| /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)
40 assert len(x_train) == len(y_train)
52 assert len(x_train) == len(y_train) == 60000
61 (x_train, y_train), (x_test, y_test) = imdb.load_data()
63 assert len(x_train) == len(y_train)
75 assert len(x_train) == len(y_train)
[all …]
|
| H A D | test_temporal_data_tasks.py | 22 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=200,
27 y_train = to_categorical(y_train)
33 model.add(layers.Dense(y_train.shape[-1], activation='softmax'))
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,
59 y_train = to_categorical(y_train)
64 outputs = layers.Dense(y_train.shape[-1], activation='softmax')(x)
69 history = model.fit(x_train, y_train, epochs=5, batch_size=10,
87 model.add(layers.LSTM(y_train.shape[-1],
90 history = model.fit(x_train, y_train, epochs=5, batch_size=16,
[all …]
|
| H A D | test_image_data_tasks.py | 15 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=500,
20 y_train = to_categorical(y_train)
37 history = model.fit(x_train, y_train, epochs=15, batch_size=16,
49 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=500,
54 y_train = to_categorical(y_train)
70 history = model.fit_generator(img_gen.flow(x_train, y_train, batch_size=16),
76 model.evaluate_generator(img_gen.flow(x_train, y_train, batch_size=16))
|
| H A D | test_vector_data_tasks.py | 18 (x_train, y_train), (x_test, y_test) = get_test_data(num_train=500,
23 y_train = to_categorical(y_train)
37 history = model.fit(x_train, y_train, epochs=15, batch_size=16,
46 (x_train, y_train), _ = get_test_data(num_train=500,
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,
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 D | rnn_train.py | 80 y_train = np.reshape(y_train, (nb_sequences, window_size, 2)) variable
83 for s in y_train:
91 y_train = y_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,
[all …]
|
| /dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/third_party/opus/src/training/ |
| H A D | rnn_train.py | 80 y_train = np.reshape(y_train, (nb_sequences, window_size, 2)) variable
83 for s in y_train:
91 y_train = y_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,
[all …]
|
| /dports/math/py-keras/Keras-2.4.3/tests/ |
| H A D | test_loss_weighting.py | 41 int_y_train = y_train.copy()
43 y_train = np_utils.to_categorical(y_train, num_classes)
53 return ((x_train, y_train), (x_test, y_test),
78 ((x_train, y_train), (x_test, y_test),
93 model.train_on_batch(x_train[:32], y_train[:32],
103 ((x_train, y_train), (x_test, y_test),
116 model.test_on_batch(x_train[:32], y_train[:32],
123 ((x_train, y_train), (x_test, y_test),
131 temporal_y_train = np.reshape(y_train, (len(y_train), 1, y_train.shape[1]))
164 ((x_train, y_train), (x_test, y_test),
[all …]
|
| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ensemble/_hist_gradient_boosting/tests/ |
| H A D | test_loss.py | 175 y_train = rng.normal(size=100)
178 assert baseline_prediction.dtype == y_train.dtype
190 y_train = rng.normal(size=100)
193 assert baseline_prediction.dtype == y_train.dtype
208 assert y_train.sum() > 0
217 y_train.fill(0.0)
227 y_train = y_train.astype(np.float64)
241 p = y_train.mean()
251 y_train = y_train.astype(np.float64)
253 y_train, None, prediction_dim
[all …]
|
| /dports/math/py-keras/Keras-2.4.3/tests/keras/callbacks/ |
| H A D | callbacks_test.py | 126 y_train = np_utils.to_categorical(y_train)
354 y_train = np_utils.to_categorical(y_train)
393 y_train = np_utils.to_categorical(y_train)
440 y_train = np_utils.to_categorical(y_train)
517 y_train = np_utils.to_categorical(y_train)
718 y_train = np_utils.to_categorical(y_train)
