# 这里的两层网络，指除了输入层还有两层，就是只有一个隐层
import sys, os
import numpy as np
from common.functions import *
from common.layers import *
from collections import OrderedDict

# 只有输入和输出层
class TwoLayerNet:
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
# 初始化权重
self.params = {}
# 用高斯分布初始化
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
self.params['b2'] = np.zeros(output_size)

# 生成层
self.layers = OrderedDict()
self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
self.layers['Relu1'] = Relu()
self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])

self.lastLayer = SoftmaxWithLoss()

def predict(self, x):
for layer in self.layers.values():
x = layer.forward(x)

return x

def loss(self, x, t):
y = self.predict(x)
loss = self.lastLayer.forward(y, t)

return loss

def accuracy(self, x, t):
y = self.predict(x)
y = np.argmax(y, axis=1)
if t.ndim != 1: t = np.argmax(t, axis=1)

accuracy = np.sum(y == t) / float(x.shape[0])
return accuracy

loss_W = lambda W: self.loss(x, t)

# forward
self.loss(x, t)
# backward
dout = 1
dout = self.lastLayer.backward(dout)

layers = list(self.layers.values())
layers.reverse()
for layer in layers:
dout = layer.backward(dout)

print('测试一下')
net = TwoLayerNet(input_size=784, hidden_size=100, output_size=10)
print('param-W1-shape', net.params['W1'].shape)
print('param-b1-shape', net.params['b1'].shape)
print('param-W2-shape', net.params['W2'].shape)
print('param-b2-shape', net.params['b2'].shape)
print('输入伪数据')
x = np.random.rand(100, 784)
t = np.random.rand(100, 10)

print('Mini Batch 的实现')
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)

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

# 超参数
iters_num = 10000
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)

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

for i in range(iters_num):
# 获取 mini-batch

# 反向传播计算梯度
# 更新参数
for key in ('W1', 'b1', 'W2', 'b2'):

# 记录学习过程
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(f'train acc, test acc | {str(train_acc)}, {str(test_acc)}')

import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt

# 绘制图像
epoch_list = [i for i in range(len(train_acc_list))]
plt.plot(epoch_list, test_acc_list, label='test acc')
plt.plot(epoch_list, train_acc_list, label='train acc', linestyle='--')
plt.ylim(-0.0, 1.0)
plt.title('train/test accuracy')
plt.legend()
plt.show()