Python Model.evaluate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 evaluate [as 别名]
def test_sparse_placeholder_fit():
    test_inputs = [sparse.random(6, 3, density=0.25).tocsr() for _ in range(2)]
    test_outputs = [sparse.random(6, i, density=0.25).tocsr() for i in range(3, 5)]
    in1 = Input(shape=(3,))
    in2 = Input(shape=(3,), sparse=True)
    out1 = Dropout(0.5, name='dropout')(in1)
    out2 = Dense(4, name='dense_1')(in2)
    model = Model([in1, in2], [out1, out2])
    model.predict(test_inputs, batch_size=2)
    model.compile('rmsprop', 'mse')
    model.fit(test_inputs, test_outputs, epochs=1, batch_size=2, validation_split=0.5)
    model.evaluate(test_inputs, test_outputs, batch_size=2)

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate [as 别名]
def test_model_with_partial_loss():
    a = Input(shape=(3,), name='input_a')
    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    a_3 = dp(a_2)
    model = Model(a, [a_2, a_3])

    optimizer = 'rmsprop'
    loss = {'dropout': 'mse'}
    model.compile(optimizer, loss, metrics=['mae'])

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

    # test train_on_batch
    out = model.train_on_batch(input_a_np, output_a_np)
    out = model.test_on_batch(input_a_np, output_a_np)
    # fit
    out = model.fit(input_a_np, [output_a_np])
    # evaluate
    out = model.evaluate(input_a_np, [output_a_np])

    # Same without dropout.
    a = Input(shape=(3,), name='input_a')
    a_2 = Dense(4, name='dense_1')(a)
    a_3 = Dense(4, name='dense_2')(a_2)
    model = Model(a, [a_2, a_3])

    optimizer = 'rmsprop'
    loss = {'dense_2': 'mse'}
    model.compile(optimizer, loss, metrics={'dense_1': 'mae'})

    # test train_on_batch
    out = model.train_on_batch(input_a_np, output_a_np)
    out = model.test_on_batch(input_a_np, output_a_np)
    # fit
    out = model.fit(input_a_np, [output_a_np])
    # evaluate
    out = model.evaluate(input_a_np, [output_a_np])

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate [as 别名]
def test_model_methods():
    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)

    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))

    # test train_on_batch
    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                               [output_a_np, output_b_np])
    out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                               {'dense_1': output_a_np, 'dropout': output_b_np})

    # test fit
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np], nb_epoch=1, batch_size=4)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np], nb_epoch=1, batch_size=4)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    nb_epoch=1, batch_size=4)

    # test validation_split
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np],
                    nb_epoch=1, batch_size=4, validation_split=0.5)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np],
                    nb_epoch=1, batch_size=4, validation_split=0.5)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    nb_epoch=1, batch_size=4, validation_split=0.5)

    # test validation data
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np],
                    nb_epoch=1, batch_size=4,
                    validation_data=([input_a_np, input_b_np], [output_a_np, output_b_np]))
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np],
                    nb_epoch=1, batch_size=4, validation_split=0.5,
                    validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, [output_a_np, output_b_np]))
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    nb_epoch=1, batch_size=4, validation_split=0.5,
                    validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, {'dense_1': output_a_np, 'dropout': output_b_np}))

    # test_on_batch
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                              [output_a_np, output_b_np])
    out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                              {'dense_1': output_a_np, 'dropout': output_b_np})

    # predict_on_batch
    out = model.predict_on_batch([input_a_np, input_b_np])
    out = model.predict_on_batch({'input_a': input_a_np, 'input_b': input_b_np})

    # predict, evaluate
    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.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)

    # with sample_weight
    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],
#.........这里部分代码省略.........

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate [as 别名]
def test_model_methods():
    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]

    input_a_np = np.random.random((10, 3))
    input_b_np = np.random.random((10, 3))
    input_a_df = pd.DataFrame(input_a_np)
    input_b_df = pd.DataFrame(input_b_np)

    output_a_np = np.random.random((10, 4))
    output_b_np = np.random.random((10, 3))
    output_a_df = pd.DataFrame(output_a_np)
    output_b_df = pd.DataFrame(output_b_np)

