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integration_tests/basic_full_flow.py
54 строки
2 KB
Roman Kazantsev
Add preliminary support of OpenVINO as Keras 3 backend (#19727)
18 дек 2024, 04:40
Не верифицирован
18 дек 2024, 04:40
bce0f5b
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import numpy as np import pytest import keras from keras.src import layers from keras.src import losses from keras.src import metrics from keras.src import optimizers from keras.src import testing class MyModel(keras.Model): def __init__(self, hidden_dim, output_dim, **kwargs): super().__init__(**kwargs) self.hidden_dim = hidden_dim self.output_dim = output_dim self.dense1 = layers.Dense(hidden_dim, activation="relu") self.dense2 = layers.Dense(hidden_dim, activation="relu") self.dense3 = layers.Dense(output_dim) def call(self, x): x = self.dense1(x) x = self.dense2(x) return self.dense3(x) class BasicFlowTest(testing.TestCase): @pytest.mark.requires_trainable_backend def test_basic_fit(self): model = MyModel(hidden_dim=2, output_dim=1) x = np.random.random((128, 4)) y = np.random.random((128, 4)) batch_size = 32 epochs = 3 model.compile( optimizer=optimizers.SGD(learning_rate=0.001), loss=losses.MeanSquaredError(), metrics=[metrics.MeanSquaredError()], ) output_before_fit = model(x) model.fit( x, y, batch_size=batch_size, epochs=epochs, validation_split=0.2 ) output_after_fit = model(x) self.assertNotAllClose(output_before_fit, output_after_fit) def test_basic_fit_no_training(self): model = MyModel(hidden_dim=2, output_dim=1) x = np.random.random((128, 4)) model.predict(x) model(x)