Python keras.optimizers.serialize() Examples

The following are 13 code examples of keras.optimizers.serialize(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.optimizers , or try the search function .
Example #1
Source File: workers.py    From dist-keras with GNU General Public License v3.0 6 votes vote down vote up 
def __init__(self, model, optimizer, loss, loss_weights, metrics=["accuracy"], features_col="features", label_col="label",
                 batch_size=32, num_epoch=1, learning_rate=1.0):
        assert isinstance(optimizer, (str, Optimizer)), "'optimizer' must be a string or a Keras Optimizer instance"
        assert isinstance(features_col, (str, list)), "'features_col' must be a string or a list of strings"
        assert isinstance(label_col, (str, list)), "'label_col' must be a string or a list of strings"
        self.model = model
        self.optimizer = {'class_name': optimizer, 'config': {}} if isinstance(optimizer, str) else serialize(optimizer)
        self.loss = loss
        self.loss_weights = loss_weights
        self.metrics= metrics
        self.features_column = [features_col] if isinstance(features_col, str) else features_col
        self.label_column = [label_col] if isinstance(label_col, str) else label_col
        self.batch_size = batch_size
        self.num_epoch = num_epoch
        self.max_mini_batches = 100
        self.prefetching_thread = None
        self.mini_batches = None
        self.is_prefetching = True
        self.worker_id = -1
        self.learning_rate = learning_rate
        self.num_inputs = len(self.features_column)
        self.num_outputs = len(self.label_column)
        self.current_epoch = 0
Example #2
Source File: test_ml_model.py    From elephas with MIT License 5 votes vote down vote up 
def test_spark_ml_model(spark_context):

    df = to_data_frame(spark_context, x_train, y_train, categorical=True)
    test_df = to_data_frame(spark_context, x_test, y_test, categorical=True)

    sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    sgd_conf = optimizers.serialize(sgd)

    # Initialize Spark ML Estimator
    estimator = ElephasEstimator()
    estimator.set_keras_model_config(model.to_yaml())
    estimator.set_optimizer_config(sgd_conf)
    estimator.set_mode("synchronous")
    estimator.set_loss("categorical_crossentropy")
    estimator.set_metrics(['acc'])
    estimator.set_epochs(epochs)
    estimator.set_batch_size(batch_size)
    estimator.set_validation_split(0.1)
    estimator.set_categorical_labels(True)
    estimator.set_nb_classes(nb_classes)

    # Fitting a model returns a Transformer
    pipeline = Pipeline(stages=[estimator])
    fitted_pipeline = pipeline.fit(df)

    # Evaluate Spark model by evaluating the underlying model
    prediction = fitted_pipeline.transform(test_df)
    pnl = prediction.select("label", "prediction")
    pnl.show(100)

    prediction_and_label = pnl.rdd.map(lambda row: (row.label, row.prediction))
    metrics = MulticlassMetrics(prediction_and_label)
    print(metrics.precision())
    print(metrics.recall())
Example #3
Source File: optimizers.py    From keras-contrib with MIT License 5 votes vote down vote up 
def _test_optimizer(optimizer, target=0.75):
    x_train, y_train = get_test_data()
    model = get_model(x_train.shape[1], 10, y_train.shape[1])
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
    history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
    assert history.history['acc'][-1] >= target
    config = optimizers.serialize(optimizer)
    custom_objects = {optimizer.__class__.__name__: optimizer.__class__}
    optim = optimizers.deserialize(config, custom_objects)
    new_config = optimizers.serialize(optim)
    assert config == new_config
Example #4
Source File: optimizers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def _test_optimizer(optimizer, target=0.75):
    x_train, y_train = get_test_data()

    model = Sequential()
    model.add(Dense(10, input_shape=(x_train.shape[1],)))
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])

    history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
    assert history.history['acc'][-1] >= target
    config = optimizers.serialize(optimizer)
    optim = optimizers.deserialize(config)
    new_config = optimizers.serialize(optim)
    new_config['class_name'] = new_config['class_name'].lower()
    assert config == new_config

    # Test constraints.
    model = Sequential()
    dense = Dense(10,
                  input_shape=(x_train.shape[1],),
                  kernel_constraint=lambda x: 0. * x + 1.,
                  bias_constraint=lambda x: 0. * x + 2.,)
    model.add(dense)
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
    model.train_on_batch(x_train[:10], y_train[:10])
    kernel, bias = dense.get_weights()
    assert_allclose(kernel, 1.)
    assert_allclose(bias, 2.)
Example #5
Source File: optimizers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def _test_optimizer(optimizer, target=0.75):
    x_train, y_train = get_test_data()

    model = Sequential()
    model.add(Dense(10, input_shape=(x_train.shape[1],)))
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])

    history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
    assert history.history['acc'][-1] >= target
    config = optimizers.serialize(optimizer)
    optim = optimizers.deserialize(config)
    new_config = optimizers.serialize(optim)
    new_config['class_name'] = new_config['class_name'].lower()
    assert config == new_config

