import numpy as np
if __name__ == '__main__':
if platform.system() == 'Windows':
folder = 'C:/data/train_data'
elif platform.system() == 'Linux':
folder = '/home/shaoheng/Documents/PythonPractice/handwritedigit'
batch = 5
class_num = 10
data_generator = DataGenerator(folder,
batch, (16, 16),
class_num=class_num)
model = MlpBatch(input_nodes=16 * 16,
hidden_nodes=(12, class_num),
batch_size=batch)
right = 0
for i in range(1000000):
if (i + 1) % 100 == 0:
print('acc=%.2f' % (right / (i * batch) * 100), '%')
x, y = data_generator.load_data()
out = model.forward_prop(x)
model.back_prop(x, y)
for b in range(batch):
if np.argmax(y[b]) == np.argmax(out[b]):
right += 1
folder = 'C:/data/train_data'
test_folder = 'C:/data/test_data'
elif platform.system() == 'Linux':
folder = '/home/shaoheng/Documents/PythonPractice/handwritedigit'
batch = 32
class_num = 10
data_generator = DataGenerator(folder,
batch, (16, 16),
class_num=class_num)
valid_data_gen = DataGenerator(test_folder,
160, (16, 16),
class_num=class_num)
valid_x, valid_y = valid_data_gen.load_data()
train_data_gen = DataGenerator(folder, 320, (16, 16), class_num=class_num)
train_full_x, train_full_y = train_data_gen.load_data()
def forward(x):
for deep, now_layer in enumerate(model):
if type(now_layer) == Conv2D and deep == 0:
x = np.expand_dims(x, -1)
elif type(now_layer) == FC:
x = np.reshape(
x, (np.shape(x)[0], -1)) # flatten for feeding into FC
x = now_layer.forward_prop(x)
return x
def backward(y):
