# Create Spark context
conf = SparkConf().setAppName('Mnist_Spark_MLP').setMaster('local[8]')
sc = SparkContext(conf=conf)
# Build RDD from numpy features and labels
lp_rdd = to_labeled_point(sc, x_train, y_train, categorical=True)
rdd = lp_to_simple_rdd(lp_rdd, True, nb_classes)
# Initialize SparkModel from Keras model and Spark context
adadelta = elephas_optimizers.Adadelta()
spark_model = SparkMLlibModel(sc,
model,
optimizer=adadelta,
frequency='batch',
mode='asynchronous',
num_workers=2,
master_optimizer=rms)
# Train Spark model
spark_model.train(lp_rdd,
nb_epoch=20,
batch_size=32,
verbose=0,
validation_split=0.1,
categorical=True,
nb_classes=nb_classes)
# Evaluate Spark model by evaluating the underlying model
score = spark_model.master_network.evaluate(x_test, y_test, verbose=2)
print('Test accuracy:', score[1])
model.add(Dropout(0.2))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=["accuracy"])
# Create Spark context
conf = SparkConf().setAppName('Mnist_Spark_MLP').setMaster('local[8]')
#sc = SparkContext(conf=conf)
# Build RDD from numpy features and labels
lp_rdd = to_labeled_point(sc, x_train, y_train, categorical=True)
rdd = lp_to_simple_rdd(lp_rdd, True, nb_classes)
# Initialize SparkModel from Keras model and Spark context
adagrad = elephas_optimizers.Adagrad()
spark_model = SparkMLlibModel(sc, model, optimizer=adagrad, frequency='batch', mode='asynchronous', num_workers=4)
# Train Spark model
spark_model.train(lp_rdd, nb_epoch=nb_epoch, batch_size=batch_size, verbose=0,
validation_split=0.1, categorical=True, nb_classes=nb_classes)
# Evaluate Spark model by evaluating the underlying model
loss, acc = spark_model.master_network.evaluate(x_test, y_test, verbose=2)
print('Test accuracy:', acc)
model = Sequential()
model.add(Dense(784, 128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128, 128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128, 10))
model.add(Activation('softmax'))
# Compile model
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms)
# Create Spark context
conf = SparkConf().setAppName('Mnist_Spark_MLP').setMaster('local[8]')
sc = SparkContext(conf=conf)
# Build RDD from numpy features and labels
lp_rdd = to_labeled_point(sc, X_train, Y_train, categorical=True)
# Initialize SparkModel from Keras model and Spark context
spark_model = SparkMLlibModel(sc,model)
# Train Spark model
spark_model.train(lp_rdd, nb_epoch=20, batch_size=32, verbose=0, validation_split=0.1, num_workers=8, categorical=True, nb_classes=nb_classes)
# Evaluate Spark model by evaluating the underlying model
score = spark_model.get_network().evaluate(X_test, Y_test, show_accuracy=True, verbose=2)
print('Test accuracy:', score[1])
