# Create Spark context conf = SparkConf().setAppName('Mnist_Spark_MLP').setMaster('local[8]') sc = SparkContext(conf=conf) # Build RDD from numpy features and labels df = to_data_frame(sc, x_train, y_train, categorical=True) test_df = to_data_frame(sc, x_test, y_test, categorical=True) # Define elephas optimizer adadelta = elephas_optimizers.Adadelta() # Initialize Spark ML Estimator estimator = ElephasEstimator() estimator.set_keras_model_config(model.to_yaml()) estimator.set_optimizer_config(adadelta.get_config()) estimator.set_nb_epoch(nb_epoch) estimator.set_batch_size(batch_size) estimator.set_num_workers(1) estimator.set_verbosity(0) 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)
model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam') # Initialize Elephas Spark ML Estimator adagrad = elephas_optimizers.Adagrad() estimator = ElephasEstimator() estimator.setFeaturesCol("scaled_features") estimator.setLabelCol("index_category") estimator.set_keras_model_config(model.to_yaml()) estimator.set_optimizer_config(adagrad.get_config()) estimator.set_nb_epoch(10) estimator.set_batch_size(128) estimator.set_num_workers(4) estimator.set_verbosity(0) estimator.set_validation_split(0.15) estimator.set_categorical_labels(True) estimator.set_nb_classes(nb_classes) # Fitting a model returns a Transformer pipeline = Pipeline(stages=[string_indexer, scaler, estimator]) fitted_pipeline = pipeline.fit(train_df) from pyspark.mllib.evaluation import MulticlassMetrics # Evaluate Spark model prediction = fitted_pipeline.transform(train_df)
from elephas.ml_model import ElephasEstimator from elephas import optimizers as elephas_optimizers # Define elephas optimizer (which tells the model how to aggregate updates on the Spark master) adadelta = elephas_optimizers.Adadelta() # Initialize SparkML Estimator and set all relevant properties estimator = ElephasEstimator() estimator.setFeaturesCol("scaled_features") # These two come directly from pyspark, estimator.setLabelCol("index_category") # hence the camel case. Sorry :) estimator.set_keras_model_config(model.to_yaml()) # Provide serialized Keras model estimator.set_optimizer_config(adadelta.get_config()) # Provide serialized Elephas optimizer estimator.set_categorical_labels(True) estimator.set_nb_classes(nb_classes) estimator.set_num_workers(1) # We just use one worker here. Feel free to adapt it. estimator.set_nb_epoch(20) estimator.set_batch_size(128) estimator.set_verbosity(1) estimator.set_validation_split(0.15) from pyspark.ml import Pipeline pipeline = Pipeline(stages=[string_indexer, scaler, estimator]) fitted_pipeline = pipeline.fit(train_df) # Fit model to data prediction = fitted_pipeline.transform(train_df) # Evaluate on train data.
model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam') # Initialize Elephas Spark ML Estimator adagrad = elephas_optimizers.Adagrad() estimator = ElephasEstimator() estimator.setFeaturesCol("scaled_features") estimator.setLabelCol("index_category") estimator.set_keras_model_config(model.to_yaml()) estimator.set_optimizer_config(adagrad.get_config()) estimator.set_nb_epoch(10) estimator.set_batch_size(128) estimator.set_num_workers(4) estimator.set_verbosity(0) estimator.set_validation_split(0.15) estimator.set_categorical_labels(True) estimator.set_nb_classes(nb_classes) # Fitting a model returns a Transformer pipeline = Pipeline(stages=[string_indexer, scaler, estimator]) fitted_pipeline = pipeline.fit(train_df) from pyspark.mllib.evaluation import MulticlassMetrics # Evaluate Spark model prediction = fitted_pipeline.transform(train_df)