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@rodrigobaron
Created May 7, 2018 14:35
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import rpyc
from rpyc.utils.zerodeploy import DeployedServer
from plumbum import SshMachine
mach = SshMachine("rodrigo@server")
server = DeployedServer(mach, python_executable='/home/rodrigo/venv/bin/python')
conn = server.classic_connect()
import sys
conn.modules.sys.stdout = sys.stdout
keras = conn.modules.keras
mnist = conn.modules['keras.datasets'].mnist
Sequential = conn.modules['keras.models'].Sequential
Dense = conn.modules['keras.layers'].Dense
Dropout = conn.modules['keras.layers'].Dropout
RMSprop = conn.modules['keras.optimizers'].RMSprop
batch_size = 128
num_classes = 10
epochs = 20
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
(x_train, y_train), (x_test, y_test) = (x_train[:100], y_train[:100]), (x_test[:100], y_test[:100])
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
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