| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/speech_recognition/ |
| H A D | main.py | 221 def load_model(args, contexts, data_train): argument
248 data_names = [x[0] for x in data_train.provide_data]
299 data_train, data_val, args = load_data(args) variable
301 data_train, args = load_data(args) variable
320 data_names = [x[0] for x in data_train.provide_data]
324 do_training(args=args, module=module, data_train=data_train, data_val=data_val)
327 do_training(args=args, module=model_loaded, data_train=data_train, data_val=data_val,
346 model.bind(data_shapes=data_train.provide_data,
347 label_shapes=data_train.provide_label,
358 for nbatch, data_batch in enumerate(data_train):
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| H A D | train.py | 96 default_bucket_key=data_train.default_bucket_key,
98 data_train.reset()
100 model.bind(data_shapes=data_train.provide_data,
101 label_shapes=data_train.provide_label,
107 module.bind(data_shapes=data_train.provide_data,
108 label_shapes=data_train.provide_label,
130 data_train.reset()
131 data_train.is_first_epoch = True
143 for nbatch, data_batch in enumerate(data_train):
169 data_train.reset()
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/speech_recognition/ |
| H A D | main.py | 221 def load_model(args, contexts, data_train): argument
248 data_names = [x[0] for x in data_train.provide_data]
299 data_train, data_val, args = load_data(args) variable
301 data_train, args = load_data(args) variable
320 data_names = [x[0] for x in data_train.provide_data]
324 do_training(args=args, module=module, data_train=data_train, data_val=data_val)
327 do_training(args=args, module=model_loaded, data_train=data_train, data_val=data_val,
346 model.bind(data_shapes=data_train.provide_data,
347 label_shapes=data_train.provide_label,
358 for nbatch, data_batch in enumerate(data_train):
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| H A D | train.py | 96 default_bucket_key=data_train.default_bucket_key,
98 data_train.reset()
100 model.bind(data_shapes=data_train.provide_data,
101 label_shapes=data_train.provide_label,
107 module.bind(data_shapes=data_train.provide_data,
108 label_shapes=data_train.provide_label,
130 data_train.reset()
131 data_train.is_first_epoch = True
143 for nbatch, data_batch in enumerate(data_train):
169 data_train.reset()
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/nce-loss/ |
| H A D | wordvec.py | 42 data_train = DataIterWords("./data/text8", batch_size, num_label) variable
44 network = get_word_net(data_train.vocab_size, num_label - 1)
52 data_names=[x[0] for x in data_train.provide_data],
53 label_names=[y[0] for y in data_train.provide_label],
60 train_data=data_train,
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| H A D | lstm_word.py | 49 data_train = DataIterLstm("./data/text8", batch_size, seq_len, num_label, init_states) variable
51 network = get_lstm_net(data_train.vocab_size, seq_len, num_lstm_layer, num_hidden)
59 data_names=[x[0] for x in data_train.provide_data],
60 label_names=[y[0] for y in data_train.provide_label],
67 train_data=data_train,
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| H A D | wordvec_subwords.py | 56 data_train = DataIterSubWords( variable
65 network = get_subword_net(data_train.vocab_size, num_label - 1, embedding_size)
73 data_names=[x[0] for x in data_train.provide_data],
74 label_names=[y[0] for y in data_train.provide_label],
81 train_data=data_train,
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| H A D | toy_softmax.py | 45 data_train = DataIterSoftmax(100000, batch_size, vocab_size, num_label, feature_size) variable
52 data_names=[x[0] for x in data_train.provide_data],
53 label_names=[y[0] for y in data_train.provide_label],
58 train_data=data_train,
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| H A D | toy_nce.py | 53 data_train = DataIterNce(100000, batch_size, vocab_size, num_label, feature_size) variable
59 data_names=[x[0] for x in data_train.provide_data],
60 label_names=[y[0] for y in data_train.provide_label],
66 train_data=data_train,
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/nce-loss/ |
| H A D | wordvec.py | 42 data_train = DataIterWords("./data/text8", batch_size, num_label) variable
44 network = get_word_net(data_train.vocab_size, num_label - 1)
52 data_names=[x[0] for x in data_train.provide_data],
53 label_names=[y[0] for y in data_train.provide_label],
60 train_data=data_train,
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| H A D | lstm_word.py | 49 data_train = DataIterLstm("./data/text8", batch_size, seq_len, num_label, init_states) variable
51 network = get_lstm_net(data_train.vocab_size, seq_len, num_lstm_layer, num_hidden)
59 data_names=[x[0] for x in data_train.provide_data],
60 label_names=[y[0] for y in data_train.provide_label],
67 train_data=data_train,
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| H A D | wordvec_subwords.py | 56 data_train = DataIterSubWords( variable
65 network = get_subword_net(data_train.vocab_size, num_label - 1, embedding_size)
73 data_names=[x[0] for x in data_train.provide_data],
74 label_names=[y[0] for y in data_train.provide_label],
81 train_data=data_train,
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| H A D | toy_softmax.py | 45 data_train = DataIterSoftmax(100000, batch_size, vocab_size, num_label, feature_size) variable
