promotion.Prelude
import MathFlow.Core
import MathFlow.PyString
allCandidateSampler' :: String -> String -> String -> String -> String -> String -> Tensor n t a
allCandidateSampler' true_classes num_true num_sampled unique seed name = TSym "tf.all_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique <+> TArgS "seed" seed <+> TArgS "name" name
allCandidateSampler :: String -> String -> String -> String -> Tensor n t a
allCandidateSampler true_classes num_true num_sampled unique = TSym "tf.all_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique
atrousConv2d' :: String -> String -> String -> String -> String -> Tensor n t a
atrousConv2d' value filters rate padding name = TSym "tf.atrous_conv2d" <+> TArgS "value" value <+> TArgS "filters" filters <+> TArgS "rate" rate <+> TArgS "padding" padding <+> TArgS "name" name
atrousConv2d :: String -> String -> String -> String -> Tensor n t a
atrousConv2d value filters rate padding = TSym "tf.atrous_conv2d" <+> TArgS "value" value <+> TArgS "filters" filters <+> TArgS "rate" rate <+> TArgS "padding" padding
atrousConv2dTranspose' :: String -> String -> String -> String -> String -> String -> Tensor n t a
atrousConv2dTranspose' value filters output_shape rate padding name = TSym "tf.atrous_conv2d_transpose" <+> TArgS "value" value <+> TArgS "filters" filters <+> TArgS "output_shape" output_shape <+> TArgS "rate" rate <+> TArgS "padding" padding <+> TArgS "name" name
atrousConv2dTranspose :: String -> String -> String -> String -> String -> Tensor n t a
atrousConv2dTranspose value filters output_shape rate padding = TSym "tf.atrous_conv2d_transpose" <+> TArgS "value" value <+> TArgS "filters" filters <+> TArgS "output_shape" output_shape <+> TArgS "rate" rate <+> TArgS "padding" padding
avgPool' :: SingI n => String -> String -> Sing n -> String -> String -> String -> Tensor n t a
avgPool' value ksize strides padding data_format name = TSym "tf.avg_pool" <+> TArgS "value" value <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
avgPool :: SingI n => String -> String -> Sing n -> String -> Tensor n t a
avgPool value ksize strides padding = TSym "tf.avg_pool" <+> TArgS "value" value <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding
avgPool3d' :: SingI n => String -> String -> Sing n -> String -> String -> String -> Tensor n t a
avgPool3d' input ksize strides padding data_format name = TSym "tf.avg_pool3d" <+> TArgS "input" input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
avgPool3d :: SingI n => String -> String -> Sing n -> String -> Tensor n t a
avgPool3d input ksize strides padding = TSym "tf.avg_pool3d" <+> TArgS "input" input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding
batchNormWithGlobalNormalization' :: String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
batchNormWithGlobalNormalization' t m v beta gamma variance_epsilon scale_after_normalization name = TSym "tf.batch_norm_with_global_normalization" <+> TArgS "t" t <+> TArgS "m" m <+> TArgS "v" v <+> TArgS "beta" beta <+> TArgS "gamma" gamma <+> TArgS "variance_epsilon" variance_epsilon <+> TArgS "scale_after_normalization" scale_after_normalization <+> TArgS "name" name
batchNormWithGlobalNormalization :: String -> String -> String -> String -> String -> String -> String -> Tensor n t a
batchNormWithGlobalNormalization t m v beta gamma variance_epsilon scale_after_normalization = TSym "tf.batch_norm_with_global_normalization" <+> TArgS "t" t <+> TArgS "m" m <+> TArgS "v" v <+> TArgS "beta" beta <+> TArgS "gamma" gamma <+> TArgS "variance_epsilon" variance_epsilon <+> TArgS "scale_after_normalization" scale_after_normalization
batchNormalization' :: Tensor n t a -> String -> String -> String -> String -> String -> String -> Tensor n t a
batchNormalization' x mean variance offset scale variance_epsilon name = TSym "tf.batch_normalization" <+> TArgT "x" x <+> TArgS "mean" mean <+> TArgS "variance" variance <+> TArgS "offset" offset <+> TArgS "scale" scale <+> TArgS "variance_epsilon" variance_epsilon <+> TArgS "name" name
batchNormalization :: Tensor n t a -> String -> String -> String -> String -> String -> Tensor n t a
batchNormalization x mean variance offset scale variance_epsilon = TSym "tf.batch_normalization" <+> TArgT "x" x <+> TArgS "mean" mean <+> TArgS "variance" variance <+> TArgS "offset" offset <+> TArgS "scale" scale <+> TArgS "variance_epsilon" variance_epsilon
biasAdd' :: String -> String -> String -> String -> Tensor n t a
biasAdd' value bias data_format name = TSym "tf.bias_add" <+> TArgS "value" value <+> TArgS "bias" bias <+> TArgS "data_format" data_format <+> TArgS "name" name
biasAdd :: String -> String -> Tensor n t a
biasAdd value bias = TSym "tf.bias_add" <+> TArgS "value" value <+> TArgS "bias" bias
bidirectionalDynamicRnn' :: String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
bidirectionalDynamicRnn' cell_fw cell_bw inputs sequence_length initial_state_fw initial_state_bw dtype parallel_iterations swap_memory time_major scope = TSym "tf.bidirectional_dynamic_rnn" <+> TArgS "cell_fw" cell_fw <+> TArgS "cell_bw" cell_bw <+> TArgS "inputs" inputs <+> TArgS "sequence_length" sequence_length <+> TArgS "initial_state_fw" initial_state_fw <+> TArgS "initial_state_bw" initial_state_bw <+> TArgS "dtype" dtype <+> TArgS "parallel_iterations" parallel_iterations <+> TArgS "swap_memory" swap_memory <+> TArgS "time_major" time_major <+> TArgS "scope" scope
bidirectionalDynamicRnn :: String -> String -> String -> Tensor n t a
bidirectionalDynamicRnn cell_fw cell_bw inputs = TSym "tf.bidirectional_dynamic_rnn" <+> TArgS "cell_fw" cell_fw <+> TArgS "cell_bw" cell_bw <+> TArgS "inputs" inputs
