| /external/libopus/training/ |
| D | rnn_train.py | 79 y_train = np.copy(all_data[:nb_sequences*window_size, -2:]) variable
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
93 print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape)
109 model.fit(x_train, y_train,
111 epochs=10, validation_data=(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,
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| /external/rnnoise/src/ |
| 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))
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| /external/libopus/scripts/ |
| 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))
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| /external/tensorflow/tensorflow/compiler/xla/g3doc/tutorials/ |
| D | autoclustering_xla.ipynb | 115 " (x_train, y_train) = result['train']['image'],result['train']['label']\n",
122 " y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)\n",
124 " return ((x_train, y_train), (x_test, y_test))\n",
126 "(x_train, y_train), (x_test, y_test) = load_data()"
201 "def train_model(model, x_train, y_train, x_test, y_test, epochs=25):\n",
202 …" model.fit(x_train, y_train, batch_size=256, epochs=epochs, validation_data=(x_test, y_test), sh…
204 "def warmup(model, x_train, y_train, x_test, y_test):\n",
207 " train_model(model, x_train, y_train, x_test, y_test, epochs=1)\n",
210 "warmup(model, x_train, y_train, x_test, y_test)\n",
211 "%time train_model(model, x_train, y_train, x_test, y_test)\n",
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| /external/rnnoise/training/ |
| D | rnn_train.py | 96 y_train = np.copy(all_data[:nb_sequences*window_size, 42:64]) variable
97 y_train = np.reshape(y_train, (nb_sequences, window_size, 22)) variable
109 print(len(x_train), 'train sequences. x shape =', x_train.shape, 'y shape = ', y_train.shape)
112 model.fit(x_train, [y_train, vad_train],
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| /external/tensorflow/tensorflow/python/profiler/integration_test/ |
| D | mnist_testing_utils.py | 25 y_train = tf.ones([batch_size * steps_per_epoch, 1], dtype=tf.dtypes.int32)
26 train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train))
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| /external/tensorflow/tensorflow/lite/examples/experimental_new_converter/ |
| D | keras_lstm.ipynb | 116 "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
130 " y_train = y_train[:_TRAINING_DATA_COUNT]\n",
132 "model.fit(x_train, y_train, epochs=_EPOCHS)\n",
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| D | Keras_LSTM_fusion_Codelab.ipynb | 100 "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
114 " y_train = y_train[:_TRAINING_DATA_COUNT]\n",
116 "model.fit(x_train, y_train, epochs=_EPOCHS)\n",
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| /external/tensorflow/tensorflow/python/debug/examples/v2/ |
| D | debug_mnist_v2.py | 220 x_train, y_train = next(train_batches)
226 loss_val = loss(y, y_train)
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| /external/tensorflow/tensorflow/lite/g3doc/tutorials/ |
| D | pose_classification.ipynb | 867 "# Split training data (X, y) into (X_train, y_train) and (X_val, y_val)\n",
868 "X_train, X_val, y_train, y_val = train_test_split(X, y,\n",
1050 "history = model.fit(X_train, y_train,\n",
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