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

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

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

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