computeAccidentalHits' :: String -> String -> String -> String -> String -> Tensor n t a
computeAccidentalHits' true_classes sampled_candidates num_true seed name = TSym "tf.compute_accidental_hits" <+> TArgS "true_classes" true_classes <+> TArgS "sampled_candidates" sampled_candidates <+> TArgS "num_true" num_true <+> TArgS "seed" seed <+> TArgS "name" name
computeAccidentalHits :: String -> String -> String -> Tensor n t a
computeAccidentalHits true_classes sampled_candidates num_true = TSym "tf.compute_accidental_hits" <+> TArgS "true_classes" true_classes <+> TArgS "sampled_candidates" sampled_candidates <+> TArgS "num_true" num_true
conv1d' :: String -> String -> String -> String -> String -> String -> String -> Tensor n t a
conv1d' value filters stride padding use_cudnn_on_gpu data_format name = TSym "tf.conv1d" <+> TArgS "value" value <+> TArgS "filters" filters <+> TArgS "stride" stride <+> TArgS "padding" padding <+> TArgS "use_cudnn_on_gpu" use_cudnn_on_gpu <+> TArgS "data_format" data_format <+> TArgS "name" name
conv1d :: String -> String -> String -> String -> Tensor n t a
conv1d value filters stride padding = TSym "tf.conv1d" <+> TArgS "value" value <+> TArgS "filters" filters <+> TArgS "stride" stride <+> TArgS "padding" padding
conv2d' :: SingI n => String -> Tensor n t a -> Sing n -> String -> String -> String -> String -> Tensor n t a
conv2d' input filter strides padding use_cudnn_on_gpu data_format name = TSym "tf.conv2d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "use_cudnn_on_gpu" use_cudnn_on_gpu <+> TArgS "data_format" data_format <+> TArgS "name" name
conv2d :: SingI n => String -> Tensor n t a -> Sing n -> String -> Tensor n t a
conv2d input filter strides padding = TSym "tf.conv2d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "padding" padding
conv2dBackpropFilter' :: SingI n => String -> String -> String -> Sing n -> String -> String -> String -> String -> Tensor n t a
conv2dBackpropFilter' input filter_sizes out_backprop strides padding use_cudnn_on_gpu data_format name = TSym "tf.conv2d_backprop_filter" <+> TArgS "input" input <+> TArgS "filter_sizes" filter_sizes <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "use_cudnn_on_gpu" use_cudnn_on_gpu <+> TArgS "data_format" data_format <+> TArgS "name" name
conv2dBackpropFilter :: SingI n => String -> String -> String -> Sing n -> String -> Tensor n t a
conv2dBackpropFilter input filter_sizes out_backprop strides padding = TSym "tf.conv2d_backprop_filter" <+> TArgS "input" input <+> TArgS "filter_sizes" filter_sizes <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding
conv2dBackpropInput' :: SingI n => String -> Tensor n t a -> String -> Sing n -> String -> String -> String -> String -> Tensor n t a
conv2dBackpropInput' input_sizes filter out_backprop strides padding use_cudnn_on_gpu data_format name = TSym "tf.conv2d_backprop_input" <+> TArgS "input_sizes" input_sizes <+> TArgT "filter" filter <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "use_cudnn_on_gpu" use_cudnn_on_gpu <+> TArgS "data_format" data_format <+> TArgS "name" name
conv2dBackpropInput :: SingI n => String -> Tensor n t a -> String -> Sing n -> String -> Tensor n t a
conv2dBackpropInput input_sizes filter out_backprop strides padding = TSym "tf.conv2d_backprop_input" <+> TArgS "input_sizes" input_sizes <+> TArgT "filter" filter <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding
conv2dTranspose' :: SingI n => String -> Tensor n t a -> String -> Sing n -> String -> String -> String -> Tensor n t a
conv2dTranspose' value filter output_shape strides padding data_format name = TSym "tf.conv2d_transpose" <+> TArgS "value" value <+> TArgT "filter" filter <+> TArgS "output_shape" output_shape <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
conv2dTranspose :: SingI n => String -> Tensor n t a -> String -> Sing n -> Tensor n t a
conv2dTranspose value filter output_shape strides = TSym "tf.conv2d_transpose" <+> TArgS "value" value <+> TArgT "filter" filter <+> TArgS "output_shape" output_shape <+> TArgSing "strides" strides
conv3d' :: SingI n => String -> Tensor n t a -> Sing n -> String -> String -> String -> Tensor n t a
conv3d' input filter strides padding data_format name = TSym "tf.conv3d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
conv3d :: SingI n => String -> Tensor n t a -> Sing n -> String -> Tensor n t a
conv3d input filter strides padding = TSym "tf.conv3d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "padding" padding
conv3dBackpropFilterV2' :: SingI n => String -> String -> String -> Sing n -> String -> String -> String -> Tensor n t a
conv3dBackpropFilterV2' input filter_sizes out_backprop strides padding data_format name = TSym "tf.conv3d_backprop_filter_v2" <+> TArgS "input" input <+> TArgS "filter_sizes" filter_sizes <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
conv3dBackpropFilterV2 :: SingI n => String -> String -> String -> Sing n -> String -> Tensor n t a
conv3dBackpropFilterV2 input filter_sizes out_backprop strides padding = TSym "tf.conv3d_backprop_filter_v2" <+> TArgS "input" input <+> TArgS "filter_sizes" filter_sizes <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding
conv3dTranspose' :: SingI n => String -> Tensor n t a -> String -> Sing n -> String -> String -> String -> Tensor n t a
conv3dTranspose' value filter output_shape strides padding data_format name = TSym "tf.conv3d_transpose" <+> TArgS "value" value <+> TArgT "filter" filter <+> TArgS "output_shape" output_shape <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
conv3dTranspose :: SingI n => String -> Tensor n t a -> String -> Sing n -> Tensor n t a
