Module: TensorFlow::NN

Defined in:
lib/tensorflow/nn.rb

Class Method Summary collapse

Class Method Details

.all_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, seed: nil, seed2: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 4

def all_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, seed: nil, seed2: nil)
  RawOps.all_candidate_sampler(true_classes: true_classes, num_true: num_true, num_sampled: num_sampled, unique: unique, seed: seed, seed2: seed2)
end

.avg_pool(value, ksize: nil, strides: nil, padding: nil, data_format: nil) ⇒ Object

def atrous_conv2d_transpose end



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# File 'lib/tensorflow/nn.rb', line 14

def avg_pool(value, ksize: nil, strides: nil, padding: nil, data_format: nil)
  RawOps.avg_pool(value: value, ksize: ksize, strides: strides, padding: padding, data_format: data_format)
end

.avg_pool3d(input, ksize: nil, strides: nil, padding: nil, data_format: nil) ⇒ Object

def avg_pool2d end



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# File 'lib/tensorflow/nn.rb', line 24

def avg_pool3d(input, ksize: nil, strides: nil, padding: nil, data_format: nil)
  RawOps.avg_pool3d(input: input, ksize: ksize, strides: strides, padding: padding, data_format: data_format)
end

.batch_norm_with_global_normalization(t, m, v, beta, gamma, variance_epsilon: nil, scale_after_normalization: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 28

def batch_norm_with_global_normalization(t, m, v, beta, gamma, variance_epsilon: nil, scale_after_normalization: nil)
  RawOps.batch_norm_with_global_normalization(t: t, m: m, v: v, beta: beta, gamma: gamma, variance_epsilon: variance_epsilon, scale_after_normalization: scale_after_normalization)
end

.bias_add(value, bias, data_format: nil) ⇒ Object

def batch_normalization end



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# File 'lib/tensorflow/nn.rb', line 35

def bias_add(value, bias, data_format: nil)
  RawOps.bias_add(value: value, bias: bias, data_format: data_format)
end

.compute_accidental_hits(true_classes, sampled_candidates, num_true: nil, seed: nil, seed2: nil) ⇒ Object

def collapse_repeated end



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# File 'lib/tensorflow/nn.rb', line 42

def compute_accidental_hits(true_classes, sampled_candidates, num_true: nil, seed: nil, seed2: nil)
  RawOps.compute_accidental_hits(true_classes: true_classes, sampled_candidates: sampled_candidates, num_true: num_true, seed: seed, seed2: seed2)
end

.conv2d(input, filter, strides: nil, use_cudnn_on_gpu: nil, padding: nil, explicit_paddings: nil, data_format: nil, dilations: nil) ⇒ Object

def conv1d_transpose end



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# File 'lib/tensorflow/nn.rb', line 55

def conv2d(input, filter, strides: nil, use_cudnn_on_gpu: nil, padding: nil, explicit_paddings: nil, data_format: nil, dilations: nil)
  RawOps.conv2d(input: input, filter: filter, strides: strides, use_cudnn_on_gpu: use_cudnn_on_gpu, padding: padding, explicit_paddings: explicit_paddings, data_format: data_format, dilations: dilations)
end

.conv3d(input, filter, strides: nil, padding: nil, data_format: nil, dilations: nil) ⇒ Object

def conv2d_transpose end



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# File 'lib/tensorflow/nn.rb', line 62

def conv3d(input, filter, strides: nil, padding: nil, data_format: nil, dilations: nil)
  RawOps.conv3d(input: input, filter: filter, strides: strides, padding: padding, data_format: data_format, dilations: dilations)
end

.ctc_beam_search_decoder(inputs, sequence_length, beam_width: nil, top_paths: nil, merge_repeated: nil) ⇒ Object

def crelu end



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# File 'lib/tensorflow/nn.rb', line 78

def ctc_beam_search_decoder(inputs, sequence_length, beam_width: nil, top_paths: nil, merge_repeated: nil)
  RawOps.ctc_beam_search_decoder(inputs: inputs, sequence_length: sequence_length, beam_width: beam_width, top_paths: top_paths, merge_repeated: merge_repeated)
end

