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/dports/math/py-keras/Keras-2.4.3/tests/integration_tests/
H A Dtest_datasets.py19 (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)
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H A Dtest_temporal_data_tasks.py22 (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,
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H A Dtest_vector_data_tasks.py18 (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,
/dports/www/chromium-legacy/chromium-88.0.4324.182/third_party/opus/src/training/
H A Drnn_train.py77 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,
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/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/third_party/opus/src/training/
H A Drnn_train.py77 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,
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/dports/math/openturns/openturns-1.18/python/doc/examples/meta_modeling/general_purpose_metamodels/
H A Dplot_overfitting_model_selection.py70 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)
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/dports/math/py-keras/Keras-2.4.3/examples/
H A Dmnist_cnn.py24 (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,
H A Dmnist_swwae.py104 (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,
H A Dmnist_hierarchical_rnn.py53 (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,
H A Dmnist_irnn.py35 (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,
H A Dmnist_sklearn_wrapper.py24 (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)
H A Dimdb_fasttext.py80 (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,
H A Dmnist_transfer_cnn.py44 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]
H A Dmnist_mlp.py21 (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,
H A Dcifar10_cnn.py26 (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,
/dports/math/py-keras/Keras-2.4.3/tests/
H A Dtest_loss_weighting.py53 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),
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/dports/science/py-dipy/dipy-1.4.1/dipy/nn/tests/
H A Dtest_tf.py23 (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)
/dports/multimedia/obs-studio/obs-studio-27.1.3/plugins/obs-filters/rnnoise/src/
H A Drnn_train.py42 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))
/dports/audio/mumble/mumble-1.3.3/3rdparty/rnnoise-src/src/
H A Drnn_train.py42 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))
/dports/audio/rnnoise/rnnoise-1cbdbcf/src/
H A Drnn_train.py42 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))
/dports/audio/rnnoise-nu/rnnoise-nu-2626930/src/
H A Drnn_train.py42 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))
/dports/audio/speech-denoiser-lv2/speech-denoiser-04cfba9/rnnoise/src/
H A Drnn_train.py42 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))
/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/third_party/opus/src/scripts/
H A Drnn_train.py45 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))
/dports/www/chromium-legacy/chromium-88.0.4324.182/third_party/opus/src/scripts/
H A Drnn_train.py45 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))
/dports/math/openturns/openturns-1.18/python/doc/examples/meta_modeling/kriging_metamodel/
H A Dplot_kriging_simulate.py57 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()))
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