Functions — github.com/ahangchen/GDLnotes

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Functions224 in github.com/ahangchen/GDLnotes

⨍ Functions 224◇ Types & classes 7

↓ 2 callersFunctionmaybe_download

Download filename from url unless it's already in directory.

src/rnn/data_utils.py:52

↓ 2 callersFunctionmerge_prune

(train_folders, test_folders)

src/not_mnist/merge_prune.py:66

↓ 2 callersFunctionmodel

(data, init=False)

src/optimize/cnn_long_optimize.py:54

↓ 2 callersMethodnext

Generate the next array of batches from the data. The array consists of the last batch of the previous array, followed by num_unrollings new o

src/rnn/seq2seq.py:96

↓ 2 callersMethodnext_train

(self)

src/rnn/lstm_regular.py:44

↓ 2 callersFunctionrev_id

(forward)

src/rnn/seq2seq.py:131

↓ 2 callersFunctionsample

Turn a (column) prediction into 1-hot encoded samples.

src/rnn/bigram_lstm.py:151

↓ 2 callersFunctionsample

Turn a (column) prediction into 1-hot encoded samples.

src/rnn/singlew_lstm.py:151

↓ 2 callersFunctionsample

Turn a (column) prediction into 1-hot encoded samples.

src/rnn/embed_bigram_lstm.py:157

↓ 2 callersFunctionsample

Turn a (column) prediction into 1-hot encoded samples.

src/rnn/lstm.py:148

↓ 2 callersFunctionsoftmax

(x)

src/soft_max.py:8

↓ 1 callersFunction_conv_layer

(input, weights, bias)

src/app/neural_style/vgg.py:50

↓ 1 callersFunction_pool_layer

(input)

src/app/neural_style/vgg.py:56

↓ 1 callersFunctionaccuracy

(predictions, train_labels)

src/neural/digit_nn.py:23

↓ 1 callersFunctionbetter_conv_train

(drop=False, lrd=False)

src/convnet/conv_mnist.py:196

↓ 1 callersFunctionbetter_trend_hyper

(hypers, loss)

src/util/request.py:65

↓ 1 callersFunctionbigramonehot

(encodings)

src/rnn/embed_bigram_lstm.py:122

↓ 1 callersFunctionbigrams

(encodings)

src/rnn/embed_bigram_lstm.py:107

↓ 1 callersFunctionbitrain

(g, num_steps, summary_frequency, num_unrollings, batch_size)

src/rnn/embed_bigram_lstm.py:271

↓ 1 callersFunctionbuild_dataset

(words)

src/rnn/word2vec.py:22

↓ 1 callersFunctionbuild_dataset

(words, vocabulary_size)

src/rnn/cbow.py:22

↓ 1 callersFunctionbuild_parser

()

src/app/neural_style/neural_style.py:23

↓ 1 callersFunctionconv_train

(train_dataset, train_labels, valid_dataset, valid_labels, test_dataset, test_labels, image_size,

src/optimize/cnn_long_optimize.py:17

↓ 1 callersFunctionconv_train

(train_dataset, train_labels, valid_dataset, valid_labels, test_dataset, test_labels, image_size,

src/optimize/cnn_prophet.py:18

↓ 1 callersFunctionconv_train

(train_dataset, train_labels, valid_dataset, valid_labels, test_dataset, test_labels, image_size,

src/optimize/random_param_cnn.py:17

↓ 1 callersFunctionconv_train

(train_dataset, train_labels, valid_dataset, valid_labels, test_dataset, test_labels, image_size,

src/convnet/cnn_board.py:15

↓ 1 callersFunctionconv_train

(train_dataset, train_labels, valid_dataset, valid_labels, test_dataset, test_labels, image_size,

src/convnet/hyper_conv_mnist.py:16

↓ 1 callersFunctioncreate_model

(sess, forward_only)

src/rnn/seq2seq.py:139

↓ 1 callersFunctiondata_idx

()

src/rnn/lstm_regular.py:29

↓ 1 callersFunctiondig_nn

(dataset, train_labels, batch_size, data_count, label_count)

src/neural/digit_nn.py:27

↓ 1 callersFunctionetc_hp

()