conv3dTranspose value filter output_shape strides = TSym "tf.conv3d_transpose" <+> TArgS "value" value <+> TArgT "filter" filter <+> TArgS "output_shape" output_shape <+> TArgSing "strides" strides
convolution' :: SingI n => String -> Tensor n t a -> String -> Sing n -> String -> String -> String -> Tensor n t a
convolution' input filter padding strides dilation_rate name data_format = TSym "tf.convolution" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgS "padding" padding <+> TArgSing "strides" strides <+> TArgS "dilation_rate" dilation_rate <+> TArgS "name" name <+> TArgS "data_format" data_format
convolution :: String -> Tensor n t a -> String -> Tensor n t a
convolution input filter padding = TSym "tf.convolution" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgS "padding" padding
crelu' :: String -> String -> Tensor n t a
crelu' features name = TSym "tf.crelu" <+> TArgS "features" features <+> TArgS "name" name
crelu :: String -> Tensor n t a
crelu features = TSym "tf.crelu" <+> TArgS "features" features
ctcBeamSearchDecoder' :: String -> String -> String -> String -> String -> Tensor n t a
ctcBeamSearchDecoder' inputs sequence_length beam_width top_paths merge_repeated = TSym "tf.ctc_beam_search_decoder" <+> TArgS "inputs" inputs <+> TArgS "sequence_length" sequence_length <+> TArgS "beam_width" beam_width <+> TArgS "top_paths" top_paths <+> TArgS "merge_repeated" merge_repeated
ctcBeamSearchDecoder :: String -> String -> Tensor n t a
ctcBeamSearchDecoder inputs sequence_length = TSym "tf.ctc_beam_search_decoder" <+> TArgS "inputs" inputs <+> TArgS "sequence_length" sequence_length
ctcGreedyDecoder' :: String -> String -> String -> Tensor n t a
ctcGreedyDecoder' inputs sequence_length merge_repeated = TSym "tf.ctc_greedy_decoder" <+> TArgS "inputs" inputs <+> TArgS "sequence_length" sequence_length <+> TArgS "merge_repeated" merge_repeated
ctcGreedyDecoder :: String -> String -> Tensor n t a
ctcGreedyDecoder inputs sequence_length = TSym "tf.ctc_greedy_decoder" <+> TArgS "inputs" inputs <+> TArgS "sequence_length" sequence_length
ctcLoss' :: String -> String -> String -> String -> String -> String -> String -> Tensor n t a
ctcLoss' labels inputs sequence_length preprocess_collapse_repeated ctc_merge_repeated ignore_longer_outputs_than_inputs time_major = TSym "tf.ctc_loss" <+> TArgS "labels" labels <+> TArgS "inputs" inputs <+> TArgS "sequence_length" sequence_length <+> TArgS "preprocess_collapse_repeated" preprocess_collapse_repeated <+> TArgS "ctc_merge_repeated" ctc_merge_repeated <+> TArgS "ignore_longer_outputs_than_inputs" ignore_longer_outputs_than_inputs <+> TArgS "time_major" time_major
ctcLoss :: String -> String -> String -> Tensor n t a
ctcLoss labels inputs sequence_length = TSym "tf.ctc_loss" <+> TArgS "labels" labels <+> TArgS "inputs" inputs <+> TArgS "sequence_length" sequence_length
depthwiseConv2d' :: SingI n => String -> Tensor n t a -> Sing n -> String -> String -> String -> String -> Tensor n t a
depthwiseConv2d' input filter strides padding rate name data_format = TSym "tf.depthwise_conv2d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "rate" rate <+> TArgS "name" name <+> TArgS "data_format" data_format
depthwiseConv2d :: SingI n => String -> Tensor n t a -> Sing n -> String -> Tensor n t a
depthwiseConv2d input filter strides padding = TSym "tf.depthwise_conv2d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "padding" padding
depthwiseConv2dNative' :: SingI n => String -> Tensor n t a -> Sing n -> String -> String -> String -> Tensor n t a
depthwiseConv2dNative' input filter strides padding data_format name = TSym "tf.depthwise_conv2d_native" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
depthwiseConv2dNative :: SingI n => String -> Tensor n t a -> Sing n -> String -> Tensor n t a
depthwiseConv2dNative input filter strides padding = TSym "tf.depthwise_conv2d_native" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "padding" padding
depthwiseConv2dNativeBackpropFilter' :: SingI n => String -> String -> String -> Sing n -> String -> String -> String -> Tensor n t a
depthwiseConv2dNativeBackpropFilter' input filter_sizes out_backprop strides padding data_format name = TSym "tf.depthwise_conv2d_native_backprop_filter" <+> TArgS "input" input <+> TArgS "filter_sizes" filter_sizes <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
depthwiseConv2dNativeBackpropFilter :: SingI n => String -> String -> String -> Sing n -> String -> Tensor n t a
depthwiseConv2dNativeBackpropFilter input filter_sizes out_backprop strides padding = TSym "tf.depthwise_conv2d_native_backprop_filter" <+> TArgS "input" input <+> TArgS "filter_sizes" filter_sizes <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding
depthwiseConv2dNativeBackpropInput' :: SingI n => String -> Tensor n t a -> String -> Sing n -> String -> String -> String -> Tensor n t a
depthwiseConv2dNativeBackpropInput' input_sizes filter out_backprop strides padding data_format name = TSym "tf.depthwise_conv2d_native_backprop_input" <+> TArgS "input_sizes" input_sizes <+> TArgT "filter" filter <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
depthwiseConv2dNativeBackpropInput :: SingI n => String -> Tensor n t a -> String -> Sing n -> String -> Tensor n t a
depthwiseConv2dNativeBackpropInput input_sizes filter out_backprop strides padding = TSym "tf.depthwise_conv2d_native_backprop_input" <+> TArgS "input_sizes" input_sizes <+> TArgT "filter" filter <+> TArgS "out_backprop" out_backprop <+> TArgSing "strides" strides <+> TArgS "padding" padding
dilation2d' :: SingI n => String -> Tensor n t a -> Sing n -> String -> String -> String -> Tensor n t a