.ctc_greedy_decoder(inputs, sequence_length, merge_repeated: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 82

def ctc_greedy_decoder(inputs, sequence_length, merge_repeated: nil)
  RawOps.ctc_greedy_decoder(inputs: inputs, sequence_length: sequence_length, merge_repeated: merge_repeated)
end

.ctc_loss(inputs, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated: nil, ctc_merge_repeated: nil, ignore_longer_outputs_than_inputs: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 86

def ctc_loss(inputs, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated: nil, ctc_merge_repeated: nil, ignore_longer_outputs_than_inputs: nil)
  RawOps.ctc_loss(inputs: inputs, labels_indices: labels_indices, labels_values: labels_values, sequence_length: sequence_length, preprocess_collapse_repeated: preprocess_collapse_repeated, ctc_merge_repeated: ctc_merge_repeated, ignore_longer_outputs_than_inputs: ignore_longer_outputs_than_inputs)
end

.depth_to_space(input, block_size: nil, data_format: nil) ⇒ Object

def ctc_unique_labels end



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# File 'lib/tensorflow/nn.rb', line 93

def depth_to_space(input, block_size: nil, data_format: nil)
  RawOps.depth_to_space(input: input, block_size: block_size, data_format: data_format)
end

.dilation2d(input, filter, strides: nil, rates: nil, padding: nil) ⇒ Object

def depthwise_conv2d_backprop_input end



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# File 'lib/tensorflow/nn.rb', line 106

def dilation2d(input, filter, strides: nil, rates: nil, padding: nil)
  RawOps.dilation2d(input: input, filter: filter, strides: strides, rates: rates, padding: padding)
end

.elu(features) ⇒ Object

def dropout end



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# File 'lib/tensorflow/nn.rb', line 113

def elu(features)
  RawOps.elu(features: features)
end

.fixed_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, vocab_file: nil, distortion: nil, num_reserved_ids: nil, num_shards: nil, shard: nil, unigrams: nil, seed: nil, seed2: nil) ⇒ Object

def erosion2d end



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# File 'lib/tensorflow/nn.rb', line 126

def fixed_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, vocab_file: nil, distortion: nil, num_reserved_ids: nil, num_shards: nil, shard: nil, unigrams: nil, seed: nil, seed2: nil)
  RawOps.fixed_unigram_candidate_sampler(true_classes: true_classes, num_true: num_true, num_sampled: num_sampled, unique: unique, range_max: range_max, vocab_file: vocab_file, distortion: distortion, num_reserved_ids: num_reserved_ids, num_shards: num_shards, shard: shard, unigrams: unigrams, seed: seed, seed2: seed2)
end

.fractional_avg_pool(value, pooling_ratio: nil, pseudo_random: nil, overlapping: nil, deterministic: nil, seed: nil, seed2: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 130

def fractional_avg_pool(value, pooling_ratio: nil, pseudo_random: nil, overlapping: nil, deterministic: nil, seed: nil, seed2: nil)
  RawOps.fractional_avg_pool(value: value, pooling_ratio: pooling_ratio, pseudo_random: pseudo_random, overlapping: overlapping, deterministic: deterministic, seed: seed, seed2: seed2)
end

.fractional_max_pool(value, pooling_ratio: nil, pseudo_random: nil, overlapping: nil, deterministic: nil, seed: nil, seed2: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 134

def fractional_max_pool(value, pooling_ratio: nil, pseudo_random: nil, overlapping: nil, deterministic: nil, seed: nil, seed2: nil)
  RawOps.fractional_max_pool(value: value, pooling_ratio: pooling_ratio, pseudo_random: pseudo_random, overlapping: overlapping, deterministic: deterministic, seed: seed, seed2: seed2)
end

.in_top_k(predictions, targets, k: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 138

def in_top_k(predictions, targets, k: nil)
  RawOps.in_top_k(predictions: predictions, targets: targets, k: k)
end

.l2_loss(t) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 142

def l2_loss(t)
  RawOps.l2_loss(t: t)
end

.leaky_relu(features, alpha: nil) ⇒ Object

def l2_normalize end



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# File 'lib/tensorflow/nn.rb', line 149

def leaky_relu(features, alpha: nil)
  RawOps.leaky_relu(features: features, alpha: alpha)
end

