np.random.shuffle(b)
shuffle(x_train, y_train)
shuffle(x_test, y_test)
from models import normal_model
model = normal_model.model()
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=verbose)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print('Stochastic model')
from models import stochastic_model
model = stochastic_model.model()
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=verbose)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print('Normal model')
from models import normal_model
model = normal_model.model()
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=verbose)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print('Stochastic model')
from models import stochastic_model
model_stochastic = stochastic_model.model()
history = model_stochastic.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
validation_data=(x_test, y_test))
score = model_stochastic.evaluate(x_test, y_test, verbose=verbose)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
from data import noisy_mnist
(_, _), (x_test, y_test) = noisy_mnist.data()
score = model.evaluate(x_test, y_test, verbose=verbose)
print('Normal Noisy Test loss:', score[0])
print('Normal Noisy Test accuracy:', score[1])
verbose = 2
batch_size = 128
epochs = 20
from data import normal_mnist
(x_train, y_train), (x_test, y_test) = normal_mnist.data()
try:
import sys
bitsize = 2**int(sys.argv[1])
except:
bitsize = 32
print('Stochastic model')
from models import stochastic_model
model = stochastic_model.model(bit_size=bitsize)
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=verbose)
print('Test loss:', score[0])
print('Test accuracy:', score[1])