Python Model.fit_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 fit_generator [as 别名]
def test_warnings():
    a = Input(shape=(3,), name='input_a')
    b = Input(shape=(3,), name='input_b')

    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    b_2 = dp(b)

    model = Model([a, b], [a_2, b_2])

    optimizer = 'rmsprop'
    loss = 'mse'
    loss_weights = [1., 0.5]
    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    with pytest.warns(Warning) as w:
        out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
    warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
    assert warning_raised, 'No warning raised when using generator with processes.'

    with pytest.warns(None) as w:
        out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
    assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.'

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

#.........这里部分代码省略.........
    input_a_np = np.random.random((10, 3))
    input_b_np = np.random.random((10, 3))

    output_a_np = np.random.random((10, 4))
    output_b_np = np.random.random((10, 3))

    sample_weight = [None, np.random.random((10,))]
    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np],
                               sample_weight=sample_weight)

    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np],
                              sample_weight=sample_weight)

    # test accuracy metric
    model.compile(optimizer, loss, metrics=['acc'],
                  sample_weight_mode=None)

    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    assert len(out) == 5
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    assert len(out) == 5

    # this should also work
    model.compile(optimizer, loss, metrics={'dense_1': 'acc'},
                  sample_weight_mode=None)

    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    assert len(out) == 4
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    assert len(out) == 4

    # and this as well
    model.compile(optimizer, loss, metrics={'dense_1': ['acc']},
                  sample_weight_mode=None)

    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    assert len(out) == 4
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    assert len(out) == 4

    # test starting from non-zero initial epoch
    trained_epochs = []

    def on_epoch_begin(epoch, logs):
        trained_epochs.append(epoch)
    tracker_cb = LambdaCallback(on_epoch_begin=on_epoch_begin)
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np], nb_epoch=5, batch_size=4,
                    initial_epoch=2, callbacks=[tracker_cb])
    assert trained_epochs == [2, 3, 4]

    # test starting from non-zero initial epoch for generator too
    trained_epochs = []

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])
    out = model.fit_generator(gen_data(4), samples_per_epoch=10, nb_epoch=5,
                              initial_epoch=2, callbacks=[tracker_cb])
    assert trained_epochs == [2, 3, 4]

    # test with a custom metric function
    mse = lambda y_true, y_pred: K.mean(K.pow(y_true - y_pred, 2))

    def mse_powers(y_true, y_pred):
        m = mse(y_true, y_pred)
        return {
            'mse_squared': K.pow(m, 2),
            'mse_cubed': K.pow(m, 3)
        }

    model.compile(optimizer, loss, metrics=[mse, mse_powers],
                  sample_weight_mode=None)

    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    out_len = 1 + 2 * 4  # total loss, per layer: loss + 3 metrics
    assert len(out) == out_len
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    assert len(out) == out_len

    input_a_np = np.random.random((10, 3))
    input_b_np = np.random.random((10, 3))

    output_a_np = np.random.random((10, 4))
    output_b_np = np.random.random((10, 3))

    out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4, nb_epoch=1)
    out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
    out = model.predict([input_a_np, input_b_np], batch_size=4)

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

#.........这里部分代码省略.........
    # and this as well
    model.compile(optimizer, loss, metrics={'dense_1': ['acc']},
                  sample_weight_mode=None)

    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    assert len(out) == 4
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    assert len(out) == 4

    # test starting from non-zero initial epoch
    trained_epochs = []

    # define tracer callback
    def on_epoch_begin(epoch, logs):
        trained_epochs.append(epoch)

    tracker_cb = LambdaCallback(on_epoch_begin=on_epoch_begin)

    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np], epochs=5, batch_size=4,
                    initial_epoch=2, callbacks=[tracker_cb])
    assert trained_epochs == [2, 3, 4]

    # test starting from non-zero initial epoch for generator too
    trained_epochs = []

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    out = model.fit_generator(gen_data(4), steps_per_epoch=3, epochs=5,
                              initial_epoch=2, callbacks=[tracker_cb])
    assert trained_epochs == [2, 3, 4]

    # test with a custom metric function
    def mse(y_true, y_pred):
        return K.mean(K.pow(y_true - y_pred, 2))

    model.compile(optimizer, loss, metrics=[mse],
                  sample_weight_mode=None)

    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    out_len = 1 + 2 * (1 + 1)  # total loss + 2 outputs * (loss + metric)
    assert len(out) == out_len
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    assert len(out) == out_len

    input_a_np = np.random.random((10, 3))
    input_b_np = np.random.random((10, 3))

    output_a_np = np.random.random((10, 4))
    output_b_np = np.random.random((10, 3))

    out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4, epochs=1)
    out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
    out = model.predict([input_a_np, input_b_np], batch_size=4)

    # empty batch
    with pytest.raises(ValueError):
        def gen_data():
            while True:

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

#.........这里部分代码省略.........
                               [output_a_np, output_b_np])
    assert len(out) == 4
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    assert len(out) == 4