src/optimize/cnn_long_optimize.py:154

↓ 1 callersFunctionfit_better

()

src/optimize/cnn_long_optimize.py:165

↓ 1 callersFunctionfit_better

()

src/optimize/cnn_half_optimize.py:176

↓ 1 callersFunctionfit_better

()

src/optimize/cnn_step_optimize.py:176

↓ 1 callersFunctionfit_better

()

src/optimize/cnn_optimize.py:201

↓ 1 callersFunctionfit_predict

()

src/optimize/cnn_prophet.py:204

↓ 1 callersFunctionget_wmt_enfr_dev_set

Download the WMT en-fr training corpus to directory unless it's there.

src/rnn/data_utils.py:89

↓ 1 callersFunctionget_wmt_enfr_train_set

Download the WMT en-fr training corpus to directory unless it's there.

src/rnn/data_utils.py:75

↓ 1 callersFunctionhalf_trend_hyper

(hypers, loss)

src/util/request.py:75

↓ 1 callersFunctionhp_train

()

src/convnet/cnn_board.py:250

↓ 1 callersFunctionhp_train

()

src/convnet/hyper_conv_mnist.py:166

↓ 1 callersFunctionids

Turn a 1-hot encoding or a probability distribution over the possible characters back into its (most likely) character representation.

src/rnn/seq2seq.py:113

↓ 1 callersFunctionimgs_except

(left, right)

src/not_mnist/clean_overlap.py:52

↓ 1 callersFunctionimsave

(path, img)

src/app/neural_style/neural_style.py:146

↓ 1 callersFunctioninitialize_vocabulary

Initialize vocabulary from file. We assume the vocabulary is stored one-item-per-line, so a file: dog cat will result in a vocabulary {"d

src/rnn/data_utils.py:157

↓ 1 callersFunctionload_letter

Load the data for a single letter label.

src/not_mnist/img_pickle.py:16

↓ 1 callersFunctionload_pickle

(pickle_name)

src/not_mnist/pick.py:18

↓ 1 callersFunctionload_reformat_not_mnist

(image_size, num_labels, num_channels)

src/convnet/conv_mnist.py:15

↓ 1 callersFunctionmain

()

src/app/neural_style/neural_style.py:78

↓ 1 callersFunctionmodel

(data, init=True)

src/convnet/cnn_board.py:74

↓ 1 callersFunctionone_hot_voc

(prediction, size=vocabulary_size)

src/rnn/embed_bigram_lstm.py:164

↓ 1 callersFunctionpiece_data

(raw_data, i, piece_size)

src/rnn/lstm_regular.py:21

↓ 1 callersFunctionpiece_label

(raw_data, i, piece_size)

src/rnn/lstm_regular.py:25

↓ 1 callersFunctionplot

(embeddings, labels)

src/rnn/word2vec.py:182

↓ 1 callersFunctionplot

(embeddings, labels)

src/rnn/cbow.py:207

↓ 1 callersFunctionpredict_future

(hypers, loss)

src/util/request.py:43

↓ 1 callersFunctionprepare_data

Preapre all necessary files that are required for the training. Args: data_dir: directory in which the data sets will be stored. from

src/rnn/data_utils.py:278

↓ 1 callersFunctionprint_progress

(i, last=False)

src/app/neural_style/stylize.py:102

↓ 1 callersFunctionrandom_distribution

Generate a random column of probabilities.

src/rnn/bigram_lstm.py:158

↓ 1 callersFunctionrandom_distribution

Generate a random column of probabilities.

src/rnn/singlew_lstm.py:158

↓ 1 callersFunctionrandom_distribution

Generate a random column of probabilities.

src/rnn/embed_bigram_lstm.py:170

↓ 1 callersFunctionrandom_distribution

Generate a random column of probabilities.

