import torch
from torch import nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
# 超参数
# Hyper Parameters
# 训练整批数据多少次, 为了节约时间, 只训练一次
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 64
TIME_STEP = 28 # rnn time step / image height 时间步数 / 图片高度
INPUT_SIZE = 28 # rnn input size / image width 每步输入值 / 图片每行像素
LR = 0.01 # learning rate
DOWNLOAD_MNIST = True # set to True if haven't download the data
# Mnist 手写数字
# Mnist digital dataset
train_data = dsets.MNIST(
root='./mnist/', # 保存或者提取位置
train=True, # this is training data
transform=transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to
# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
download=DOWNLOAD_MNIST, # download it if you don't have it
)
# plot one example
print(train_data.train_data.size()) # (60000, 28, 28)
print(train_data.train_labels.size()) # (60000)
plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.train_labels[0])
plt.show()
# 数据加载
# Data Loader for easy mini-batch return in training 批训练 50samples, 1 channel, 28x28 (50, 1, 28, 28)
train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
# 测试数据
# convert test data into Variable, pick 2000 samples to speed up testing
test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())
test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1)
test_y = test_data.test_labels.numpy()[:2000] # covert to numpy array
# 用一个 class 来建立 RNN 模型.
# 这个 RNN 整体流程:
# (input0, state0) -> LSTM -> (output0, state1);
# (input1, state1) -> LSTM -> (output1, state2);
# …
# (inputN, stateN)-> LSTM -> (outputN, stateN+1);
# outputN -> Linear -> prediction.
# 通过LSTM分析每一时刻的值, 并且将这一时刻和前面时刻的理解合并在一起, 生成当前时刻对前面数据的理解或记忆. 传递这种理解给下一时刻分析.
# 定义神经网络
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM( # if use nn.RNN(), it hardly learns LSTM 效果要比 nn.RNN() 好多了
input_size=INPUT_SIZE,
hidden_size=64, # rnn hidden unit
num_layers=1, # number of rnn layer 有几层 RNN layers
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size) input & output 会是以 batch size 为第一维度的特征集 e.g. (batch, time_step, input_size)
)
# 输出层
self.out = nn.Linear(64, 10)
def forward(self, x):
# x shape (batch, time_step, input_size)
# r_out shape (batch, time_step, output_size)
# h_n shape (n_layers, batch, hidden_size) # LSTM 有两个 hidden states, h_n 是分线, h_c 是主线
# h_c shape (n_layers, batch, hidden_size)
r_out, (h_n, h_c) = self.rnn(x, None) # None represents zero initial hidden state
# 选取最后一个时间点的 r_out 输出
# choose r_out at the last time step
out = self.out(r_out[:, -1, :]) # 这里 r_out[:, -1, :] 的值也是 h_n 的值
return out
rnn = RNN()
print(rnn)
# 选择优化器
optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters
# 选择损失函数
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
# 训练和测试
# 将图片数据看成一个时间上的连续数据, 每一行的像素点都是这个时刻的输入,
# 读完整张图片就是从上而下的读完了每行的像素点. 然后我们就可以拿出 RNN 在最后一步的分析值判断图片是哪一类了.
# training and testing
for epoch in range(EPOCH):
for step, (b_x, b_y) in enumerate(train_loader): # gives batch data
b_x = b_x.view(-1, 28, 28) # reshape x to (batch, time_step, input_size)
output = rnn(b_x) # rnn output # 喂给 rnn net 训练数据 b_x, 输出预测值
loss = loss_func(output, b_y) # cross entropy loss # 计算两者的误差
optimizer.zero_grad() # clear gradients for this training step # 清空上一步的残余更新参数值
loss.backward() # backpropagation, compute gradients # 误差反向传播, 计算参数更新值
optimizer.step() # apply gradients # 将参数更新值施加到 rnn net 的 parameters 上
if step % 50 == 0:
test_output = rnn(test_x) # (samples, time_step, input_size)
pred_y = torch.max(test_output, 1)[1].data.numpy()
accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size)
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
# print 10 predictions from test data
test_output = rnn(test_x[:10].view(-1, 28, 28))
pred_y = torch.max(test_output, 1)[1].data.numpy()
print(pred_y, 'prediction number')
print(test_y[:10], 'real number')