| /dports/multimedia/obs-studio/obs-studio-27.1.3/plugins/obs-filters/rnnoise/src/ |
| H A D | rnn_train.py | 42 x_train = all_data[:nb_sequences*window_size, :-22] variable
43 x_train = np.reshape(x_train, (nb_sequences, window_size, 22)) variable
51 x_train = x_train.astype('float32') variable
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| /dports/audio/mumble/mumble-1.3.3/3rdparty/rnnoise-src/src/ |
| H A D | rnn_train.py | 42 x_train = all_data[:nb_sequences*window_size, :-22] variable
43 x_train = np.reshape(x_train, (nb_sequences, window_size, 22)) variable
51 x_train = x_train.astype('float32') variable
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| /dports/audio/rnnoise-nu/rnnoise-nu-2626930/src/ |
| H A D | rnn_train.py | 42 x_train = all_data[:nb_sequences*window_size, :-22] variable
43 x_train = np.reshape(x_train, (nb_sequences, window_size, 22)) variable
51 x_train = x_train.astype('float32') variable
|
| /dports/audio/speech-denoiser-lv2/speech-denoiser-04cfba9/rnnoise/src/ |
| H A D | rnn_train.py | 42 x_train = all_data[:nb_sequences*window_size, :-22] variable
43 x_train = np.reshape(x_train, (nb_sequences, window_size, 22)) variable
51 x_train = x_train.astype('float32') variable
|
| /dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/third_party/opus/src/scripts/ |
| H A D | rnn_train.py | 45 x_train = all_data[:nb_sequences*window_size, :-2] variable
46 x_train = np.reshape(x_train, (nb_sequences, window_size, 25)) variable
52 x_train = x_train.astype('float32') variable
|
| /dports/www/chromium-legacy/chromium-88.0.4324.182/third_party/opus/src/scripts/ |
| H A D | rnn_train.py | 45 x_train = all_data[:nb_sequences*window_size, :-2] variable
46 x_train = np.reshape(x_train, (nb_sequences, window_size, 25)) variable
52 x_train = x_train.astype('float32') variable
|
| /dports/math/py-keras/Keras-2.4.3/examples/ |
| H A D | mnist_swwae.py | 106 x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) variable
108 x_train = x_train.astype('float32') variable
131 x_train = np.pad(x_train, [[0, 0], [0, 0], [2, 2], [2, 2]], variable
137 x_train = x_train[:, :, :-1, :-1] variable
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| H A D | mnist_mlp.py | 23 x_train = x_train.reshape(60000, 784) variable
25 x_train = x_train.astype('float32') variable
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| H A D | mnist_net2net.py | 92 x_train, x_test = map(preprocess_input, [x_train, x_test]) variable
227 def make_teacher_model(x_train, y_train, argument
253 x_train, y_train, argument
303 x_train, y_train, argument
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| H A D | mnist_irnn.py | 37 x_train = x_train.reshape(x_train.shape[0], -1, 1) variable
39 x_train = x_train.astype('float32') variable
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| H A D | mnist_cnn.py | 27 x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) variable
35 x_train = x_train.astype('float32') variable
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| H A D | antirectifier.py | 71 x_train = x_train.reshape(60000, 784) variable
73 x_train = x_train.astype('float32') variable
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| H A D | mnist_hierarchical_rnn.py | 56 x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) variable
58 x_train = x_train.astype('float32') variable
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| H A D | mnist_sklearn_wrapper.py | 27 x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) variable
35 x_train = x_train.astype('float32') variable
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| H A D | imdb_fasttext.py | 108 x_train = add_ngram(x_train, token_indice, ngram_range) variable
116 x_train = sequence.pad_sequences(x_train, maxlen=maxlen) variable
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| H A D | variational_autoencoder.py | 122 x_train = np.reshape(x_train, [-1, original_dim]) variable
124 x_train = x_train.astype('float32') / 255 variable
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| H A D | imdb_lstm.py | 36 x_train = sequence.pad_sequences(x_train, maxlen=maxlen) variable
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| H A D | mnist_denoising_autoencoder.py | 39 x_train = np.reshape(x_train, [-1, image_size, image_size, 1]) variable
41 x_train = x_train.astype('float32') / 255 variable
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| H A D | imdb_bidirectional_lstm.py | 29 x_train = sequence.pad_sequences(x_train, maxlen=maxlen) variable
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| H A D | variational_autoencoder_deconv.py | 122 x_train = np.reshape(x_train, [-1, image_size, image_size, 1]) variable
124 x_train = x_train.astype('float32') / 255 variable
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| /dports/www/chromium-legacy/chromium-88.0.4324.182/third_party/opus/src/training/ |
| H A D | rnn_train.py | 76 x_train = all_data[:nb_sequences*window_size, :-2] variable
77 x_train = np.reshape(x_train, (nb_sequences, window_size, 25)) variable
90 x_train = x_train.astype('float32') variable
|
| /dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/third_party/opus/src/training/ |
| H A D | rnn_train.py | 76 x_train = all_data[:nb_sequences*window_size, :-2] variable
77 x_train = np.reshape(x_train, (nb_sequences, window_size, 25)) variable
90 x_train = x_train.astype('float32') variable
|
| /dports/math/openturns/openturns-1.18/python/doc/examples/meta_modeling/general_purpose_metamodels/ |
| H A D | plot_overfitting_model_selection.py | 70 x_train = linearSample(0, 1, n_train) variable
215 def myPolynomialDataFitting(total_degree, x_train, y_train): argument
231 def myPolynomialCurveFittingGraph(total_degree, x_train, y_train): argument
402 x_train, y_train = createDataset(n_train) variable
410 x_train, y_train = createDataset(n_train) variable
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| /dports/math/openturns/openturns-1.18/python/doc/examples/meta_modeling/kriging_metamodel/ |
| H A D | plot_kriging_simulate.py | 57 x_train = ot.Sample([[x] for x in [1., 3., 4., 6., 7.9, 11., 11.5]]) variable
80 def plot_data_train(x_train, y_train): argument
176 def deleteCommonValues(x_train, x_test): argument
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/linear_model/ |
| H A D | plot_polynomial_interpolation.py | 69 x_train = np.linspace(0, 10, 100) variable
71 x_train = np.sort(rng.choice(x_train, size=20, replace=False)) variable
|