2nd_随机梯度下降法.py

import numpy as np
from mnist import load_mnist
from two_layer_net import TwoLayerNet

(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
train_size = x_train.shape[0]

train_loss_list = []
train_acc_list = []
test_acc_list = []

# 超参数
iters_num = 10000
batch_size = 100
learning_rate = 0.1

# 平均每个epoch的重复次数
iter_per_epoch = max(train_size / batch_size, 1)

network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)

for i in range(iters_num):
	# 获取mini-batch
	batch_mask = np.random.choice(train_size, batch_size)
	x_batch = x_train[batch_mask]
	t_batch = t_train[batch_mask]
	
	# 计算梯度
	# grad = network.numerical_gradient(x_batch, t_batch)
	grad = network.gradient(x_batch, t_batch)       # 高速版
	
	# 更新参数
	for key in ('W1', 'b1', 'W2', 'b2'):
		network.params[key] -= learning_rate * grad[key]
		
	loss = network.loss(x_batch, t_batch)
	train_loss_list.append(loss)
	# 计算每个epoch的识别精度
	if i % iter_per_epoch == 0:
		train_acc = network.accuracy(x_train, t_train)
		test_acc = network.accuracy(x_test, t_test)
		train_acc_list.append(train_acc)
		test_acc_list.append(test_acc)
		print("train acc, test acc |" + str(train_acc) + "," + str(test_acc))