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简述
还是跟之前的CNN一样，都是学于莫烦Python的。
解释
- 关于数据导入部分的代码含义，其实跟之前的CNN几乎完全一致。
- 而且还需要部分的源代码–MNIST（在之前的地方有超链接）
- 这些都可以在下面的CNN的链接中看到
- 卷积神经网络CNN入门【pytorch学习】
模型含义
这里使用RNN，这是跟之前的CNN唯一的不同的地方，其他的都是完全一致的。
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM(
input_size=28,
hidden_size=64,
num_layers=1,
batch_first=True
)
self.out = nn.Linear(64, 10) # fully connected layer, output 10 classes
def forward(self, x):
r_out, (h_n, h_c) = self.rnn(x, None) # None 表示 hidden state 会用全0的 state
# r_out = [BATCH_SIZE, input_size, hidden_size]
# r_out[:, -1, :] = [BATCH_SIZE, hidden_size] '-1'，表示选取最后一个时间点的 r_out 输出
out = self.out(r_out[:, -1, :])
# out = [BATCH_SIZE, 10]
return out
rnn = RNN()
LSTM参数解释
- 输入参数，其实是表示有多少序列。这里的最小单位，考虑的其实不是整个图片的完整全部序列。而是每一行为最小单位的。
- 所以说经过LSTM之后，输出的结果就是
r_out = [BATCH_SIZE, input_size, hidden_size]。 第一个input_size其实是恰好这个图片大小是（input_size, input_size）的
out中输入的有-1
- 会发现这里有一个数字-1，其实就是表示要选最后的一列作为最后的结果。其实就是说只看最后的一行。
完整代码
import os
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
# Hyper Parameters
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 50
LR = 0.001 # learning rate
DOWNLOAD_MNIST = False
# Mnist digits dataset
if not (os.path.exists('./mnist/')) or not os.listdir('./mnist/'):
# not mnist dir or mnist is empyt dir
DOWNLOAD_MNIST = True
train_data = torchvision.datasets.MNIST(
root='./mnist/',
train=True, # this is training data
transform=torchvision.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,
)
# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
# pick 2000 samples to speed up testing
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[
:2000] / 255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels[:2000]
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM(
input_size=28,
hidden_size=64,
num_layers=1,
batch_first=True
)
self.out = nn.Linear(64, 10) # fully connected layer, output 10 classes
def forward(self, x):
r_out, (h_n, h_c) = self.rnn(x, None) # None 表示 hidden state 会用全0的 state
# r_out = [BATCH_SIZE, input_size, hidden_size]
# r_out[:, -1, :] = [BATCH_SIZE, hidden_size] '-1'，表示选取最后一个时间点的 r_out 输出
out = self.out(r_out[:, -1, :])
# out = [BATCH_SIZE, 10]
return out
rnn = RNN()
optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
for epoch in range(EPOCH):
for step, (x, b_y) in enumerate(train_loader): # gives batch data
b_x = x.view(-1, 28, 28) # reshape x to (batch, time_step, input_size)
output = rnn(b_x) # rnn output
loss = loss_func(output, b_y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # backpropagation, compute gradients
optimizer.step()
test_output = rnn(test_x[:10].view(-1, 28, 28))
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
print(pred_y, 'prediction number')
print(test_y[:10], 'real number')
- 结果：
[7 2 1 0 4 1 4 9 6 9] prediction number
tensor([7, 2, 1, 0, 4, 1, 4, 9, 5, 9]) real number