def main():
x_train, y_train, x_test, y_test = data.mnist(one_hot=True)
# Define Deep Neural Network structure (input_dim, num_of_nodes)
layers = [[x_train.shape[1], 256], [256, 128], [128, 64]]
# Initialize a deep neural network
dnn = DNN(MODEL_FOLDER, os_slash, layers, params)
pre_epochs = 100
train_epochs = 100
# Create auto-encoders and train them one by one by stacking them in the DNN
pre_trained_weights = dnn.pre_train(x_train, pre_epochs)
# Then use the pre-trained weights of these layers as initial weight values for the MLP
history = dnn.train(x_train,
y_train,
train_epochs,
init_weights=pre_trained_weights)
plot.plot_loss(history, loss_type='MSE')
predicted, score = dnn.test(x_test, y_test)
print("Test accuracy: ", score[1])
dnn.model.save_weights(MODEL_FOLDER + os_slash + "final_weights.h5")
dnn.model.save(MODEL_FOLDER + os_slash + "model.h5")
save_results(score[1])
def main2():
dnn = DNN(input=28 * 28,
layers=[DropoutLayer(160, LQ),
Layer(10, LCE)],
eta=0.05,
lmbda=1) # 98%
dnn.initialize_rand()
train, test, vadilation = load_mnist_simple()
f_names = [f'mnist_expaned_k0{i}.pkl.gz' for i in range(50)]
shuffle(f_names)
for f_name in f_names:
print(f_name)
with timing("load"):
raw_data = load_data(f_name)
with timing("shuffle"):
shuffle(raw_data)
with timing("reshape"):
data = [(x.reshape((784, 1)), y)
for x, y in islice(raw_data, 100000)]
del raw_data
with timing("learn"):
dnn.learn(data)
del data
print('TEST:', dnn.test(test))
def main():
train, test, vadilation = load_mnist_simple()
# x, y = train[0]
# print("x: ", x.shape)
# print("y: ", y)
with timing(f""):
# dnn = DNN(input=28 * 28, layers=[Layer(30, LQ), Layer(10, LCE)], eta=0.05) # 96%
# dnn = DNN(input=28 * 28, layers=[Layer(30, LQ), Layer(10, SM)], eta=0.001) # 68%
# dnn = DNN(input=28 * 28, layers=[Layer(100, LQ), Layer(10, LCE)], eta=0.05, lmbda=5) # 98%
# dnn = DNN(input=28 * 28, layers=[DropoutLayer(100, LQ), Layer(10, LCE)], eta=0.05) # 97.5%
dnn = DNN(input=28 * 28, layers=[DropoutLayer(160, LQ), Layer(10, LCE)], eta=0.05, lmbda=3)
dnn.initialize_rand()
dnn.learn(train, epochs=30, test=vadilation, batch_size=29)
print('test:', dnn.test(test))
print(dnn.stats())
