SYMBOL INDEX (101 symbols across 45 files)

FILE: examples/2_BasicModels/linear_regression_eager_api.py
  function linear_regression (line 36) | def linear_regression(inputs):
  function mean_square_fn (line 41) | def mean_square_fn(model_fn, inputs, labels):

FILE: examples/2_BasicModels/logistic_regression_eager_api.py
  function logistic_regression (line 39) | def logistic_regression(inputs):
  function loss_fn (line 44) | def loss_fn(inference_fn, inputs, labels):
  function accuracy_fn (line 51) | def accuracy_fn(inference_fn, inputs, labels):

FILE: examples/2_BasicModels/word2vec.py
  function next_batch (line 93) | def next_batch(batch_size, num_skips, skip_window):

FILE: examples/3_NeuralNetworks/autoencoder.py
  function encoder (line 57) | def encoder(x):
  function decoder (line 68) | def decoder(x):

FILE: examples/3_NeuralNetworks/bidirectional_rnn.py
  function BiRNN (line 57) | def BiRNN(x, weights, biases):

FILE: examples/3_NeuralNetworks/convolutional_network.py
  function conv_net (line 33) | def conv_net(x_dict, n_classes, dropout, reuse, is_training):
  function model_fn (line 69) | def model_fn(features, labels, mode):

FILE: examples/3_NeuralNetworks/convolutional_network_raw.py
  function conv2d (line 37) | def conv2d(x, W, b, strides=1):
  function maxpool2d (line 44) | def maxpool2d(x, k=2):
  function conv_net (line 51) | def conv_net(x, weights, biases, dropout):

FILE: examples/3_NeuralNetworks/dcgan.py
  function generator (line 41) | def generator(x, reuse=False):
  function discriminator (line 61) | def discriminator(x, reuse=False):

FILE: examples/3_NeuralNetworks/dynamic_rnn.py
  class ToySequenceData (line 24) | class ToySequenceData(object):
    method __init__ (line 36) | def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3,
    method next (line 66) | def next(self, batch_size):
  function dynamicRNN (line 114) | def dynamicRNN(x, seqlen, weights, biases):

FILE: examples/3_NeuralNetworks/gan.py
  function glorot_init (line 44) | def glorot_init(shape):
  function generator (line 63) | def generator(x):
  function discriminator (line 74) | def discriminator(x):

FILE: examples/3_NeuralNetworks/multilayer_perceptron.py
  function multilayer_perceptron (line 59) | def multilayer_perceptron(x):

FILE: examples/3_NeuralNetworks/neural_network.py
  function neural_net (line 39) | def neural_net(x_dict):
  function model_fn (line 52) | def model_fn(features, labels, mode):

FILE: examples/3_NeuralNetworks/neural_network_eager_api.py
  class NeuralNet (line 49) | class NeuralNet(tfe.Network):
    method __init__ (line 50) | def __init__(self):
    method call (line 62) | def call(self, x):
  function loss_fn (line 72) | def loss_fn(inference_fn, inputs, labels):
  function accuracy_fn (line 79) | def accuracy_fn(inference_fn, inputs, labels):

FILE: examples/3_NeuralNetworks/neural_network_raw.py
  function neural_net (line 52) | def neural_net(x):

FILE: examples/3_NeuralNetworks/recurrent_network.py
  function RNN (line 54) | def RNN(x, weights, biases):

FILE: examples/3_NeuralNetworks/variational_autoencoder.py
  function glorot_init (line 45) | def glorot_init(shape):
  function vae_loss (line 84) | def vae_loss(x_reconstructed, x_true):

FILE: examples/4_Utils/save_restore_model.py
  function multilayer_perceptron (line 36) | def multilayer_perceptron(x, weights, biases):

FILE: examples/4_Utils/tensorboard_advanced.py
  function multilayer_perceptron (line 39) | def multilayer_perceptron(x, weights, biases):

FILE: examples/5_DataManagement/build_an_image_dataset.py
  function read_images (line 55) | def read_images(dataset_path, mode, batch_size):
  function conv_net (line 132) | def conv_net(x, n_classes, dropout, reuse, is_training):

FILE: examples/5_DataManagement/tensorflow_dataset_api.py
  function conv_net (line 56) | def conv_net(x, n_classes, dropout, reuse, is_training):

FILE: examples/6_MultiGPU/multigpu_basics.py
  function matpow (line 42) | def matpow(M, n):

FILE: examples/6_MultiGPU/multigpu_cnn.py
  function conv_net (line 39) | def conv_net(x, n_classes, dropout, reuse, is_training):
  function average_gradients (line 81) | def average_gradients(tower_grads):
  function assign_to_device (line 112) | def assign_to_device(device, ps_device='/cpu:0'):

