/dports/math/py-keras/Keras-2.4.3/tests/integration_tests/ |
H A D | test_datasets.py | 19 (x_train, y_train), (x_test, y_test) = cifar10.load_data() 20 assert len(x_train) == len(y_train) == 50000 23 assert len(x_train) == len(y_train) == 50000 26 assert len(x_train) == len(y_train) == 50000 36 assert len(x_train) == len(y_train) 38 assert len(x_train) + len(x_test) == 11228 40 assert len(x_train) == len(y_train) 52 assert len(x_train) == len(y_train) == 60000 63 assert len(x_train) == len(y_train) 75 assert len(x_train) == len(y_train) [all …]
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H A D | test_temporal_data_tasks.py | 22 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=200, 32 input_shape=(x_train.shape[1], x_train.shape[2]))) 38 history = model.fit(x_train, y_train, epochs=5, batch_size=10, 54 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=200, 62 inputs = layers.Input(shape=(x_train.shape[1], x_train.shape[2])) 69 history = model.fit(x_train, y_train, epochs=5, batch_size=10, 81 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=200, 88 input_shape=(x_train.shape[1], x_train.shape[2]))) 90 history = model.fit(x_train, y_train, epochs=5, batch_size=16, 103 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=100, [all …]
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H A D | test_vector_data_tasks.py | 18 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=500, 28 layers.Dense(16, input_shape=(x_train.shape[-1],), activation='relu'), 37 history = model.fit(x_train, y_train, epochs=15, batch_size=16, 46 (x_train, y_train), _ = get_test_data(num_train=500, 52 inputs = layers.Input(shape=(x_train.shape[-1],)) 61 history = model.fit(x_train, y_train, epochs=15, batch_size=16, 62 validation_data=(x_train, y_train), 72 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=500, 79 layers.Dense(16, input_shape=(x_train.shape[-1],), activation='tanh'), 84 history = model.fit(x_train, y_train, epochs=20, batch_size=16,
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/dports/www/chromium-legacy/chromium-88.0.4324.182/third_party/opus/src/training/ |
H A D | rnn_train.py | 77 x_train = np.reshape(x_train, (nb_sequences, window_size, 25)) variable 90 x_train = x_train.astype('float32') variable 109 model.fit(x_train, y_train, 115 model.fit(x_train, y_train, 125 model.fit(x_train, y_train, 131 model.fit(x_train, y_train, 137 model.fit(x_train, y_train, 143 model.fit(x_train, y_train, 149 model.fit(x_train, y_train, 155 model.fit(x_train, y_train, [all …]
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/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/third_party/opus/src/training/ |
H A D | rnn_train.py | 77 x_train = np.reshape(x_train, (nb_sequences, window_size, 25)) variable 90 x_train = x_train.astype('float32') variable 109 model.fit(x_train, y_train, 115 model.fit(x_train, y_train, 125 model.fit(x_train, y_train, 131 model.fit(x_train, y_train, 137 model.fit(x_train, y_train, 143 model.fit(x_train, y_train, 149 model.fit(x_train, y_train, 155 model.fit(x_train, y_train, [all …]
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/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 83 y_train = g(x_train) + noiseSample 92 cloud = ot.Cloud(x_train, y_train) 142 designMatrix = basis(x_train) 167 cloud = ot.Cloud(x_train, y_train) 189 cloud = ot.Cloud(x_train, y_train) 200 curve = ot.Curve([x_train[i], x_train[i]], [ 223 designMatrix = basis(x_train) 235 total_degree, x_train, y_train) 247 cloud = ot.Cloud(x_train, y_train) [all …]
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/dports/math/py-keras/Keras-2.4.3/examples/ |
H A D | mnist_cnn.py | 24 (x_train, y_train), (x_test, y_test) = mnist.load_data() 27 x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) variable 31 x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) 35 x_train = x_train.astype('float32') variable 37 x_train /= 255 39 print('x_train shape:', x_train.shape) 40 print(x_train.shape[0], 'train samples') 63 model.fit(x_train, y_train,
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H A D | mnist_swwae.py | 104 (x_train, _), (x_test, _) = mnist.load_data() 106 x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) variable 108 x_train = x_train.astype('float32') variable 110 x_train /= 255 112 print('x_train shape:', x_train.shape) 113 print(x_train.shape[0], 'train samples') 131 x_train = np.pad(x_train, [[0, 0], [0, 0], [2, 2], [2, 2]], variable 137 x_train = x_train[:, :, :-1, :-1] variable 145 input_shape = x_train.shape[1:] 176 model.fit(x_train, x_train,
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H A D | mnist_hierarchical_rnn.py | 53 (x_train, y_train), (x_test, y_test) = mnist.load_data() 56 x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) variable 58 x_train = x_train.astype('float32') variable 60 x_train /= 255 62 print('x_train shape:', x_train.shape) 63 print(x_train.shape[0], 'train samples') 70 row, col, pixel = x_train.shape[1:] 89 model.fit(x_train, y_train,
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H A D | mnist_irnn.py | 35 (x_train, y_train), (x_test, y_test) = mnist.load_data() 37 x_train = x_train.reshape(x_train.shape[0], -1, 1) variable 39 x_train = x_train.astype('float32') variable 41 x_train /= 255 43 print('x_train shape:', x_train.shape) 44 print(x_train.shape[0], 'train samples') 57 input_shape=x_train.shape[1:])) 65 model.fit(x_train, y_train,
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H A D | mnist_sklearn_wrapper.py | 24 (x_train, y_train), (x_test, y_test) = mnist.load_data() 27 x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) variable 31 x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) 35 x_train = x_train.astype('float32') variable 37 x_train /= 255 91 validator.fit(x_train, y_train)
