def test_lp_to_simple_rdd_categorical(spark_context):
features = np.ones((2, 10))
labels = np.asarray([[0, 0, 1.0], [0, 1.0, 0]])
lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, True)
rdd = rdd_utils.lp_to_simple_rdd(lp_rdd, categorical=True, nb_classes=3)
assert rdd.first()[0].shape == (10, )
assert rdd.first()[1].shape == (3, )
def test_lp_to_simple_rdd_not_categorical(spark_context):
features = np.ones((2, 10))
labels = np.asarray([[2.0], [1.0]]).reshape((2, ))
lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, False)
rdd = rdd_utils.lp_to_simple_rdd(lp_rdd, categorical=False, nb_classes=3)
assert rdd.first()[0].shape == (10, )
assert rdd.first()[1] == 2.0
def test_from_labeled_rdd(spark_context):
features = np.ones((2, 10))
labels = np.asarray([[2.0], [1.0]]).reshape((2, ))
lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, False)
x, y = rdd_utils.from_labeled_point(lp_rdd, False, None)
assert x.shape == features.shape
assert y.shape == labels.shape
def test_from_labeled_rdd_categorical(spark_context):
features = np.ones((2, 10))
labels = np.asarray([[0, 0, 1.0], [0, 1.0, 0]])
lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, True)
x, y = rdd_utils.from_labeled_point(lp_rdd, True, 3)
assert x.shape == features.shape
assert y.shape == labels.shape
def test_to_labeled_rdd_not_categorical(spark_context):
features = np.ones((2, 10))
labels = np.asarray([[2.0], [1.0]])
lp_rdd = rdd_utils.to_labeled_point(spark_context, features, labels, False)
assert lp_rdd.count() == 2
first = lp_rdd.first()
assert first.features.shape == (10, )
assert first.label == 2.0
def test_mllib_model(spark_context):
# Build RDD from numpy features and labels
lp_rdd = to_labeled_point(spark_context, x_train, y_train, categorical=True)
# Initialize SparkModel from Keras model and Spark context
spark_model = SparkMLlibModel(model=model, frequency='epoch', mode='synchronous')
# Train Spark model
spark_model.fit(lp_rdd, epochs=5, 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])
def test_mllib_model(spark_context, classification_model, mnist_data):
rms = RMSprop()
classification_model.compile(rms, 'categorical_crossentropy', ['acc'])
x_train, y_train, x_test, y_test = mnist_data
x_train = x_train[:1000]
y_train = y_train[:1000]
# Build RDD from numpy features and labels
lp_rdd = to_labeled_point(spark_context, x_train,
y_train, categorical=True)
# Initialize SparkModel from tensorflow.keras model and Spark context
spark_model = SparkMLlibModel(
model=classification_model, frequency='epoch', mode='synchronous')
# Train Spark model
spark_model.fit(lp_rdd, epochs=5, 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)
assert score
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))
# Compile model
rms = RMSprop()
model.compile(rms, "categorical_crossentropy", ['acc'])
# 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 tensorflow.keras model and Spark context
spark_model = SparkMLlibModel(model=model,
frequency='epoch',
mode='synchronous')
# Train Spark model
spark_model.fit(lp_rdd,
epochs=5,
batch_size=32,
verbose=0,
validation_split=0.1,
categorical=True,
nb_classes=nb_classes)
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.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(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)
print(lp_rdd.first())
rdd = lp_to_simple_rdd(lp_rdd, True, nb_classes)
rdd = rdd.repartition(4)
rdd.first()
# 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)
# 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.get_network().evaluate(X_test, Y_test, show_accuracy=True, verbose=2)
print('Test accuracy:', score[1])
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(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)
print(lp_rdd.first())
rdd = lp_to_simple_rdd(lp_rdd, True, nb_classes)
rdd = rdd.repartition(4)
rdd.first()
# 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)
# Train Spark model