    # training/testing doesn't work before compiling.
    with pytest.raises(RuntimeError):
        model.train_on_batch([input_a_np, input_b_np], [output_a_np, output_b_np])

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

    # test train_on_batch
    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                               [output_a_np, output_b_np])
    out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                               {'dense_1': output_a_np, 'dropout': output_b_np})
    out = model.train_on_batch([input_a_df, input_b_df],
                               [output_a_df, output_b_df])

    # test fit
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np], epochs=1, batch_size=4)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np], epochs=1, batch_size=4)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    epochs=1, batch_size=4)
    out = model.fit([input_a_df, input_b_df],
                    [output_a_df, output_b_df], epochs=1, batch_size=4)

    # test validation_split
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np],
                    epochs=1, batch_size=4, validation_split=0.5)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np],
                    epochs=1, batch_size=4, validation_split=0.5)

    # test validation data
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np],
                    epochs=1, batch_size=4,
                    validation_data=([input_a_np, input_b_np], [output_a_np, output_b_np]))
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np],
                    epochs=1, batch_size=4, validation_split=0.5,
                    validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, [output_a_np, output_b_np]))
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    epochs=1, batch_size=4, validation_split=0.5,
                    validation_data=(
                        {'input_a': input_a_np, 'input_b': input_b_np},
                        {'dense_1': output_a_np, 'dropout': output_b_np}))

    # test_on_batch
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                              [output_a_np, output_b_np])
    out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                              {'dense_1': output_a_np, 'dropout': output_b_np})
    out = model.test_on_batch([input_a_df, input_b_df],
                              [output_a_df, output_b_df])

    # predict_on_batch
    out = model.predict_on_batch([input_a_np, input_b_np])
    out = model.predict_on_batch({'input_a': input_a_np, 'input_b': input_b_np})
    out = model.predict_on_batch([input_a_df, input_b_df])

    # predict, evaluate
    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.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
    out = model.evaluate([input_a_df, input_b_df], [output_a_df, output_b_df], batch_size=4)
#.........这里部分代码省略.........

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

        # 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
        with pytest.raises(ValueError):
            out = model.predict(None, batch_size=10)
        out = model.predict(None, steps=3)
        assert out.shape == (10 * 3, 4)

        # Test multi-output model without external data.
        a = Input(tensor=tf.Variable(input_a_np, dtype=tf.float32))
        a_1 = Dense(4, name='dense_1')(a)
        a_2 = Dropout(0.5, name='dropout')(a_1)
        model = Model(a, [a_1, a_2])
        model.add_loss(K.mean(a_2))
        model.compile(optimizer='rmsprop',
                      loss=None,
                      metrics=['mean_squared_error'])
#.........这里部分代码省略.........

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate [as 别名]
def test_model_with_input_feed_tensor():
    """We test building a model with a TF variable as input.
    We should be able to call fit, evaluate, predict,
    by only passing them data for the placeholder inputs
    in the model.
    """
    import tensorflow as tf

    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))

    a = Input(tensor=tf.Variable(input_a_np, dtype=tf.float32))
    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])
    model.summary()

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

    # test train_on_batch
    out = model.train_on_batch(input_b_np,
                               [output_a_np, output_b_np])
    out = model.train_on_batch({'input_b': input_b_np},
                               [output_a_np, output_b_np])
    out = model.test_on_batch({'input_b': input_b_np},
                              [output_a_np, output_b_np])
    out = model.predict_on_batch({'input_b': input_b_np})

    # test fit
    out = model.fit({'input_b': input_b_np},
                    [output_a_np, output_b_np], epochs=1, batch_size=10)
    out = model.fit(input_b_np,
                    [output_a_np, output_b_np], epochs=1, batch_size=10)

    # test evaluate
    out = model.evaluate({'input_b': input_b_np},
                         [output_a_np, output_b_np], batch_size=10)
    out = model.evaluate(input_b_np,
                         [output_a_np, output_b_np], batch_size=10)