dilation2d' input filter strides rates padding name = TSym "tf.dilation2d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "rates" rates <+> TArgS "padding" padding <+> TArgS "name" name
dilation2d :: SingI n => String -> Tensor n t a -> Sing n -> String -> String -> Tensor n t a
dilation2d input filter strides rates padding = TSym "tf.dilation2d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgSing "strides" strides <+> TArgS "rates" rates <+> TArgS "padding" padding
dropout' :: Tensor n t a -> String -> String -> String -> String -> Tensor n t a
dropout' x keep_prob noise_shape seed name = TSym "tf.dropout" <+> TArgT "x" x <+> TArgS "keep_prob" keep_prob <+> TArgS "noise_shape" noise_shape <+> TArgS "seed" seed <+> TArgS "name" name
dropout :: Tensor n t a -> String -> Tensor n t a
dropout x keep_prob = TSym "tf.dropout" <+> TArgT "x" x <+> TArgS "keep_prob" keep_prob
dynamicRnn' :: String -> String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
dynamicRnn' cell inputs sequence_length initial_state dtype parallel_iterations swap_memory time_major scope = TSym "tf.dynamic_rnn" <+> TArgS "cell" cell <+> TArgS "inputs" inputs <+> TArgS "sequence_length" sequence_length <+> TArgS "initial_state" initial_state <+> TArgS "dtype" dtype <+> TArgS "parallel_iterations" parallel_iterations <+> TArgS "swap_memory" swap_memory <+> TArgS "time_major" time_major <+> TArgS "scope" scope
dynamicRnn :: String -> String -> Tensor n t a
dynamicRnn cell inputs = TSym "tf.dynamic_rnn" <+> TArgS "cell" cell <+> TArgS "inputs" inputs
elu' :: String -> String -> Tensor n t a
elu' features name = TSym "tf.elu" <+> TArgS "features" features <+> TArgS "name" name
elu :: String -> Tensor n t a
elu features = TSym "tf.elu" <+> TArgS "features" features
embeddingLookup' :: String -> String -> String -> String -> String -> String -> Tensor n t a
embeddingLookup' params ids partition_strategy name validate_indices max_norm = TSym "tf.embedding_lookup" <+> TArgS "params" params <+> TArgS "ids" ids <+> TArgS "partition_strategy" partition_strategy <+> TArgS "name" name <+> TArgS "validate_indices" validate_indices <+> TArgS "max_norm" max_norm
embeddingLookup :: String -> String -> Tensor n t a
embeddingLookup params ids = TSym "tf.embedding_lookup" <+> TArgS "params" params <+> TArgS "ids" ids
embeddingLookupSparse' :: String -> String -> String -> String -> String -> String -> String -> Tensor n t a
embeddingLookupSparse' params sp_ids sp_weights partition_strategy name combiner max_norm = TSym "tf.embedding_lookup_sparse" <+> TArgS "params" params <+> TArgS "sp_ids" sp_ids <+> TArgS "sp_weights" sp_weights <+> TArgS "partition_strategy" partition_strategy <+> TArgS "name" name <+> TArgS "combiner" combiner <+> TArgS "max_norm" max_norm
embeddingLookupSparse :: String -> String -> String -> Tensor n t a
embeddingLookupSparse params sp_ids sp_weights = TSym "tf.embedding_lookup_sparse" <+> TArgS "params" params <+> TArgS "sp_ids" sp_ids <+> TArgS "sp_weights" sp_weights
erosion2d' :: SingI n => String -> String -> Sing n -> String -> String -> String -> Tensor n t a
erosion2d' value kernel strides rates padding name = TSym "tf.erosion2d" <+> TArgS "value" value <+> TArgS "kernel" kernel <+> TArgSing "strides" strides <+> TArgS "rates" rates <+> TArgS "padding" padding <+> TArgS "name" name
erosion2d :: SingI n => String -> String -> Sing n -> String -> String -> Tensor n t a
erosion2d value kernel strides rates padding = TSym "tf.erosion2d" <+> TArgS "value" value <+> TArgS "kernel" kernel <+> TArgSing "strides" strides <+> TArgS "rates" rates <+> TArgS "padding" padding
fixedUnigramCandidateSampler' :: String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
fixedUnigramCandidateSampler' true_classes num_true num_sampled unique range_max vocab_file distortion num_reserved_ids num_shards shard unigrams seed name = TSym "tf.fixed_unigram_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique <+> TArgS "range_max" range_max <+> TArgS "vocab_file" vocab_file <+> TArgS "distortion" distortion <+> TArgS "num_reserved_ids" num_reserved_ids <+> TArgS "num_shards" num_shards <+> TArgS "shard" shard <+> TArgS "unigrams" unigrams <+> TArgS "seed" seed <+> TArgS "name" name
fixedUnigramCandidateSampler :: String -> String -> String -> String -> String -> Tensor n t a
fixedUnigramCandidateSampler true_classes num_true num_sampled unique range_max = TSym "tf.fixed_unigram_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique <+> TArgS "range_max" range_max
fractionalAvgPool' :: String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
fractionalAvgPool' value pooling_ratio pseudo_random overlapping deterministic seed seed2 name = TSym "tf.fractional_avg_pool" <+> TArgS "value" value <+> TArgS "pooling_ratio" pooling_ratio <+> TArgS "pseudo_random" pseudo_random <+> TArgS "overlapping" overlapping <+> TArgS "deterministic" deterministic <+> TArgS "seed" seed <+> TArgS "seed2" seed2 <+> TArgS "name" name
fractionalAvgPool :: String -> String -> Tensor n t a
fractionalAvgPool value pooling_ratio = TSym "tf.fractional_avg_pool" <+> TArgS "value" value <+> TArgS "pooling_ratio" pooling_ratio
fractionalMaxPool' :: String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
fractionalMaxPool' value pooling_ratio pseudo_random overlapping deterministic seed seed2 name = TSym "tf.fractional_max_pool" <+> TArgS "value" value <+> TArgS "pooling_ratio" pooling_ratio <+> TArgS "pseudo_random" pseudo_random <+> TArgS "overlapping" overlapping <+> TArgS "deterministic" deterministic <+> TArgS "seed" seed <+> TArgS "seed2" seed2 <+> TArgS "name" name
fractionalMaxPool :: String -> String -> Tensor n t a