    # Test constraints.
    model = Sequential()
    dense = Dense(10,
                  input_shape=(x_train.shape[1],),
                  kernel_constraint=lambda x: 0. * x + 1.,
                  bias_constraint=lambda x: 0. * x + 2.,)
    model.add(dense)
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
    model.train_on_batch(x_train[:10], y_train[:10])
    kernel, bias = dense.get_weights()
    assert_allclose(kernel, 1.)
    assert_allclose(bias, 2.)
Example #6
Source File: optimizers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def _test_optimizer(optimizer, target=0.75):
    x_train, y_train = get_test_data()

    model = Sequential()
    model.add(Dense(10, input_shape=(x_train.shape[1],)))
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])

    history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
    assert history.history['acc'][-1] >= target
    config = optimizers.serialize(optimizer)
    optim = optimizers.deserialize(config)
    new_config = optimizers.serialize(optim)
    new_config['class_name'] = new_config['class_name'].lower()
    assert config == new_config

    # Test constraints.
    model = Sequential()
    dense = Dense(10,
                  input_shape=(x_train.shape[1],),
                  kernel_constraint=lambda x: 0. * x + 1.,
                  bias_constraint=lambda x: 0. * x + 2.,)
    model.add(dense)
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
    model.train_on_batch(x_train[:10], y_train[:10])
    kernel, bias = dense.get_weights()
    assert_allclose(kernel, 1.)
    assert_allclose(bias, 2.)
Example #7
Source File: optimizers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def _test_optimizer(optimizer, target=0.75):
    x_train, y_train = get_test_data()

    model = Sequential()
    model.add(Dense(10, input_shape=(x_train.shape[1],)))
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])

    history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
    assert history.history['acc'][-1] >= target
    config = optimizers.serialize(optimizer)
    optim = optimizers.deserialize(config)
    new_config = optimizers.serialize(optim)
    new_config['class_name'] = new_config['class_name'].lower()
    assert config == new_config

    # Test constraints.
    model = Sequential()
    dense = Dense(10,
                  input_shape=(x_train.shape[1],),
                  kernel_constraint=lambda x: 0. * x + 1.,
                  bias_constraint=lambda x: 0. * x + 2.,)
    model.add(dense)
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
    model.train_on_batch(x_train[:10], y_train[:10])
    kernel, bias = dense.get_weights()
    assert_allclose(kernel, 1.)
    assert_allclose(bias, 2.)
Example #8
Source File: optimizers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def _test_optimizer(optimizer, target=0.75):
    x_train, y_train = get_test_data()

    model = Sequential()
    model.add(Dense(10, input_shape=(x_train.shape[1],)))
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])

    history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
    assert history.history['acc'][-1] >= target
    config = optimizers.serialize(optimizer)
    optim = optimizers.deserialize(config)
    new_config = optimizers.serialize(optim)
    new_config['class_name'] = new_config['class_name'].lower()
    assert config == new_config

    # Test constraints.
    model = Sequential()
    dense = Dense(10,
                  input_shape=(x_train.shape[1],),
                  kernel_constraint=lambda x: 0. * x + 1.,
                  bias_constraint=lambda x: 0. * x + 2.,)
    model.add(dense)
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
    model.train_on_batch(x_train[:10], y_train[:10])
    kernel, bias = dense.get_weights()
    assert_allclose(kernel, 1.)
    assert_allclose(bias, 2.)
Example #9
Source File: optimizers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def _test_optimizer(optimizer, target=0.75):
    x_train, y_train = get_test_data()

    model = Sequential()
    model.add(Dense(10, input_shape=(x_train.shape[1],)))
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])

    history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
    assert history.history['acc'][-1] >= target
    config = optimizers.serialize(optimizer)
    optim = optimizers.deserialize(config)
    new_config = optimizers.serialize(optim)
    new_config['class_name'] = new_config['class_name'].lower()
    assert config == new_config

    # Test constraints.
    model = Sequential()
    dense = Dense(10,
                  input_shape=(x_train.shape[1],),
                  kernel_constraint=lambda x: 0. * x + 1.,
                  bias_constraint=lambda x: 0. * x + 2.,)
    model.add(dense)
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
    model.train_on_batch(x_train[:10], y_train[:10])
    kernel, bias = dense.get_weights()
    assert_allclose(kernel, 1.)
    assert_allclose(bias, 2.)
Example #10
Source File: optimizers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def _test_optimizer(optimizer, target=0.75):
    x_train, y_train = get_test_data()

    model = Sequential()
    model.add(Dense(10, input_shape=(x_train.shape[1],)))
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])

    history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
    assert history.history['acc'][-1] >= target
    config = optimizers.serialize(optimizer)
    optim = optimizers.deserialize(config)
    new_config = optimizers.serialize(optim)
    new_config['class_name'] = new_config['class_name'].lower()
    assert config == new_config