    # test predict
    out = model.predict({'input_b': input_b_np}, batch_size=10)
    out = model.predict(input_b_np, batch_size=10)
    assert len(out) == 2

    # Now test a model with a single input
    # i.e. we don't pass any data to fit the model.
    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.summary()

    optimizer = 'rmsprop'
    loss = 'mse'
    model.compile(optimizer, loss, metrics=['mean_squared_error'])

    # test train_on_batch
    out = model.train_on_batch(None,
                               output_a_np)
    out = model.train_on_batch(None,
                               output_a_np)
    out = model.test_on_batch(None,
                              output_a_np)
    out = model.predict_on_batch(None)
    out = model.train_on_batch([],
                               output_a_np)
    out = model.train_on_batch({},
                               output_a_np)

    # test fit
    out = model.fit(None,
                    output_a_np, epochs=1, batch_size=10)
    out = model.fit(None,
                    output_a_np, epochs=1, batch_size=10)

    # test evaluate
    out = model.evaluate(None,
                         output_a_np, batch_size=10)
    out = model.evaluate(None,
                         output_a_np, batch_size=10)

    # test predict
    out = model.predict(None, steps=3)
    out = model.predict(None, steps=3)
    assert out.shape == (10 * 3, 4)

    # Same, without learning phase
#.........这里部分代码省略.........

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate [as 别名]
def test_pandas_dataframe():
    input_a = Input(shape=(3,), name='input_a')
    input_b = Input(shape=(3,), name='input_b')

    x = Dense(4, name='dense_1')(input_a)
    y = Dense(3, name='desne_2')(input_b)

    model_1 = Model(inputs=input_a, outputs=x)
    model_2 = Model(inputs=[input_a, input_b], outputs=[x, y])

    optimizer = 'rmsprop'
    loss = 'mse'

    model_1.compile(optimizer=optimizer, loss=loss)
    model_2.compile(optimizer=optimizer, loss=loss)

    input_a_df = pd.DataFrame(np.random.random((10, 3)))
    input_b_df = pd.DataFrame(np.random.random((10, 3)))

    output_a_df = pd.DataFrame(np.random.random((10, 4)))
    output_b_df = pd.DataFrame(np.random.random((10, 3)))

    model_1.fit(input_a_df,
                output_a_df)
    model_2.fit([input_a_df, input_b_df],
                [output_a_df, output_b_df])
    model_1.fit([input_a_df],
                [output_a_df])
    model_1.fit({'input_a': input_a_df},
                output_a_df)
    model_2.fit({'input_a': input_a_df, 'input_b': input_b_df},
                [output_a_df, output_b_df])

    model_1.predict(input_a_df)
    model_2.predict([input_a_df, input_b_df])
    model_1.predict([input_a_df])
    model_1.predict({'input_a': input_a_df})
    model_2.predict({'input_a': input_a_df, 'input_b': input_b_df})

    model_1.predict_on_batch(input_a_df)
    model_2.predict_on_batch([input_a_df, input_b_df])
    model_1.predict_on_batch([input_a_df])
    model_1.predict_on_batch({'input_a': input_a_df})
    model_2.predict_on_batch({'input_a': input_a_df, 'input_b': input_b_df})

    model_1.evaluate(input_a_df,
                     output_a_df)
    model_2.evaluate([input_a_df, input_b_df],
                     [output_a_df, output_b_df])
    model_1.evaluate([input_a_df],
                     [output_a_df])
    model_1.evaluate({'input_a': input_a_df},
                     output_a_df)
    model_2.evaluate({'input_a': input_a_df, 'input_b': input_b_df},
                     [output_a_df, output_b_df])

    model_1.train_on_batch(input_a_df,
                           output_a_df)
    model_2.train_on_batch([input_a_df, input_b_df],
                           [output_a_df, output_b_df])
    model_1.train_on_batch([input_a_df],
                           [output_a_df])
    model_1.train_on_batch({'input_a': input_a_df},
                           output_a_df)
    model_2.train_on_batch({'input_a': input_a_df, 'input_b': input_b_df},
                           [output_a_df, output_b_df])

    model_1.test_on_batch(input_a_df,
                          output_a_df)
    model_2.test_on_batch([input_a_df, input_b_df],
                          [output_a_df, output_b_df])
    model_1.test_on_batch([input_a_df],
                          [output_a_df])
    model_1.test_on_batch({'input_a': input_a_df},
                          output_a_df)
    model_2.test_on_batch({'input_a': input_a_df, 'input_b': input_b_df},
                          [output_a_df, output_b_df])