src/rnn/lstm.py:155

↓ 1 callersFunctionrandom_hp

()

src/optimize/random_param_cnn.py:159

↓ 1 callersFunctionraw_data

()

src/rnn/lstm_regular.py:15

↓ 1 callersFunctionread_data

(filename)

src/rnn/bigram_lstm.py:13

↓ 1 callersFunctionread_data

(filename)

src/rnn/seq2seq.py:14

↓ 1 callersFunctionread_data

Extract the first file enclosed in a zip file as a list of words

src/rnn/word2vec.py:15

↓ 1 callersFunctionread_data

(filename)

src/rnn/singlew_lstm.py:13

↓ 1 callersFunctionread_data

Extract the first file enclosed in a zip file as a list of words

src/rnn/cbow.py:15

↓ 1 callersFunctionread_data

(filename)

src/rnn/embed_bigram_lstm.py:13

↓ 1 callersFunctionread_data

(filename)

src/rnn/lstm.py:13

↓ 1 callersFunctionsample_distribution

Sample one element from a distribution assumed to be an array of normalized probabilities.

src/rnn/bigram_lstm.py:137

↓ 1 callersFunctionsample_distribution

Sample one element from a distribution assumed to be an array of normalized probabilities.

src/rnn/singlew_lstm.py:137

↓ 1 callersFunctionsample_distribution

Sample one element from a distribution assumed to be an array of normalized probabilities.

src/rnn/embed_bigram_lstm.py:144

↓ 1 callersFunctionsample_distribution

Sample one element from a distribution assumed to be an array of normalized probabilities.

src/rnn/lstm.py:134

↓ 1 callersFunctionsave_obj

(pickle_file, obj)

src/not_mnist/pick.py:6

↓ 1 callersFunctionsentence_to_token_ids

Convert a string to list of integers representing token-ids. For example, a sentence "I have a dog" may become tokenized into ["I", "have", "a",

src/rnn/data_utils.py:187

↓ 1 callersFunctionshow_imgs

(imgs, show_max=-1)

src/not_mnist/img_pickle.py:66

↓ 1 callersFunctionstylize

Stylize images. This function yields tuples (iteration, image); `iteration` is None if this is the final image (the last iteration). Ot

src/app/neural_style/stylize.py:20

↓ 1 callersFunctiontf_deep_nn

(regular=False, drop_out=False, lrd=False, layer_cnt=2)

src/neural/nn_overfit.py:106

↓ 1 callersFunctiontf_deep_nn

(regular=False, test=False)

src/app/caltech/dnn_caltech_board.py:43

↓ 1 callersFunctiontf_deep_nn

(regular=False, drop_out=False, lrd=False, layer_cnt=2)

src/app/caltech/dnn_caltech.py:10

↓ 1 callersFunctiontf_sgd_relu_nn

()

src/neural/full_connect.py:147

↓ 1 callersFunctiontrain_data

()

src/neural/digit_nn.py:12

↓ 1 callersFunctionvalid_hp

(hps)

src/optimize/cnn_half_optimize.py:160

↓ 1 callersFunctionvalid_hp

(hps)

src/optimize/cnn_step_optimize.py:160

↓ 1 callersFunctionvalid_hp

(hps)

src/optimize/cnn_optimize.py:188

FunctionBKDRHash

(string)

src/not_mnist/clean_overlap.py:35

Method__init__

(self, text, batch_size, num_unrollings)

src/rnn/bigram_lstm.py:76

Method__init__

(self)

src/rnn/lstm_regular.py:36

Method__init__

(self, text, batch_size, num_unrollings)

src/rnn/seq2seq.py:76

Method__init__

Create the model. Args: source_vocab_size: size of the source vocabulary. target_vocab_size: size of the target vocabulary. buc

src/rnn/seq2seq_model.py:46

Method__init__

(self, text, batch_size, num_unrollings)

src/rnn/singlew_lstm.py:73

Method__init__

(self, text, batch_size, num_unrollings)

src/rnn/embed_bigram_lstm.py:71

Method__init__

(self, text, batch_size, num_unrollings)

src/rnn/lstm.py:73

Functionbatches2id

Convert a sequence of batches back into their (most likely) string representation.

src/rnn/seq2seq.py:119

Functioncharacters

Turn a 1-hot encoding or a probability distribution over the possible characters back into its (most likely) character representation.

src/rnn/seq2seq.py:107

Functioncnn_model_fn

Model function for CNN.

src/skflow/skflow_cnn.py:29

Functionconv_max_pool_train

()

src/convnet/conv_mnist.py:121

Functionconv_train

()

src/convnet/conv_mnist.py:47

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