Python Model.predict_generator方法代码示例

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import predict_generator [as 别名]

#.........这里部分代码省略.........
        model.compile(optimizer, loss='mse', sample_weight_mode={'lstm': 'temporal'})

    # `sample_weight_mode` does not match output_names.
    with pytest.raises(ValueError):
        model.compile(optimizer, loss='mse', sample_weight_mode=['temporal'])

    # `sample_weight_mode` matches output_names partially.
    with pytest.raises(ValueError):
        model.compile(optimizer, loss='mse', sample_weight_mode={'dense_1': 'temporal'})

    # `loss` does not exist.
    with pytest.raises(ValueError):
        model.compile(optimizer, loss=[])

    model.compile(optimizer, loss=['mse', 'mae'])
    model.compile(optimizer, loss='mse', loss_weights={'dense_1': 0.2, 'dropout': 0.8})
    model.compile(optimizer, loss='mse', loss_weights=[0.2, 0.8])

    # the rank of weight arrays should be 1.
    with pytest.raises(ValueError):
        out = model.train_on_batch([input_a_np, input_b_np],
                                   [output_a_np, output_b_np],
                                   sample_weight=[None, np.random.random((10, 20, 30))])

    model.compile(optimizer, loss='mse', sample_weight_mode={'dense_1': None, 'dropout': 'temporal'})
    model.compile(optimizer, loss='mse', sample_weight_mode=[None, 'temporal'])

    # the rank of output arrays should be at least 3D.
    with pytest.raises(ValueError):
        out = model.train_on_batch([input_a_np, input_b_np],
                                   [output_a_np, output_b_np],
                                   sample_weight=sample_weight)

    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)
    trained_epochs = []
    trained_batches = []
    out = model.fit_generator(generator=RandomSequence(3), steps_per_epoch=3, epochs=5,
                              initial_epoch=0, validation_data=RandomSequence(4),
                              validation_steps=3, callbacks=[tracker_cb])
    assert trained_epochs == [0, 1, 2, 3, 4]
    assert trained_batches == list(range(3)) * 5

    # steps_per_epoch will be equal to len of sequence if it's unspecified
    trained_epochs = []
    trained_batches = []
    out = model.fit_generator(generator=RandomSequence(3), epochs=5,
                              initial_epoch=0, validation_data=RandomSequence(4),
                              callbacks=[tracker_cb])
    assert trained_epochs == [0, 1, 2, 3, 4]
    assert trained_batches == list(range(12)) * 5

    # fit_generator will throw an exception if steps is unspecified for regular generator
    with pytest.raises(ValueError):
        def gen_data():
            while True:
                yield (np.asarray([]), np.asarray([]))
        out = model.fit_generator(generator=gen_data(), epochs=5,
                                  initial_epoch=0, validation_data=gen_data(),
                                  callbacks=[tracker_cb])

    # predict_generator output shape behavior should be consistent
    def expected_shape(batch_size, n_batches):
        return (batch_size * n_batches, 4), (batch_size * n_batches, 3)

    # Multiple outputs and one step.
    batch_size = 5
    sequence_length = 1
    shape_0, shape_1 = expected_shape(batch_size, sequence_length)
    out = model.predict_generator(RandomSequence(batch_size,
                                                 sequence_length=sequence_length))
    assert np.shape(out[0]) == shape_0 and np.shape(out[1]) == shape_1

    # Multiple outputs and multiple steps.
    batch_size = 5
    sequence_length = 2
    shape_0, shape_1 = expected_shape(batch_size, sequence_length)
    out = model.predict_generator(RandomSequence(batch_size,
                                                 sequence_length=sequence_length))
    assert np.shape(out[0]) == shape_0 and np.shape(out[1]) == shape_1

    # Create a model with a single output.
    single_output_model = Model([a, b], a_2)
    single_output_model.compile(optimizer, loss, metrics=[], sample_weight_mode=None)

    # Single output and one step.
    batch_size = 5
    sequence_length = 1
    shape_0, _ = expected_shape(batch_size, sequence_length)
    out = single_output_model.predict_generator(RandomSequence(batch_size,
                                                sequence_length=sequence_length))
    assert np.shape(out) == shape_0

    # Single output and multiple steps.
    batch_size = 5
    sequence_length = 2
    shape_0, _ = expected_shape(batch_size, sequence_length)
    out = single_output_model.predict_generator(RandomSequence(batch_size,
                                                sequence_length=sequence_length))
    assert np.shape(out) == shape_0