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate [as 别名]
def test_model_methods():
    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)

    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))

    # test train_on_batch
    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                               [output_a_np, output_b_np])
    out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                               {'dense_1': output_a_np, 'dropout': output_b_np})

    # test fit
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np], nb_epoch=1, batch_size=4)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np], nb_epoch=1, batch_size=4)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    nb_epoch=1, batch_size=4)

    # test validation_split
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np],
                    nb_epoch=1, batch_size=4, validation_split=0.5)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np],
                    nb_epoch=1, batch_size=4, validation_split=0.5)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    nb_epoch=1, batch_size=4, validation_split=0.5)

    # test validation data
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np],
                    nb_epoch=1, batch_size=4,
                    validation_data=([input_a_np, input_b_np], [output_a_np, output_b_np]))
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np],
                    nb_epoch=1, batch_size=4, validation_split=0.5,
                    validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, [output_a_np, output_b_np]))
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    nb_epoch=1, batch_size=4, validation_split=0.5,
                    validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, {'dense_1': output_a_np, 'dropout': output_b_np}))

    # test_on_batch
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                              [output_a_np, output_b_np])
    out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                              {'dense_1': output_a_np, 'dropout': output_b_np})

    # predict_on_batch
    out = model.predict_on_batch([input_a_np, input_b_np])
    out = model.predict_on_batch({'input_a': input_a_np, 'input_b': input_b_np})

    # predict, evaluate
    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.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)

    # with sample_weight
    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],
#.........这里部分代码省略.........

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate [as 别名]
def test_model_methods():
    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]

    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))

    # training/testing doesn't work before compiling.
    with pytest.raises(RuntimeError):
        model.train_on_batch([input_a_np, input_b_np], [output_a_np, output_b_np])

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

    # test train_on_batch
    out = model.train_on_batch([input_a_np, input_b_np],
                               [output_a_np, output_b_np])
    out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                               [output_a_np, output_b_np])
    out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                               {'dense_1': output_a_np, 'dropout': output_b_np})

    # test fit
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np], epochs=1, batch_size=4)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np], epochs=1, batch_size=4)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    epochs=1, batch_size=4)

    # test validation_split
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np],
                    epochs=1, batch_size=4, validation_split=0.5)
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np],
                    epochs=1, batch_size=4, validation_split=0.5)

    # test validation data
    out = model.fit([input_a_np, input_b_np],
                    [output_a_np, output_b_np],
                    epochs=1, batch_size=4,
                    validation_data=([input_a_np, input_b_np], [output_a_np, output_b_np]))
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    [output_a_np, output_b_np],
                    epochs=1, batch_size=4, validation_split=0.5,
                    validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, [output_a_np, output_b_np]))
    out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
                    {'dense_1': output_a_np, 'dropout': output_b_np},
                    epochs=1, batch_size=4, validation_split=0.5,
                    validation_data=(
                        {'input_a': input_a_np, 'input_b': input_b_np},
                        {'dense_1': output_a_np, 'dropout': output_b_np}))

    # test_on_batch
    out = model.test_on_batch([input_a_np, input_b_np],
                              [output_a_np, output_b_np])
    out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                              [output_a_np, output_b_np])
    out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
                              {'dense_1': output_a_np, 'dropout': output_b_np})

    # predict_on_batch
    out = model.predict_on_batch([input_a_np, input_b_np])
    out = model.predict_on_batch({'input_a': input_a_np, 'input_b': input_b_np})

    # predict, evaluate
    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.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)

