def main():
start_time = time()
print("---------- main1 --------------")
f0 = gzip.open('/home/luca/data/mnist/train-images-idx3-ubyte.gz', 'r')
f1 = gzip.open('/home/luca/data/mnist/t10k-images-idx3-ubyte.gz', 'r')
l0 = gzip.open('/home/luca/data/mnist/train-labels-idx1-ubyte.gz', 'r')
l1 = gzip.open('/home/luca/data/mnist/t10k-labels-idx1-ubyte.gz', 'r')
X_train = np.frombuffer(f0.read(), dtype=np.uint8,
offset=16).reshape(-1, 28 * 28)
X_test = np.frombuffer(f1.read(), dtype=np.uint8,
offset=16).reshape(-1, 28 * 28)
y_train = np.frombuffer(l0.read(), dtype=np.uint8, offset=8)
y_test = np.frombuffer(l1.read(), dtype=np.uint8, offset=8)
y_train = one_hot_encoding(y_train)
y_label = one_hot_encoding(y_test)
mean = np.mean(X_train)
std = np.std(X_train)
X_train, X_test = X_train - mean, X_test - mean
X_train, X_test = X_train / std, X_test / std
model = neural_network((89, 'TanH'), (10, 'Sigmoid'),
input_nodes=784,
seed=20190119)
model = fit(x_train=X_train,
y_train=y_train,
x_test=X_test,
y_test=y_label,
model=model,
optimizer=sgd(epochs=50,
eta=0.35,
etaN=0.15,
decay_type='exponential'),
batch_size=60,
eval_every=5,
early_stop=True,
seed=20190119)
validate_accuracy(x_test=X_test, y_test=y_test, model=model)
# print(model[0][0][0].shape)
# print(np.sum(model[0][0][0]))
# print(model[0][0][1].shape)
# print(np.sum(model[0][0][1]))
#
# print(model[1][0][0].shape)
# print(np.sum(model[1][0][0]))
# print(model[1][0][1].shape)
# print(np.sum(model[1][0][1]))
# print()
print("--- %s seconds ---" % (time() - start_time))
def main():
start_time = time()
print("---------- main5 --------------")
f0 = gzip.open('/home/luca/data/mnist/train-images-idx3-ubyte.gz', 'r')
f1 = gzip.open('/home/luca/data/mnist/t10k-images-idx3-ubyte.gz', 'r')
l0 = gzip.open('/home/luca/data/mnist/train-labels-idx1-ubyte.gz', 'r')
l1 = gzip.open('/home/luca/data/mnist/t10k-labels-idx1-ubyte.gz', 'r')
X_train = np.frombuffer(f0.read(), dtype=np.uint8,
offset=16).reshape(-1, 28 * 28)
X_test = np.frombuffer(f1.read(), dtype=np.uint8,
offset=16).reshape(-1, 28 * 28)
y_train = np.frombuffer(l0.read(), dtype=np.uint8, offset=8)
y_test = np.frombuffer(l1.read(), dtype=np.uint8, offset=8)
y_train = one_hot_encoding(y_train)
y_label = one_hot_encoding(y_test)
mean = np.mean(X_train)
std = np.std(X_train)
X_train, X_test = X_train - mean, X_test - mean
X_train, X_test = X_train / std, X_test / std
model = neural_network((89, 'TanH'), (10, 'Softmax'),
input_nodes=784,
seed=20190119,
weight_init='scaled')
model = fit(x_train=X_train,
y_train=y_train,
x_test=X_test,
y_test=y_label,
model=model,
optimizer=sgd(epochs=50,
eta=0.15,
etaN=0.05,
decay_type='exponential',
beta=0.85),
batch_size=60,
eval_every=5,
early_stop=True,
loss_function='cross-entropy',
seed=20190119,
dropout=0.8)
validate_accuracy(x_test=X_test, y_test=y_test, model=model)
print("--- %s seconds ---" % (time() - start_time))