fractionalMaxPool value pooling_ratio = TSym "tf.fractional_max_pool" <+> TArgS "value" value <+> TArgS "pooling_ratio" pooling_ratio
fusedBatchNorm' :: Tensor n t a -> String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
fusedBatchNorm' x scale offset mean variance epsilon data_format is_training name = TSym "tf.fused_batch_norm" <+> TArgT "x" x <+> TArgS "scale" scale <+> TArgS "offset" offset <+> TArgS "mean" mean <+> TArgS "variance" variance <+> TArgS "epsilon" epsilon <+> TArgS "data_format" data_format <+> TArgS "is_training" is_training <+> TArgS "name" name
fusedBatchNorm :: Tensor n t a -> String -> String -> Tensor n t a
fusedBatchNorm x scale offset = TSym "tf.fused_batch_norm" <+> TArgT "x" x <+> TArgS "scale" scale <+> TArgS "offset" offset
inTopK' :: String -> String -> String -> String -> Tensor n t a
inTopK' predictions targets k name = TSym "tf.in_top_k" <+> TArgS "predictions" predictions <+> TArgS "targets" targets <+> TArgS "k" k <+> TArgS "name" name
inTopK :: String -> String -> String -> Tensor n t a
inTopK predictions targets k = TSym "tf.in_top_k" <+> TArgS "predictions" predictions <+> TArgS "targets" targets <+> TArgS "k" k
l2Loss' :: String -> String -> Tensor n t a
l2Loss' t name = TSym "tf.l2_loss" <+> TArgS "t" t <+> TArgS "name" name
l2Loss :: String -> Tensor n t a
l2Loss t = TSym "tf.l2_loss" <+> TArgS "t" t
l2Normalize' :: Tensor n t a -> String -> String -> String -> Tensor n t a
l2Normalize' x dim epsilon name = TSym "tf.l2_normalize" <+> TArgT "x" x <+> TArgS "dim" dim <+> TArgS "epsilon" epsilon <+> TArgS "name" name
l2Normalize :: Tensor n t a -> String -> Tensor n t a
l2Normalize x dim = TSym "tf.l2_normalize" <+> TArgT "x" x <+> TArgS "dim" dim
learnedUnigramCandidateSampler' :: String -> String -> String -> String -> String -> String -> String -> Tensor n t a
learnedUnigramCandidateSampler' true_classes num_true num_sampled unique range_max seed name = TSym "tf.learned_unigram_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique <+> TArgS "range_max" range_max <+> TArgS "seed" seed <+> TArgS "name" name
learnedUnigramCandidateSampler :: String -> String -> String -> String -> String -> Tensor n t a
learnedUnigramCandidateSampler true_classes num_true num_sampled unique range_max = TSym "tf.learned_unigram_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique <+> TArgS "range_max" range_max
localResponseNormalization' :: String -> String -> String -> String -> String -> String -> Tensor n t a
localResponseNormalization' input depth_radius bias alpha beta name = TSym "tf.local_response_normalization" <+> TArgS "input" input <+> TArgS "depth_radius" depth_radius <+> TArgS "bias" bias <+> TArgS "alpha" alpha <+> TArgS "beta" beta <+> TArgS "name" name
localResponseNormalization :: String -> Tensor n t a
localResponseNormalization input = TSym "tf.local_response_normalization" <+> TArgS "input" input
logPoissonLoss' :: String -> String -> String -> String -> Tensor n t a
logPoissonLoss' targets log_input compute_full_loss name = TSym "tf.log_poisson_loss" <+> TArgS "targets" targets <+> TArgS "log_input" log_input <+> TArgS "compute_full_loss" compute_full_loss <+> TArgS "name" name
logPoissonLoss :: String -> String -> Tensor n t a
logPoissonLoss targets log_input = TSym "tf.log_poisson_loss" <+> TArgS "targets" targets <+> TArgS "log_input" log_input
logSoftmax' :: String -> String -> String -> Tensor n t a
logSoftmax' logits dim name = TSym "tf.log_softmax" <+> TArgS "logits" logits <+> TArgS "dim" dim <+> TArgS "name" name
logSoftmax :: String -> Tensor n t a
logSoftmax logits = TSym "tf.log_softmax" <+> TArgS "logits" logits
logUniformCandidateSampler' :: String -> String -> String -> String -> String -> String -> String -> Tensor n t a
logUniformCandidateSampler' true_classes num_true num_sampled unique range_max seed name = TSym "tf.log_uniform_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique <+> TArgS "range_max" range_max <+> TArgS "seed" seed <+> TArgS "name" name
logUniformCandidateSampler :: String -> String -> String -> String -> String -> Tensor n t a
logUniformCandidateSampler true_classes num_true num_sampled unique range_max = TSym "tf.log_uniform_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique <+> TArgS "range_max" range_max
lrn' :: String -> String -> String -> String -> String -> String -> Tensor n t a
lrn' input depth_radius bias alpha beta name = TSym "tf.lrn" <+> TArgS "input" input <+> TArgS "depth_radius" depth_radius <+> TArgS "bias" bias <+> TArgS "alpha" alpha <+> TArgS "beta" beta <+> TArgS "name" name
lrn :: String -> Tensor n t a
lrn input = TSym "tf.lrn" <+> TArgS "input" input
maxPool' :: SingI n => String -> String -> Sing n -> String -> String -> String -> Tensor n t a
maxPool' value ksize strides padding data_format name = TSym "tf.max_pool" <+> TArgS "value" value <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
maxPool :: SingI n => String -> String -> Sing n -> String -> Tensor n t a
maxPool value ksize strides padding = TSym "tf.max_pool" <+> TArgS "value" value <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding
maxPool3d' :: SingI n => String -> String -> Sing n -> String -> String -> String -> Tensor n t a
maxPool3d' input ksize strides padding data_format name = TSym "tf.max_pool3d" <+> TArgS "input" input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "data_format" data_format <+> TArgS "name" name
maxPool3d :: SingI n => String -> String -> Sing n -> String -> Tensor n t a
maxPool3d input ksize strides padding = TSym "tf.max_pool3d" <+> TArgS "input" input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding
