(self)
| 20 | self.assertEqual(4, len(result.shape)) |
| 21 | |
| 22 | def test_tf_keras(self): |
| 23 | x_train = np.random.random((100, 28, 28)) |
| 24 | y_train = np.random.randint(10, size=(100, 1)) |
| 25 | x_test = np.random.random((20, 28, 28)) |
| 26 | y_test = np.random.randint(10, size=(20, 1)) |
| 27 | |
| 28 | model = tf.keras.models.Sequential([ |
| 29 | tf.keras.layers.Flatten(input_shape=(28, 28)), |
| 30 | tf.keras.layers.Dense(128, activation='relu'), |
| 31 | tf.keras.layers.Dropout(0.2), |
| 32 | tf.keras.layers.Dense(10, activation='softmax') |
| 33 | ]) |
| 34 | |
| 35 | model.compile( |
| 36 | optimizer='adam', |
| 37 | loss='sparse_categorical_crossentropy', |
| 38 | metrics=['accuracy']) |
| 39 | |
| 40 | model.fit(x_train, y_train, epochs=1) |
| 41 | |
| 42 | result = model.evaluate(x_test, y_test) |
| 43 | self.assertEqual(2, len(result)) |
| 44 | |
| 45 | # exercices pydot path. |
| 46 | tf.keras.utils.plot_model(model, to_file="tf_plot_model.png") |
| 47 | self.assertTrue(os.path.isfile("tf_plot_model.png")) |
| 48 | |
| 49 | def test_lstm(self): |
| 50 | x_train = np.random.random((100, 28, 28)) |
nothing calls this directly
no outgoing calls
no test coverage detected