try:
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
from mnist import load_mnist
import multilayernet as network
except ImportError:
print('Library Module Can Not Fount')
# 1. load training / test data
(train_x, train_t), (test_x, test_t) = load_mnist(normalize=True,
flatten=True,
one_hot_label=True)
# 2. initialize network
network.initialize(input_size=train_x.shape[1],
hidden_size=[50],
output_size=train_t.shape[1])
# 3. batch by 3
train_x_batch = train_x[:3]
train_t_batch = train_t[:3]
# 4. gradient
gradient_numerical = network.numerical_gradient_net(train_x_batch,
train_t_batch)
gradient_backpropagation = network.backpropagation_gradient_net(
train_x_batch, train_t_batch)
print(gradient_backpropagation)
# 5.mean of modules
import sys
from pathlib import Path
try:
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
from mnist import load_mnist
import multilayernet as network
except ImportError:
print('Library Module Can Not Found')
# 1. load train/test data
(train_x, train_t), (test_x, test_t) = load_mnist(normalize=True,
flatten=True,
one_hot_label=True)
# 2. load params dataset trained
params_file = os.path.join(os.getcwd(), 'model', 'twolayer_params.pkl')
params = None
with open(params_file, 'rb') as f:
params = pickle.load(f)
# 3. model frame
network.initialize(input_size=train_x.shape[1],
hidden_size=[50, 100],
output_size=train_t.shape[1],
init_params=params)
train_accuracy = network.accuracy(train_x, train_t)
test_accuracy = network.accuracy(test_x, test_t)
print(train_accuracy, test_accuracy)