    # Test constraints.
    model = Sequential()
    dense = Dense(10,
                  input_shape=(x_train.shape[1],),
                  kernel_constraint=lambda x: 0. * x + 1.,
                  bias_constraint=lambda x: 0. * x + 2.,)
    model.add(dense)
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
    model.train_on_batch(x_train[:10], y_train[:10])
    kernel, bias = dense.get_weights()
    assert_allclose(kernel, 1.)
    assert_allclose(bias, 2.)
Example #11
Source File: optimizers_test.py    From faceswap with GNU General Public License v3.0 5 votes vote down vote up 
def _test_optimizer(optimizer, target=0.75):
    x_train, y_train = get_test_data()

    model = Sequential()
    model.add(Dense(10, input_shape=(x_train.shape[1],)))
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])

    history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
    # TODO PlaidML fails this test
    assert history.history['acc'][-1] >= target
    config = k_optimizers.serialize(optimizer)
    optim = k_optimizers.deserialize(config)
    new_config = k_optimizers.serialize(optim)
    new_config['class_name'] = new_config['class_name'].lower()
    assert config == new_config

    # Test constraints.
    model = Sequential()
    dense = Dense(10,
                  input_shape=(x_train.shape[1],),
                  kernel_constraint=lambda x: 0. * x + 1.,
                  bias_constraint=lambda x: 0. * x + 2.,)
    model.add(dense)
    model.add(Activation('relu'))
    model.add(Dense(y_train.shape[1]))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
    model.train_on_batch(x_train[:10], y_train[:10])
    kernel, bias = dense.get_weights()
    assert_allclose(kernel, 1.)
    assert_allclose(bias, 2.)
Example #12
Source File: l2optimizer.py    From DIIN-in-Keras with MIT License 5 votes vote down vote up 
def get_config(self):
        config = {'optimizer':                  serialize(self.optimizer),
                  'l2_full_step':               float(K.get_value(self.l2_full_step)),
                  'l2_full_ratio':              float(K.get_value(self.l2_full_ratio)),
                  'l2_difference_full_ratio':   float(K.get_value(self.l2_difference_full_ratio))}
        return config
Example #13
Source File: spark_model.py    From elephas with MIT License 4 votes vote down vote up 
def __init__(self, model, mode='asynchronous', frequency='epoch',  parameter_server_mode='http', num_workers=None,
                 custom_objects=None, batch_size=32,  port=4000, *args, **kwargs):
        """SparkModel

        Base class for distributed training on RDDs. Spark model takes a Keras
        model as master network, an optimization scheme, a parallelisation mode
        and an averaging frequency.

        :param model: Compiled Keras model
        :param mode: String, choose from `asynchronous`, `synchronous` and `hogwild`
        :param frequency: String, either `epoch` or `batch`
        :param parameter_server_mode: String, either `http` or `socket`
        :param num_workers: int, number of workers used for training (defaults to None)
        :param custom_objects: Keras custom objects
        :param batch_size: batch size used for training and inference
        :param port: port used in case of 'http' parameter server mode
        """

        self._master_network = model
        if not hasattr(model, "loss"):
            raise Exception(
                "Compile your Keras model before initializing an Elephas model with it")
        metrics = model.metrics
        loss = model.loss
        optimizer = serialize_optimizer(model.optimizer)

        if custom_objects is None:
            custom_objects = {}
        if metrics is None:
            metrics = ["accuracy"]
        self.mode = mode
        self.frequency = frequency
        self.num_workers = num_workers
        self.weights = self._master_network.get_weights()
        self.pickled_weights = None
        self.master_optimizer = optimizer
        self.master_loss = loss
        self.master_metrics = metrics
        self.custom_objects = custom_objects
        self.parameter_server_mode = parameter_server_mode
        self.batch_size = batch_size
        self.port = port
        self.kwargs = kwargs

        self.serialized_model = model_to_dict(model)
        if self.mode is not 'synchronous':
            if self.parameter_server_mode == 'http':
                self.parameter_server = HttpServer(
                    self.serialized_model, self.mode, self.port)
                self.client = HttpClient(self.port)
            elif self.parameter_server_mode == 'socket':
                self.parameter_server = SocketServer(self.serialized_model)
                self.client = SocketClient()
            else:
                raise ValueError("Parameter server mode has to be either `http` or `socket`, "
                                 "got {}".format(self.parameter_server_mode))