FILE: input_data.py
  function maybe_download (line 8) | def maybe_download(filename, work_directory):
  function _read32 (line 18) | def _read32(bytestream):
  function extract_images (line 21) | def extract_images(filename):
  function dense_to_one_hot (line 37) | def dense_to_one_hot(labels_dense, num_classes=10):
  function extract_labels (line 44) | def extract_labels(filename, one_hot=False):
  class DataSet (line 59) | class DataSet(object):
    method __init__ (line 60) | def __init__(self, images, labels, fake_data=False):
    method images (line 81) | def images(self):
    method labels (line 84) | def labels(self):
    method num_examples (line 87) | def num_examples(self):
    method epochs_completed (line 90) | def epochs_completed(self):
    method next_batch (line 92) | def next_batch(self, batch_size, fake_data=False):
  function read_data_sets (line 115) | def read_data_sets(train_dir, fake_data=False, one_hot=False):

FILE: tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py
  function linear_regression (line 36) | def linear_regression(inputs):
  function mean_square_fn (line 41) | def mean_square_fn(model_fn, inputs, labels):

FILE: tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py
  function logistic_regression (line 39) | def logistic_regression(inputs):
  function loss_fn (line 44) | def loss_fn(inference_fn, inputs, labels):
  function accuracy_fn (line 51) | def accuracy_fn(inference_fn, inputs, labels):

FILE: tensorflow_v1/examples/2_BasicModels/word2vec.py
  function next_batch (line 93) | def next_batch(batch_size, num_skips, skip_window):

FILE: tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py
  function encoder (line 57) | def encoder(x):
  function decoder (line 68) | def decoder(x):

FILE: tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py
  function BiRNN (line 57) | def BiRNN(x, weights, biases):

FILE: tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py
  function conv_net (line 33) | def conv_net(x_dict, n_classes, dropout, reuse, is_training):
  function model_fn (line 69) | def model_fn(features, labels, mode):

FILE: tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py
  function conv2d (line 37) | def conv2d(x, W, b, strides=1):
  function maxpool2d (line 44) | def maxpool2d(x, k=2):
  function conv_net (line 51) | def conv_net(x, weights, biases, dropout):

FILE: tensorflow_v1/examples/3_NeuralNetworks/dcgan.py
  function generator (line 41) | def generator(x, reuse=False):
  function discriminator (line 61) | def discriminator(x, reuse=False):

FILE: tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py
  class ToySequenceData (line 24) | class ToySequenceData(object):
    method __init__ (line 36) | def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3,
    method next (line 66) | def next(self, batch_size):
  function dynamicRNN (line 114) | def dynamicRNN(x, seqlen, weights, biases):

FILE: tensorflow_v1/examples/3_NeuralNetworks/gan.py
  function glorot_init (line 44) | def glorot_init(shape):
  function generator (line 63) | def generator(x):
  function discriminator (line 74) | def discriminator(x):

FILE: tensorflow_v1/examples/3_NeuralNetworks/multilayer_perceptron.py
  function multilayer_perceptron (line 59) | def multilayer_perceptron(x):

FILE: tensorflow_v1/examples/3_NeuralNetworks/neural_network.py
  function neural_net (line 39) | def neural_net(x_dict):
  function model_fn (line 52) | def model_fn(features, labels, mode):

FILE: tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py
  class NeuralNet (line 49) | class NeuralNet(tfe.Network):
    method __init__ (line 50) | def __init__(self):
    method call (line 62) | def call(self, x):
  function loss_fn (line 72) | def loss_fn(inference_fn, inputs, labels):
  function accuracy_fn (line 79) | def accuracy_fn(inference_fn, inputs, labels):

FILE: tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py
  function neural_net (line 52) | def neural_net(x):

FILE: tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py
  function RNN (line 54) | def RNN(x, weights, biases):

FILE: tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py
  function glorot_init (line 45) | def glorot_init(shape):
  function vae_loss (line 84) | def vae_loss(x_reconstructed, x_true):

FILE: tensorflow_v1/examples/4_Utils/save_restore_model.py
  function multilayer_perceptron (line 36) | def multilayer_perceptron(x, weights, biases):

FILE: tensorflow_v1/examples/4_Utils/tensorboard_advanced.py
  function multilayer_perceptron (line 39) | def multilayer_perceptron(x, weights, biases):

FILE: tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py
  function read_images (line 55) | def read_images(dataset_path, mode, batch_size):
  function conv_net (line 132) | def conv_net(x, n_classes, dropout, reuse, is_training):

FILE: tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py
  function conv_net (line 56) | def conv_net(x, n_classes, dropout, reuse, is_training):

FILE: tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py
  function matpow (line 42) | def matpow(M, n):

FILE: tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py
  function conv_net (line 39) | def conv_net(x, n_classes, dropout, reuse, is_training):
  function average_gradients (line 81) | def average_gradients(tower_grads):
  function assign_to_device (line 112) | def assign_to_device(device, ps_device='/cpu:0'):