verbose = 2
batch_size = 128
num_classes = 10
epochs = 20
from data import normal_mnist
(x_train, y_train), (x_test, y_test) = normal_mnist.data()
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)
verbose = 2
batch_size = 128
num_classes = 10
epochs = 20
from data import normal_mnist, noisy_mnist
(x_train_m, y_train_m), (x_test_m, y_test_m) = normal_mnist.data()
(x_train_n, y_train_n), (x_test_n, y_test_n) = noisy_mnist.data()
import numpy as np
x_train = np.concatenate((x_train_m, x_train_n))
y_train = np.concatenate((y_train_m, y_train_n))
x_test = np.concatenate((x_test_m, x_test_n))
y_test = np.concatenate((y_test_m, y_test_n))
def shuffle(a, b):
rng_state = np.random.get_state()
np.random.shuffle(a)
np.random.set_state(rng_state)
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,