    # with sample_weight
    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],
#.........这里部分代码省略.........

# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate [as 别名]
class PolicyValueNet():
    """policy-value network """
    def __init__(self, board_width, board_height, model_file=None):
        self.board_width = board_width
        self.board_height = board_height 
        self.l2_const = 1e-4  # coef of l2 penalty 
        self.create_policy_value_net()   
        self._loss_train_op()

        if model_file:
            net_params = pickle.load(open(model_file, 'rb'))
            self.model.set_weights(net_params)
        
    def create_policy_value_net(self):
        """create the policy value network """   
        in_x = network = Input((4, self.board_width, self.board_height))

        # conv layers
        network = Conv2D(filters=32, kernel_size=(3, 3), padding="same", data_format="channels_first", activation="relu", kernel_regularizer=l2(self.l2_const))(network)
        network = Conv2D(filters=64, kernel_size=(3, 3), padding="same", data_format="channels_first", activation="relu", kernel_regularizer=l2(self.l2_const))(network)
        network = Conv2D(filters=128, kernel_size=(3, 3), padding="same", data_format="channels_first", activation="relu", kernel_regularizer=l2(self.l2_const))(network)
        # action policy layers
        policy_net = Conv2D(filters=4, kernel_size=(1, 1), data_format="channels_first", activation="relu", kernel_regularizer=l2(self.l2_const))(network)
        policy_net = Flatten()(policy_net)
        self.policy_net = Dense(self.board_width*self.board_height, activation="softmax", kernel_regularizer=l2(self.l2_const))(policy_net)
        # state value layers
        value_net = Conv2D(filters=2, kernel_size=(1, 1), data_format="channels_first", activation="relu", kernel_regularizer=l2(self.l2_const))(network)
        value_net = Flatten()(value_net)
        value_net = Dense(64, kernel_regularizer=l2(self.l2_const))(value_net)
        self.value_net = Dense(1, activation="tanh", kernel_regularizer=l2(self.l2_const))(value_net)

        self.model = Model(in_x, [self.policy_net, self.value_net])
        
        def policy_value(state_input):
            state_input_union = np.array(state_input)
            results = self.model.predict_on_batch(state_input_union)
            return results
        self.policy_value = policy_value
        
    def policy_value_fn(self, board):
        """
        input: board
        output: a list of (action, probability) tuples for each available action and the score of the board state
        """
        legal_positions = board.availables
        current_state = board.current_state()
        act_probs, value = self.policy_value(current_state.reshape(-1, 4, self.board_width, self.board_height))
        act_probs = zip(legal_positions, act_probs.flatten()[legal_positions])
        return act_probs, value[0][0]

    def _loss_train_op(self):
        """
        Three loss terms：
        loss = (z - v)^2 + pi^T * log(p) + c||theta||^2
        """

        # get the train op   
        opt = Adam()
        losses = ['categorical_crossentropy', 'mean_squared_error']
        self.model.compile(optimizer=opt, loss=losses)

        def self_entropy(probs):
            return -np.mean(np.sum(probs * np.log(probs + 1e-10), axis=1))

        def train_step(state_input, mcts_probs, winner, learning_rate):
            state_input_union = np.array(state_input)
            mcts_probs_union = np.array(mcts_probs)
            winner_union = np.array(winner)
            loss = self.model.evaluate(state_input_union, [mcts_probs_union, winner_union], batch_size=len(state_input), verbose=0)
            action_probs, _ = self.model.predict_on_batch(state_input_union)
            entropy = self_entropy(action_probs)
            K.set_value(self.model.optimizer.lr, learning_rate)
            self.model.fit(state_input_union, [mcts_probs_union, winner_union], batch_size=len(state_input), verbose=0)
            return loss[0], entropy
        
        self.train_step = train_step

    def get_policy_param(self):
        net_params = self.model.get_weights()        
        return net_params

    def save_model(self, model_file):
        """ save model params to file """
        net_params = self.get_policy_param()
        pickle.dump(net_params, open(model_file, 'wb'), protocol=2)