736 y_train = np_utils.to_categorical(y_train)
821 y_train = np_utils.to_categorical(y_train)
874 y_train = np_utils.to_categorical(y_train)
903 y_train = np_utils.to_categorical(y_train)
[all …]
|
| H A D | tensorboard_test.py | 62 (X_train, y_train), (X_test, y_test) = get_data_callbacks()
64 y_train = np_utils.to_categorical(y_train)
110 model.fit(X_train, y_train, batch_size=batch_size,
115 model.fit(X_train, y_train, batch_size=batch_size,
140 (X_train, y_train), (X_test, y_test) = get_data_callbacks(
144 y_train = np_utils.to_categorical(y_train)
213 (x_train, y_train), (x_test, y_test) = get_data_callbacks(
217 y_train = np_utils.to_categorical(y_train)
250 (x_train, y_train), _ = get_data_callbacks(num_train=10,
253 y_train = np_utils.to_categorical(y_train)
[all …]
|
| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ensemble/tests/ |
| H A D | test_bagging.py | 139 ).fit(X_train, y_train)
256 ).fit(X_train, y_train)
266 ).fit(X_train, y_train)
268 assert base_estimator.score(X_train, y_train) > ensemble.score(X_train, y_train)
275 ).fit(X_train, y_train)
295 ).fit(X_train, y_train)
305 ).fit(X_train, y_train)
522 X_train, y_train
532 X_train, y_train
571 X_train, y_train
[all …]
|
| /dports/math/openturns/openturns-1.18/python/doc/examples/meta_modeling/general_purpose_metamodels/ |
| H A D | plot_overfitting_model_selection.py | 83 y_train = g(x_train) + noiseSample variable
92 cloud = ot.Cloud(x_train, y_train)
167 cloud = ot.Cloud(x_train, y_train)
189 cloud = ot.Cloud(x_train, y_train)
235 total_degree, x_train, y_train)
247 cloud = ot.Cloud(x_train, y_train)
306 total_degree, x_train, y_train)
361 total_degree, x_train, y_train)
363 responseSurface, basis, x_train, y_train)
402 x_train, y_train = createDataset(n_train) variable
[all …]
|
| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/gluon/house_prices/ |
| H A D | kaggle_k_fold_cross_validation.py | 57 y_train = train.SalePrice.as_matrix() variable
60 y_train = nd.array(y_train) variable
61 y_train.reshape((num_train, 1))
66 def get_rmse_log(net, X_train, y_train): argument
71 nd.log(clipped_preds), nd.log(y_train))).asscalar() / num_train)
82 def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate, argument
85 dataset_train = gluon.data.ArrayDataset(X_train, y_train)
99 avg_loss = get_rmse_log(net, X_train, y_train)
104 def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train, argument
147 k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,
[all …]
|
| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/gluon/house_prices/ |
| H A D | kaggle_k_fold_cross_validation.py | 57 y_train = train.SalePrice.as_matrix() variable
60 y_train = nd.array(y_train) variable
61 y_train.reshape((num_train, 1))
66 def get_rmse_log(net, X_train, y_train): argument
71 nd.log(clipped_preds), nd.log(y_train))).asscalar() / num_train)
82 def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate, argument
85 dataset_train = gluon.data.ArrayDataset(X_train, y_train)
99 avg_loss = get_rmse_log(net, X_train, y_train)
104 def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train, argument
147 k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,
[all …]
|
| /dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/semi_supervised/ |
| H A D | plot_semi_supervised_newsgroups.py | 75 def eval_and_print_metrics(clf, X_train, y_train, X_test, y_test):
77 print("Unlabeled samples in training set:", sum(1 for x in y_train if x == -1))
78 clf.fit(X_train, y_train)
90 X_train, X_test, y_train, y_test = train_test_split(X, y)
93 eval_and_print_metrics(pipeline, X_train, y_train, X_test, y_test)