.learned_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: nil, seed2: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 153

def learned_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: nil, seed2: nil)
  RawOps.learned_unigram_candidate_sampler(true_classes: true_classes, num_true: num_true, num_sampled: num_sampled, unique: unique, range_max: range_max, seed: seed, seed2: seed2)
end

.log_softmax(logits) ⇒ Object

def log_poisson_loss end



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# File 'lib/tensorflow/nn.rb', line 163

def log_softmax(logits)
  RawOps.log_softmax(logits: logits)
end

.lrn(input, depth_radius: nil, bias: nil, alpha: nil, beta: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 167

def lrn(input, depth_radius: nil, bias: nil, alpha: nil, beta: nil)
  RawOps.lrn(input: input, depth_radius: depth_radius, bias: bias, alpha: alpha, beta: beta)
end

.max_pool(input, ksize: nil, strides: nil, padding: nil, data_format: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 171

def max_pool(input, ksize: nil, strides: nil, padding: nil, data_format: nil)
  RawOps.max_pool(input: input, ksize: ksize, strides: strides, padding: padding, data_format: data_format)
end

.max_pool3d(input, ksize: nil, strides: nil, padding: nil, data_format: nil) ⇒ Object

def max_pool2d end



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# File 'lib/tensorflow/nn.rb', line 181

def max_pool3d(input, ksize: nil, strides: nil, padding: nil, data_format: nil)
  RawOps.max_pool3d(input: input, ksize: ksize, strides: strides, padding: padding, data_format: data_format)
end

.max_pool_with_argmax(input, ksize: nil, strides: nil, padding: nil, include_batch_in_index: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 185

def max_pool_with_argmax(input, ksize: nil, strides: nil, padding: nil, include_batch_in_index: nil)
  RawOps.max_pool_with_argmax(input: input, ksize: ksize, strides: strides, padding: padding, include_batch_in_index: include_batch_in_index)
end

.relu(features) ⇒ Object

def pool end



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# File 'lib/tensorflow/nn.rb', line 201

def relu(features)
  RawOps.relu(features: features)
end

.relu6(features) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 205

def relu6(features)
  RawOps.relu6(features: features)
end

.selu(features) ⇒ Object

def scale_regularization_loss end



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# File 'lib/tensorflow/nn.rb', line 218

def selu(features)
  RawOps.selu(features: features)
end

.sigmoid(x) ⇒ Object

def separable_conv2d end



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# File 'lib/tensorflow/nn.rb', line 225

def sigmoid(x)
  RawOps.sigmoid(x: x)
end

.softmax(logits) ⇒ Object

def sigmoid_cross_entropy_with_logits end



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# File 'lib/tensorflow/nn.rb', line 232

def softmax(logits)
  RawOps.softmax(logits: logits)
end

.softmax_cross_entropy_with_logits(features, labels) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 236

def softmax_cross_entropy_with_logits(features, labels)
  RawOps.softmax_cross_entropy_with_logits(features: features, labels: labels)
end

.softplus(features) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 240

def softplus(features)
  RawOps.softplus(features: features)
end

.softsign(features) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 244

def softsign(features)
  RawOps.softsign(features: features)
end

.space_to_batch(input, paddings, block_size: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 248

def space_to_batch(input, paddings, block_size: nil)
  RawOps.space_to_batch(input: input, paddings: paddings, block_size: block_size)
end

.space_to_depth(input, block_size: nil, data_format: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 252

def space_to_depth(input, block_size: nil, data_format: nil)
  RawOps.space_to_depth(input: input, block_size: block_size, data_format: data_format)
end

.sparse_softmax_cross_entropy_with_logits(features, labels) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 256

def sparse_softmax_cross_entropy_with_logits(features, labels)
  RawOps.sparse_softmax_cross_entropy_with_logits(features: features, labels: labels)
end

.tanh(x) ⇒ Object

def sufficient_statistics end



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# File 'lib/tensorflow/nn.rb', line 263

def tanh(x)
  RawOps.tanh(x: x)
end

.top_k(input, k: nil, sorted: nil) ⇒ Object 



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# File 'lib/tensorflow/nn.rb', line 267

def top_k(input, k: nil, sorted: nil)
  RawOps.top_k(input: input, k: k, sorted: sorted)
end