52 data_names=[x[0] for x in data_train.provide_data],
53 label_names=[y[0] for y in data_train.provide_label],
58 train_data=data_train,
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| H A D | toy_nce.py | 53 data_train = DataIterNce(100000, batch_size, vocab_size, num_label, feature_size) variable
59 data_names=[x[0] for x in data_train.provide_data],
60 label_names=[y[0] for y in data_train.provide_label],
66 train_data=data_train,
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/miscellaneous/ |
| H A D | plot_kernel_approximation.py | 65 data_train, targets_train = (data[: n_samples // 2], digits.target[: n_samples // 2]) variable
91 kernel_svm.fit(data_train, targets_train)
96 linear_svm.fit(data_train, targets_train)
110 nystroem_approx_svm.fit(data_train, targets_train)
114 fourier_approx_svm.fit(data_train, targets_train)
194 pca = PCA(n_components=8).fit(data_train)
196 X = pca.transform(data_train)
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| /dports/graphics/opencv/opencv-4.5.3/samples/cpp/ |
| H A D | logistic_regression.cpp | 59 Mat data_train, data_test; in main() local
65 data_train.push_back(data.row(i)); in main()
74 cout << "training/testing samples count: " << data_train.rows << "/" << data_test.rows << endl; in main()
77 showImage(data_train, 28, "train data"); in main()
90 lr1->train(data_train, ROW_SAMPLE, labels_train); in main()
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/tests/python/train/ |
| H A D | test_bucketing.py | 40 data_train = mx.rnn.BucketSentenceIter(train_sent, batch_size, buckets=buckets,
45 return (data_train, data_val)
67 …data_train, data_val = prepare_bucketing_data(buckets, len_vocab, batch_size, invalid_label, num_s…
94 default_bucket_key=data_train.default_bucket_key,
99 train_data=data_train,
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/tests/python/train/ |
| H A D | test_bucketing.py | 40 data_train = mx.rnn.BucketSentenceIter(train_sent, batch_size, buckets=buckets,
45 return (data_train, data_val)
67 …data_train, data_val = prepare_bucketing_data(buckets, len_vocab, batch_size, invalid_label, num_s…
94 default_bucket_key=data_train.default_bucket_key,
99 train_data=data_train,
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/rnn/old/ |
| H A D | gru_bucketing.py | 63 data_train = BucketSentenceIter("./data/sherlockholmes.train.txt", vocab, variable
69 data_train = DummyIter(data_train) variable
90 model.fit(X=data_train, eval_data=data_val,
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| H A D | lstm_bucketing.py | 65 data_train = BucketSentenceIter("./data/sherlockholmes.train.txt", vocab, variable
71 data_train = DummyIter(data_train) variable
92 model.fit(X=data_train, eval_data=data_val, kvstore='device',
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/rnn/old/ |
| H A D | gru_bucketing.py | 63 data_train = BucketSentenceIter("./data/sherlockholmes.train.txt", vocab, variable
69 data_train = DummyIter(data_train) variable
90 model.fit(X=data_train, eval_data=data_val,
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| H A D | lstm_bucketing.py | 65 data_train = BucketSentenceIter("./data/sherlockholmes.train.txt", vocab, variable
71 data_train = DummyIter(data_train) variable
92 model.fit(X=data_train, eval_data=data_val, kvstore='device',
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| /dports/misc/py-gluonnlp/gluon-nlp-0.10.0/scripts/machine_translation/ |
| H A D | dataprocessor.py | 131 data_train = nlp.data.IWSLT2015('train', src_lang=src_lang, tgt_lang=tgt_lang)
137 data_train = nlp.data.WMT2016BPE('train', src_lang=src_lang, tgt_lang=tgt_lang)
143 data_train = nlp.data.WMT2014BPE('train', src_lang=src_lang, tgt_lang=tgt_lang)
150 data_train = _dataset.TOY('train', src_lang=src_lang, tgt_lang=tgt_lang)
155 src_vocab, tgt_vocab = data_train.src_vocab, data_train.tgt_vocab
158 data_train_processed = process_dataset(data_train, src_vocab, tgt_vocab,
259 def make_dataloader(data_train, data_val, data_test, args, argument
262 train_data_loader = get_dataloader(data_train, args, dataset_type='train',
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/text/ |
| H A D | plot_document_classification_20newsgroups.py | 141 data_train = fetch_20newsgroups( variable
151 target_names = data_train.target_names
158 data_train_size_mb = size_mb(data_train.data)
162 "%d documents - %0.3fMB (training set)" % (len(data_train.data), data_train_size_mb)
169 y_train, y_test = data_train.target, data_test.target
177 X_train = vectorizer.transform(data_train.data)
180 X_train = vectorizer.fit_transform(data_train.data)
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/tests/python/unittest/ |
| H A D | test_io.py | 342 data_train = mx.io.LibSVMIter(data_libsvm=data_path, label_libsvm=label_path,
348 for batch in iter(data_train):
350 data = data_train.getdata()
374 for batch in data_train:
384 data_train.reset()
403 data_train = mx.io.LibSVMIter(data_libsvm=data_path, label_libsvm=label_path,
405 for batch in iter(data_train):
406 data_train.get_data().asnumpy()
443 data_train = mx.io.CSVIter(data_csv=data_path, data_shape=(8, 8),
446 for batch in iter(data_train):
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