maxPoolWithArgmax' :: SingI n => String -> String -> Sing n -> String -> String -> String -> Tensor n t a
maxPoolWithArgmax' input ksize strides padding targmax name = TSym "tf.max_pool_with_argmax" <+> TArgS "input" input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "Targmax" targmax <+> TArgS "name" name
maxPoolWithArgmax :: SingI n => String -> String -> Sing n -> String -> Tensor n t a
maxPoolWithArgmax input ksize strides padding = TSym "tf.max_pool_with_argmax" <+> TArgS "input" input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding
moments' :: Tensor n t a -> String -> String -> String -> String -> Tensor n t a
moments' x axes shift name keep_dims = TSym "tf.moments" <+> TArgT "x" x <+> TArgS "axes" axes <+> TArgS "shift" shift <+> TArgS "name" name <+> TArgS "keep_dims" keep_dims
moments :: Tensor n t a -> String -> Tensor n t a
moments x axes = TSym "tf.moments" <+> TArgT "x" x <+> TArgS "axes" axes
nceLoss' :: String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
nceLoss' weights biases labels inputs num_sampled num_classes num_true sampled_values remove_accidental_hits partition_strategy name = TSym "tf.nce_loss" <+> TArgS "weights" weights <+> TArgS "biases" biases <+> TArgS "labels" labels <+> TArgS "inputs" inputs <+> TArgS "num_sampled" num_sampled <+> TArgS "num_classes" num_classes <+> TArgS "num_true" num_true <+> TArgS "sampled_values" sampled_values <+> TArgS "remove_accidental_hits" remove_accidental_hits <+> TArgS "partition_strategy" partition_strategy <+> TArgS "name" name
nceLoss :: String -> String -> String -> String -> String -> String -> Tensor n t a
nceLoss weights biases labels inputs num_sampled num_classes = TSym "tf.nce_loss" <+> TArgS "weights" weights <+> TArgS "biases" biases <+> TArgS "labels" labels <+> TArgS "inputs" inputs <+> TArgS "num_sampled" num_sampled <+> TArgS "num_classes" num_classes
normalizeMoments' :: String -> String -> String -> String -> String -> Tensor n t a
normalizeMoments' counts mean_ss variance_ss shift name = TSym "tf.normalize_moments" <+> TArgS "counts" counts <+> TArgS "mean_ss" mean_ss <+> TArgS "variance_ss" variance_ss <+> TArgS "shift" shift <+> TArgS "name" name
normalizeMoments :: String -> String -> String -> String -> Tensor n t a
normalizeMoments counts mean_ss variance_ss shift = TSym "tf.normalize_moments" <+> TArgS "counts" counts <+> TArgS "mean_ss" mean_ss <+> TArgS "variance_ss" variance_ss <+> TArgS "shift" shift
pool' :: SingI n => String -> String -> String -> String -> String -> Sing n -> String -> String -> Tensor n t a
pool' input window_shape pooling_type padding dilation_rate strides name data_format = TSym "tf.pool" <+> TArgS "input" input <+> TArgS "window_shape" window_shape <+> TArgS "pooling_type" pooling_type <+> TArgS "padding" padding <+> TArgS "dilation_rate" dilation_rate <+> TArgSing "strides" strides <+> TArgS "name" name <+> TArgS "data_format" data_format
pool :: String -> String -> String -> String -> Tensor n t a
pool input window_shape pooling_type padding = TSym "tf.pool" <+> TArgS "input" input <+> TArgS "window_shape" window_shape <+> TArgS "pooling_type" pooling_type <+> TArgS "padding" padding
quantizedAvgPool' :: SingI n => String -> String -> String -> String -> Sing n -> String -> String -> Tensor n t a
quantizedAvgPool' input min_input max_input ksize strides padding name = TSym "tf.quantized_avg_pool" <+> TArgS "input" input <+> TArgS "min_input" min_input <+> TArgS "max_input" max_input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "name" name
quantizedAvgPool :: SingI n => String -> String -> String -> String -> Sing n -> String -> Tensor n t a
quantizedAvgPool input min_input max_input ksize strides padding = TSym "tf.quantized_avg_pool" <+> TArgS "input" input <+> TArgS "min_input" min_input <+> TArgS "max_input" max_input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding
quantizedConv2d' :: SingI n => String -> Tensor n t a -> String -> String -> String -> String -> Sing n -> String -> String -> String -> Tensor n t a
quantizedConv2d' input filter min_input max_input min_filter max_filter strides padding out_type name = TSym "tf.quantized_conv2d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgS "min_input" min_input <+> TArgS "max_input" max_input <+> TArgS "min_filter" min_filter <+> TArgS "max_filter" max_filter <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "out_type" out_type <+> TArgS "name" name
quantizedConv2d :: SingI n => String -> Tensor n t a -> String -> String -> String -> String -> Sing n -> String -> Tensor n t a
quantizedConv2d input filter min_input max_input min_filter max_filter strides padding = TSym "tf.quantized_conv2d" <+> TArgS "input" input <+> TArgT "filter" filter <+> TArgS "min_input" min_input <+> TArgS "max_input" max_input <+> TArgS "min_filter" min_filter <+> TArgS "max_filter" max_filter <+> TArgSing "strides" strides <+> TArgS "padding" padding
quantizedMaxPool' :: SingI n => String -> String -> String -> String -> Sing n -> String -> String -> Tensor n t a
quantizedMaxPool' input min_input max_input ksize strides padding name = TSym "tf.quantized_max_pool" <+> TArgS "input" input <+> TArgS "min_input" min_input <+> TArgS "max_input" max_input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "name" name
quantizedMaxPool :: SingI n => String -> String -> String -> String -> Sing n -> String -> Tensor n t a
quantizedMaxPool input min_input max_input ksize strides padding = TSym "tf.quantized_max_pool" <+> TArgS "input" input <+> TArgS "min_input" min_input <+> TArgS "max_input" max_input <+> TArgS "ksize" ksize <+> TArgSing "strides" strides <+> TArgS "padding" padding