    # test starting from non-zero initial epoch
    trained_epochs = []
    trained_batches = []

    # define tracer callback
    def on_epoch_begin(epoch, logs):
        trained_epochs.append(epoch)

    def on_batch_begin(batch, logs):
        trained_batches.append(batch)

    tracker_cb = LambdaCallback(on_epoch_begin=on_epoch_begin,
                                on_batch_begin=on_batch_begin)

    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np], epochs=5, batch_size=4,
                    initial_epoch=2, callbacks=[tracker_cb])
    assert trained_epochs == [2, 3, 4]

    # test starting from non-zero initial epoch for generator too
    trained_epochs = []

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    out = model.fit_generator(gen_data(4), steps_per_epoch=3, epochs=5,
                              initial_epoch=2, callbacks=[tracker_cb])
    assert trained_epochs == [2, 3, 4]

    # test with a custom metric function
    def mse(y_true, y_pred):
        return K.mean(K.pow(y_true - y_pred, 2))

    model.compile(optimizer, loss, metrics=[mse],
                  sample_weight_mode=None)

    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    out_len = 1 + 2 * (1 + 1)  # total loss + 2 outputs * (loss + metric)
    assert len(out) == out_len
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    assert len(out) == out_len

    input_a_np = np.random.random((10, 3))
    input_b_np = np.random.random((10, 3))

    output_a_np = np.random.random((10, 4))
    output_b_np = np.random.random((10, 3))

    out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4, epochs=1)
    out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
    out = model.predict([input_a_np, input_b_np], batch_size=4)

    # enable verbose for evaluate_generator
    out = model.evaluate_generator(gen_data(4), steps=3, verbose=1)

    # empty batch

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import fit_generator [as 别名]
def test_model_with_external_loss():
    # None loss, only regularization loss.
    a = Input(shape=(3,), name='input_a')
    a_2 = Dense(4, name='dense_1',
                kernel_regularizer='l1',
                bias_regularizer='l2')(a)
    dp = Dropout(0.5, name='dropout')
    a_3 = dp(a_2)

    model = Model(a, [a_2, a_3])

    optimizer = 'rmsprop'
    loss = None
    model.compile(optimizer, loss, metrics=['mae'])

    input_a_np = np.random.random((10, 3))

    # test train_on_batch
    out = model.train_on_batch(input_a_np, None)
    out = model.test_on_batch(input_a_np, None)
    # fit
    out = model.fit(input_a_np, None)
    # evaluate
    out = model.evaluate(input_a_np, None)

    # No dropout, external loss.
    a = Input(shape=(3,), name='input_a')
    a_2 = Dense(4, name='dense_1')(a)
    a_3 = Dense(4, name='dense_2')(a)

    model = Model(a, [a_2, a_3])
    model.add_loss(K.mean(a_3 + a_2))

    optimizer = 'rmsprop'
    loss = None
    model.compile(optimizer, loss, metrics=['mae'])

    # test train_on_batch
    out = model.train_on_batch(input_a_np, None)
    out = model.test_on_batch(input_a_np, None)
    # fit
    out = model.fit(input_a_np, None)
    # evaluate
    out = model.evaluate(input_a_np, None)

    # Test fit with no external data at all.
    if K.backend() == 'tensorflow':
        import tensorflow as tf

        a = Input(tensor=tf.Variable(input_a_np, dtype=tf.float32))
        a_2 = Dense(4, name='dense_1')(a)
        a_2 = Dropout(0.5, name='dropout')(a_2)
        model = Model(a, a_2)
        model.add_loss(K.mean(a_2))

        model.compile(optimizer='rmsprop',
                      loss=None,
                      metrics=['mean_squared_error'])

        # test train_on_batch
        out = model.train_on_batch(None, None)
        out = model.test_on_batch(None, None)
        out = model.predict_on_batch(None)

        # test fit
        with pytest.raises(ValueError):
            out = model.fit(None, None, epochs=1, batch_size=10)
        out = model.fit(None, None, epochs=1, steps_per_epoch=1)

        # define a generator to produce x=None and y=None
        def data_tensors_generator():
            while True:
                yield (None, None)

        generator = data_tensors_generator()

        # test fit_generator for framework-native data tensors
        out = model.fit_generator(generator, epochs=1,
                                  steps_per_epoch=3)

        # test evaluate_generator for framework-native data tensors
        out = model.evaluate_generator(generator, steps=3)

        # test fit with validation data
        with pytest.raises(ValueError):
            out = model.fit(None, None,
                            epochs=1,
                            steps_per_epoch=None,
                            validation_steps=2)
        out = model.fit(None, None,
                        epochs=1,
                        steps_per_epoch=2,
                        validation_steps=2)

        # test evaluate
        with pytest.raises(ValueError):
            out = model.evaluate(None, None, batch_size=10)
        out = model.evaluate(None, None, steps=3)

        # test predict
#.........这里部分代码省略.........