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H A D | imdb_fasttext.py | 80 (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features) 81 print(len(x_train), 'train sequences') 84 np.mean(list(map(len, x_train)), dtype=int))) 92 for input_list in x_train: 108 x_train = add_ngram(x_train, token_indice, ngram_range) variable 111 np.mean(list(map(len, x_train)), dtype=int))) 116 x_train = sequence.pad_sequences(x_train, maxlen=maxlen) variable 118 print('x_train shape:', x_train.shape) 141 model.fit(x_train, y_train,
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H A D | mnist_transfer_cnn.py | 44 x_train = train[0].reshape((train[0].shape[0],) + input_shape) 46 x_train = x_train.astype('float32') 48 x_train /= 255 50 print('x_train shape:', x_train.shape) 51 print(x_train.shape[0], 'train samples') 63 model.fit(x_train, y_train, 75 (x_train, y_train), (x_test, y_test) = mnist.load_data() 78 x_train_lt5 = x_train[y_train < 5] 83 x_train_gte5 = x_train[y_train >= 5]
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H A D | mnist_mlp.py | 21 (x_train, y_train), (x_test, y_test) = mnist.load_data() 23 x_train = x_train.reshape(60000, 784) variable 25 x_train = x_train.astype('float32') variable 27 x_train /= 255 29 print(x_train.shape[0], 'train samples') 49 history = model.fit(x_train, y_train,
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H A D | cifar10_cnn.py | 26 (x_train, y_train), (x_test, y_test) = cifar10.load_data() 27 print('x_train shape:', x_train.shape) 28 print(x_train.shape[0], 'train samples') 37 input_shape=x_train.shape[1:])) 66 x_train = x_train.astype('float32') variable 68 x_train /= 255 73 model.fit(x_train, y_train, 112 datagen.fit(x_train) 115 model.fit_generator(datagen.flow(x_train, y_train,
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/dports/math/py-keras/Keras-2.4.3/tests/ |
H A D | test_loss_weighting.py | 53 return ((x_train, y_train), (x_test, y_test), 78 ((x_train, y_train), (x_test, y_test), 81 model.fit(x_train, y_train, batch_size=batch_size, 93 model.train_on_batch(x_train[:32], y_train[:32], 103 ((x_train, y_train), (x_test, y_test), 114 model.train_on_batch(x_train[:32], y_train[:32], 116 model.test_on_batch(x_train[:32], y_train[:32], 123 ((x_train, y_train), (x_test, y_test), 126 temporal_x_train = np.reshape(x_train, (len(x_train), 1, x_train.shape[1])) 164 ((x_train, y_train), (x_test, y_test), [all …]
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/dports/science/py-dipy/dipy-1.4.1/dipy/nn/tests/ |
H A D | test_tf.py | 23 (x_train, y_train), (x_test, y_test) = mnist.load_data() 24 x_train, x_test = x_train / 255.0, x_test / 255.0 37 hist = model.fit(x_train, y_train, epochs=epochs) 49 (x_train, y_train), (x_test, y_test) = mnist.load_data() 50 x_train, x_test = x_train / 255.0, x_test / 255.0 53 hist = slp.fit(x_train, y_train, epochs=epochs) 69 (x_train, y_train), (x_test, y_test) = mnist.load_data() 70 x_train, x_test = x_train / 255.0, x_test / 255.0 73 hist = mlp.fit(x_train, y_train, epochs=epochs)
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/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 54 print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape) 62 model.fit(x_train, y_train, 65 validation_data=(x_train, y_train))
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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 54 print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape) 62 model.fit(x_train, y_train, 65 validation_data=(x_train, y_train))
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/dports/audio/rnnoise/rnnoise-1cbdbcf/src/ |
H A D | rnn_train.py | 42 x_train = all_data[:nb_sequences*window_size, :-22] 43 x_train = np.reshape(x_train, (nb_sequences, window_size, 22)) 51 x_train = x_train.astype('float32') 54 print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape) 62 model.fit(x_train, y_train, 65 validation_data=(x_train, y_train))
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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 54 print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape) 62 model.fit(x_train, y_train, 65 validation_data=(x_train, y_train))
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/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 54 print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape) 62 model.fit(x_train, y_train, 65 validation_data=(x_train, y_train))
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/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 55 print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape) 63 model.fit(x_train, y_train, 66 validation_data=(x_train, y_train))
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/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 55 print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape) 63 model.fit(x_train, y_train, 66 validation_data=(x_train, y_train))
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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 58 y_train = g(x_train) 59 n_train = x_train.getSize() 80 def plot_data_train(x_train, y_train): argument 82 graph_train = ot.Cloud(x_train, y_train) 101 graph.add(plot_data_train(x_train, y_train)) 143 graph.add(plot_data_train(x_train, y_train)) 176 def deleteCommonValues(x_train, x_test): argument 182 for x_train_value in x_train: 195 np.array(x_train.asPoint()), np.array(vertices.asPoint())) [all …]
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