96 y_mask = np.random.rand(len(y_train)) < 0.2
100 list, zip(*((x, y) for x, y, m in zip(X_train, y_train, y_mask) if m))
106 y_train[~y_mask] = -1
108 eval_and_print_metrics(st_pipeline, X_train, y_train, X_test, y_test)
111 eval_and_print_metrics(ls_pipeline, X_train, y_train, X_test, y_test)
|
| /dports/science/py-nilearn/nilearn-0.8.1/examples/02_decoding/ |
| H A D | plot_miyawaki_reconstruction.py | 66 y_train = [] variable
77 y_train = np.vstack([y[:-2] for y in y_train]).astype(float) variable
81 n_pixels = y_train.shape[1]
117 yt_tall = [np.dot(height_tf, m) for m in y_train]
118 yt_large = [np.dot(m, width_tf) for m in y_train]
126 y_train = np.asarray(y_train) variable
129 X_train = X_train[y_train[:, 0] != -1]
130 y_train = y_train[y_train[:, 0] != -1] variable
149 n_clfs = y_train.shape[1]
150 for i in range(y_train.shape[1]):
[all …]
|
| /dports/multimedia/obs-studio/obs-studio-27.1.3/plugins/obs-filters/rnnoise/src/ |
| H A D | rnn_train.py | 45 y_train = np.copy(all_data[:nb_sequences*window_size, -22:]) variable
46 y_train = np.reshape(y_train, (nb_sequences, window_size, 22)) variable
52 y_train = y_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 D | rnn_train.py | 45 y_train = np.copy(all_data[:nb_sequences*window_size, -22:]) variable
46 y_train = np.reshape(y_train, (nb_sequences, window_size, 22)) variable
52 y_train = y_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 D | rnn_train.py | 45 y_train = np.copy(all_data[:nb_sequences*window_size, -22:])
46 y_train = np.reshape(y_train, (nb_sequences, window_size, 22))
52 y_train = y_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 D | rnn_train.py | 45 y_train = np.copy(all_data[:nb_sequences*window_size, -22:]) variable
46 y_train = np.reshape(y_train, (nb_sequences, window_size, 22)) variable
52 y_train = y_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 D | rnn_train.py | 45 y_train = np.copy(all_data[:nb_sequences*window_size, -22:]) variable
46 y_train = np.reshape(y_train, (nb_sequences, window_size, 22)) variable
52 y_train = y_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 D | rnn_train.py | 48 y_train = np.copy(all_data[:nb_sequences*window_size, -2:]) variable
49 y_train = np.reshape(y_train, (nb_sequences, window_size, 2)) variable
53 y_train = y_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 D | rnn_train.py | 48 y_train = np.copy(all_data[:nb_sequences*window_size, -2:]) variable
49 y_train = np.reshape(y_train, (nb_sequences, window_size, 2)) variable
53 y_train = y_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/py-keras/Keras-2.4.3/examples/ |
| H A D | mnist_transfer_cnn.py | 55 y_train = keras.utils.to_categorical(train[1], num_classes)
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]
79 y_train_lt5 = y_train[y_train < 5]
83 x_train_gte5 = x_train[y_train >= 5]
84 y_train_gte5 = y_train[y_train >= 5] - 5
|
| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ensemble/_hist_gradient_boosting/ |
| H A D | loss.py | 98 def get_baseline_prediction(self, y_train, sample_weight, prediction_dim):
177 def get_baseline_prediction(self, y_train, sample_weight, prediction_dim):
178 return np.average(y_train, weights=sample_weight)
240 def get_baseline_prediction(self, y_train, sample_weight, prediction_dim):
242 return np.median(y_train)
244 return _weighted_percentile(y_train, sample_weight, 50)
328 y_pred = np.average(y_train, weights=sample_weight)
329 eps = np.finfo(y_train.dtype).eps
383 proba_positive_class = np.average(y_train, weights=sample_weight)
384 eps = np.finfo(y_train.dtype).eps
[all …]
|