quantizedReluX' :: String -> String -> String -> String -> String -> String -> Tensor n t a
quantizedReluX' features max_value min_features max_features out_type name = TSym "tf.quantized_relu_x" <+> TArgS "features" features <+> TArgS "max_value" max_value <+> TArgS "min_features" min_features <+> TArgS "max_features" max_features <+> TArgS "out_type" out_type <+> TArgS "name" name
quantizedReluX :: String -> String -> String -> String -> Tensor n t a
quantizedReluX features max_value min_features max_features = TSym "tf.quantized_relu_x" <+> TArgS "features" features <+> TArgS "max_value" max_value <+> TArgS "min_features" min_features <+> TArgS "max_features" max_features
rawRnn' :: String -> String -> String -> String -> String -> Tensor n t a
rawRnn' cell loop_fn parallel_iterations swap_memory scope = TSym "tf.raw_rnn" <+> TArgS "cell" cell <+> TArgS "loop_fn" loop_fn <+> TArgS "parallel_iterations" parallel_iterations <+> TArgS "swap_memory" swap_memory <+> TArgS "scope" scope
rawRnn :: String -> String -> Tensor n t a
rawRnn cell loop_fn = TSym "tf.raw_rnn" <+> TArgS "cell" cell <+> TArgS "loop_fn" loop_fn
relu' :: String -> String -> Tensor n t a
relu' features name = TSym "tf.relu" <+> TArgS "features" features <+> TArgS "name" name
relu :: String -> Tensor n t a
relu features = TSym "tf.relu" <+> TArgS "features" features
relu6' :: String -> String -> Tensor n t a
relu6' features name = TSym "tf.relu6" <+> TArgS "features" features <+> TArgS "name" name
relu6 :: String -> Tensor n t a
relu6 features = TSym "tf.relu6" <+> TArgS "features" features
reluLayer' :: Tensor n t a -> String -> String -> String -> Tensor n t a
reluLayer' x weights biases name = TSym "tf.relu_layer" <+> TArgT "x" x <+> TArgS "weights" weights <+> TArgS "biases" biases <+> TArgS "name" name
reluLayer :: Tensor n t a -> String -> String -> Tensor n t a
reluLayer x weights biases = TSym "tf.relu_layer" <+> TArgT "x" x <+> TArgS "weights" weights <+> TArgS "biases" biases
sampledSoftmaxLoss' :: String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
sampledSoftmaxLoss' weights biases labels inputs num_sampled num_classes num_true sampled_values remove_accidental_hits partition_strategy name = TSym "tf.sampled_softmax_loss" <+> TArgS "weights" weights <+> TArgS "biases" biases <+> TArgS "labels" labels <+> TArgS "inputs" inputs <+> TArgS "num_sampled" num_sampled <+> TArgS "num_classes" num_classes <+> TArgS "num_true" num_true <+> TArgS "sampled_values" sampled_values <+> TArgS "remove_accidental_hits" remove_accidental_hits <+> TArgS "partition_strategy" partition_strategy <+> TArgS "name" name
sampledSoftmaxLoss :: String -> String -> String -> String -> String -> String -> Tensor n t a
sampledSoftmaxLoss weights biases labels inputs num_sampled num_classes = TSym "tf.sampled_softmax_loss" <+> TArgS "weights" weights <+> TArgS "biases" biases <+> TArgS "labels" labels <+> TArgS "inputs" inputs <+> TArgS "num_sampled" num_sampled <+> TArgS "num_classes" num_classes
separableConv2d' :: SingI n => String -> String -> String -> Sing n -> String -> String -> String -> String -> Tensor n t a
separableConv2d' input depthwise_filter pointwise_filter strides padding rate name data_format = TSym "tf.separable_conv2d" <+> TArgS "input" input <+> TArgS "depthwise_filter" depthwise_filter <+> TArgS "pointwise_filter" pointwise_filter <+> TArgSing "strides" strides <+> TArgS "padding" padding <+> TArgS "rate" rate <+> TArgS "name" name <+> TArgS "data_format" data_format
separableConv2d :: SingI n => String -> String -> String -> Sing n -> String -> Tensor n t a
separableConv2d input depthwise_filter pointwise_filter strides padding = TSym "tf.separable_conv2d" <+> TArgS "input" input <+> TArgS "depthwise_filter" depthwise_filter <+> TArgS "pointwise_filter" pointwise_filter <+> TArgSing "strides" strides <+> TArgS "padding" padding
sigmoid' :: Tensor n t a -> String -> Tensor n t a
sigmoid' x name = TSym "tf.sigmoid" <+> TArgT "x" x <+> TArgS "name" name
sigmoid :: Tensor n t a -> Tensor n t a
sigmoid x = TSym "tf.sigmoid" <+> TArgT "x" x
sigmoidCrossEntropyWithLogits :: Tensor n t a
sigmoidCrossEntropyWithLogits = TSym "tf.sigmoid_cross_entropy_with_logits"
softmax' :: String -> String -> String -> Tensor n t a
softmax' logits dim name = TSym "tf.softmax" <+> TArgS "logits" logits <+> TArgS "dim" dim <+> TArgS "name" name
softmax :: String -> Tensor n t a
softmax logits = TSym "tf.softmax" <+> TArgS "logits" logits
softmaxCrossEntropyWithLogits :: Tensor n t a
softmaxCrossEntropyWithLogits = TSym "tf.softmax_cross_entropy_with_logits"
softplus' :: String -> String -> Tensor n t a
softplus' features name = TSym "tf.softplus" <+> TArgS "features" features <+> TArgS "name" name
softplus :: String -> Tensor n t a
softplus features = TSym "tf.softplus" <+> TArgS "features" features
softsign' :: String -> String -> Tensor n t a
softsign' features name = TSym "tf.softsign" <+> TArgS "features" features <+> TArgS "name" name
softsign :: String -> Tensor n t a
softsign features = TSym "tf.softsign" <+> TArgS "features" features
sparseSoftmaxCrossEntropyWithLogits :: Tensor n t a
sparseSoftmaxCrossEntropyWithLogits = TSym "tf.sparse_softmax_cross_entropy_with_logits"
staticBidirectionalRnn' :: String -> String -> String -> String -> String -> String -> String -> String -> Tensor n t a
staticBidirectionalRnn' cell_fw cell_bw inputs initial_state_fw initial_state_bw dtype sequence_length scope = TSym "tf.static_bidirectional_rnn" <+> TArgS "cell_fw" cell_fw <+> TArgS "cell_bw" cell_bw <+> TArgS "inputs" inputs <+> TArgS "initial_state_fw" initial_state_fw <+> TArgS "initial_state_bw" initial_state_bw <+> TArgS "dtype" dtype <+> TArgS "sequence_length" sequence_length <+> TArgS "scope" scope
staticBidirectionalRnn :: String -> String -> String -> Tensor n t a
staticBidirectionalRnn cell_fw cell_bw inputs = TSym "tf.static_bidirectional_rnn" <+> TArgS "cell_fw" cell_fw <+> TArgS "cell_bw" cell_bw <+> TArgS "inputs" inputs
staticRnn' :: String -> String -> String -> String -> String -> String -> Tensor n t a
staticRnn' cell inputs initial_state dtype sequence_length scope = TSym "tf.static_rnn" <+> TArgS "cell" cell <+> TArgS "inputs" inputs <+> TArgS "initial_state" initial_state <+> TArgS "dtype" dtype <+> TArgS "sequence_length" sequence_length <+> TArgS "scope" scope
staticRnn :: String -> String -> Tensor n t a
staticRnn cell inputs = TSym "tf.static_rnn" <+> TArgS "cell" cell <+> TArgS "inputs" inputs
staticStateSavingRnn' :: String -> String -> String -> String -> String -> String -> Tensor n t a
staticStateSavingRnn' cell inputs state_saver state_name sequence_length scope = TSym "tf.static_state_saving_rnn" <+> TArgS "cell" cell <+> TArgS "inputs" inputs <+> TArgS "state_saver" state_saver <+> TArgS "state_name" state_name <+> TArgS "sequence_length" sequence_length <+> TArgS "scope" scope
staticStateSavingRnn :: String -> String -> String -> String -> Tensor n t a
staticStateSavingRnn cell inputs state_saver state_name = TSym "tf.static_state_saving_rnn" <+> TArgS "cell" cell <+> TArgS "inputs" inputs <+> TArgS "state_saver" state_saver <+> TArgS "state_name" state_name
sufficientStatistics' :: Tensor n t a -> String -> String -> String -> String -> Tensor n t a
sufficientStatistics' x axes shift keep_dims name = TSym "tf.sufficient_statistics" <+> TArgT "x" x <+> TArgS "axes" axes <+> TArgS "shift" shift <+> TArgS "keep_dims" keep_dims <+> TArgS "name" name
sufficientStatistics :: Tensor n t a -> String -> Tensor n t a
sufficientStatistics x axes = TSym "tf.sufficient_statistics" <+> TArgT "x" x <+> TArgS "axes" axes
tanh' :: Tensor n t a -> String -> Tensor n t a
tanh' x name = TSym "tf.tanh" <+> TArgT "x" x <+> TArgS "name" name
tanh :: Tensor n t a -> Tensor n t a
tanh x = TSym "tf.tanh" <+> TArgT "x" x
topK' :: String -> String -> String -> String -> Tensor n t a
topK' input k sorted name = TSym "tf.top_k" <+> TArgS "input" input <+> TArgS "k" k <+> TArgS "sorted" sorted <+> TArgS "name" name
topK :: String -> Tensor n t a
topK input = TSym "tf.top_k" <+> TArgS "input" input
uniformCandidateSampler' :: String -> String -> String -> String -> String -> String -> String -> Tensor n t a
uniformCandidateSampler' true_classes num_true num_sampled unique range_max seed name = TSym "tf.uniform_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique <+> TArgS "range_max" range_max <+> TArgS "seed" seed <+> TArgS "name" name
uniformCandidateSampler :: String -> String -> String -> String -> String -> Tensor n t a
uniformCandidateSampler true_classes num_true num_sampled unique range_max = TSym "tf.uniform_candidate_sampler" <+> TArgS "true_classes" true_classes <+> TArgS "num_true" num_true <+> TArgS "num_sampled" num_sampled <+> TArgS "unique" unique <+> TArgS "range_max" range_max
weightedCrossEntropyWithLogits' :: String -> String -> String -> String -> Tensor n t a
weightedCrossEntropyWithLogits' targets logits pos_weight name = TSym "tf.weighted_cross_entropy_with_logits" <+> TArgS "targets" targets <+> TArgS "logits" logits <+> TArgS "pos_weight" pos_weight <+> TArgS "name" name
weightedCrossEntropyWithLogits :: String -> String -> String -> Tensor n t a
weightedCrossEntropyWithLogits targets logits pos_weight = TSym "tf.weighted_cross_entropy_with_logits" <+> TArgS "targets" targets <+> TArgS "logits" logits <+> TArgS "pos_weight" pos_weight
weightedMoments' :: Tensor n t a -> String -> String -> String -> String -> Tensor n t a
weightedMoments' x axes frequency_weights name keep_dims = TSym "tf.weighted_moments" <+> TArgT "x" x <+> TArgS "axes" axes <+> TArgS "frequency_weights" frequency_weights <+> TArgS "name" name <+> TArgS "keep_dims" keep_dims
weightedMoments :: Tensor n t a -> String -> String -> Tensor n t a
weightedMoments x axes frequency_weights = TSym "tf.weighted_moments" <+> TArgT "x" x <+> TArgS "axes" axes <+> TArgS "frequency_weights" frequency_weights
withSpaceToBatch' :: String -> String -> String -> String -> String -> String -> String -> Tensor n t a
withSpaceToBatch' input dilation_rate padding op filter_shape spatial_dims data_format = TSym "tf.with_space_to_batch" <+> TArgS "input" input <+> TArgS "dilation_rate" dilation_rate <+> TArgS "padding" padding <+> TArgS "op" op <+> TArgS "filter_shape" filter_shape <+> TArgS "spatial_dims" spatial_dims <+> TArgS "data_format" data_format
withSpaceToBatch :: String -> String -> String -> String -> Tensor n t a
withSpaceToBatch input dilation_rate padding op = TSym "tf.with_space_to_batch" <+> TArgS "input" input <+> TArgS "dilation_rate" dilation_rate <+> TArgS "padding" padding <+> TArgS "op" op
xwPlusB' :: Tensor n t a -> String -> String -> String -> Tensor n t a
xwPlusB' x weights biases name = TSym "tf.xw_plus_b" <+> TArgT "x" x <+> TArgS "weights" weights <+> TArgS "biases" biases <+> TArgS "name" name
xwPlusB :: Tensor n t a -> String -> String -> Tensor n t a
xwPlusB x weights biases = TSym "tf.xw_plus_b" <+> TArgT "x" x <+> TArgS "weights" weights <+> TArgS "biases" biases
zeroFraction' :: String -> String -> Tensor n t a
zeroFraction' value name = TSym "tf.zero_fraction" <+> TArgS "value" value <+> TArgS "name" name
zeroFraction :: String -> Tensor n t a
zeroFraction value = TSym "tf.zero_fraction" <+> TArgS "value" value