1.显示数据
代码
import torch
from torch import nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
# torch.manual_seed(1) # reproducible
# Hyper Parameters
EPOCH = 1 # 训练整批数据多少次train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 64 # 批训练的数据个数
TIME_STEP = 28 # 图像高度 rnn time step / image height
INPUT_SIZE = 28 # 图像宽度 rnn input size / image width
LR = 0.01 # learning rate学习效率
DOWNLOAD_MNIST = False # True = 下载文件，此处已下载用False
# Mnist digital dataset
train_data = dsets.MNIST(
root='./mnist/', # 保存位置
train=True, # 训练数据；
transform=transforms.ToTensor(), # 转换成 PIL.Image or numpy.ndarray
# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
download=DOWNLOAD_MNIST, # 下载数据
)
# plot one example
print(train_data.data.size()) # (60000, 28, 28)
print(train_data.targets.size()) # (60000)
plt.imshow(train_data.data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.targets[0])
plt.show()
运行结果
torch.Size([60000, 28, 28])
torch.Size([60000])
2.打印RNN
代码
import torch
from torch import nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
# torch.manual_seed(1) # reproducible
# Hyper Parameters
EPOCH = 1 # 训练整批数据多少次train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 64 # 批训练的数据个数
TIME_STEP = 28 # 图像高度 rnn time step / image height
INPUT_SIZE = 28 # 图像宽度 rnn input size / image width
LR = 0.01 # learning rate学习效率
DOWNLOAD_MNIST = False # True = 下载文件，此处已下载用False
# Mnist digital dataset
train_data = dsets.MNIST(
root='./mnist/', # 保存位置
train=True, # 训练数据；
transform=transforms.ToTensor(), # 转换成 PIL.Image or numpy.ndarray
# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
download=DOWNLOAD_MNIST, # 下载数据
)
# plot one example
print(train_data.data.size()) # (60000, 28, 28)
print(train_data.targets.size()) # (60000)
plt.imshow(train_data.data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.targets[0])
plt.show()
# Data Loader for easy mini-batch return in training
train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
# 验证有没有学好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.data.type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1)
test_y = test_data.targets.numpy()[:2000] # covert to numpy array
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM( #用nn.RNN()准确率很低,LSTM=Long Short Term Memory networks
input_size=INPUT_SIZE,
hidden_size=64, # rnn hidden unit
num_layers=1, # number of rnn layer
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
#(如果time_step,batch, time_step, input_size)等batch不在第一个维度，batch_first=False
)
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)
# h_c shape (n_layers, batch, hidden_size)
r_out, (h_n, h_c) = self.rnn(x, None) # None represents zero initial hidden state
#(h_n, h_c)分别为分线程、主线程hidden state
# choose r_out at the last time step
out = self.out(r_out[:, -1, :])
return out
rnn = RNN()
print(rnn)
结果
RNN(
(rnn): LSTM(28, 64, batch_first=True)
(out): Linear(in_features=64, out_features=10, bias=True)
)
Process finished with exit code 0
3.rnn运行精度
代码
import torch
from torch import nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
# torch.manual_seed(1) # reproducible
# Hyper Parameters
EPOCH = 1 # 训练整批数据多少次train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 64 # 批训练的数据个数
TIME_STEP = 28 # 图像高度 rnn time step / image height
INPUT_SIZE = 28 # 图像宽度 rnn input size / image width
LR = 0.01 # learning rate学习效率
DOWNLOAD_MNIST = False # True = 下载文件，此处已下载用False
# Mnist digital dataset
train_data = dsets.MNIST(
root='./mnist/', # 保存位置
train=True, # 训练数据；
transform=transforms.ToTensor(), # 转换成 PIL.Image or numpy.ndarray
# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
download=DOWNLOAD_MNIST, # 下载数据
)
# plot one example
print(train_data.data.size()) # (60000, 28, 28)
print(train_data.targets.size()) # (60000)
plt.imshow(train_data.data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.targets[0])
plt.show()
# Data Loader for easy mini-batch return in training
train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
# 验证有没有学好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.data.type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1)
test_y = test_data.targets.numpy()[:2000] # covert to numpy array
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM( #用 nn.RNN()准确率很低,LSTM=Long Short Term Memory networks
input_size=INPUT_SIZE,
hidden_size=64, # rnn hidden unit
num_layers=1, # number of rnn layer
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
#(如果time_step,batch, time_step, input_size)等batch不在第一个维度，batch_first=False
)
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)
# h_c shape (n_layers, batch, hidden_size)
r_out, (h_n, h_c) = self.rnn(x, None) # None represents zero initial hidden state
#(h_n, h_c)分别为分线程、主线程hidden state
# choose r_out at the last time step
out = self.out(r_out[:, -1, :])#最后一个时间点r_out输出、r_out[:, -1, :]的值也是h_n的值
return out
rnn = RNN()
print(rnn)
#训练模型啦~~~~
optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # 优化 cnn 参数
loss_func = nn.CrossEntropyLoss() # 交叉熵误差target 不是one-hotted 000100，而是7之类
# training and testing
for epoch in range(EPOCH):
for step, (b_x, b_y) in enumerate(train_loader): # batch data
b_x = b_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() # apply gradients
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')
运行结果
torch.Size([60000, 28, 28])
torch.Size([60000])
RNN(
(rnn): LSTM(28, 64, batch_first=True)
(out): Linear(in_features=64, out_features=10, bias=True)
)
Epoch: 0 | train loss: 2.3125 | test accuracy: 0.10
Epoch: 0 | train loss: 1.2878 | test accuracy: 0.57
Epoch: 0 | train loss: 0.9112 | test accuracy: 0.64
Epoch: 0 | train loss: 0.5646 | test accuracy: 0.72
Epoch: 0 | train loss: 0.6127 | test accuracy: 0.80
Epoch: 0 | train loss: 0.3279 | test accuracy: 0.86
Epoch: 0 | train loss: 0.2661 | test accuracy: 0.86
Epoch: 0 | train loss: 0.2428 | test accuracy: 0.88
Epoch: 0 | train loss: 0.1184 | test accuracy: 0.91
Epoch: 0 | train loss: 0.2252 | test accuracy: 0.93
Epoch: 0 | train loss: 0.1278 | test accuracy: 0.93
Epoch: 0 | train loss: 0.0602 | test accuracy: 0.92
Epoch: 0 | train loss: 0.2122 | test accuracy: 0.94
Epoch: 0 | train loss: 0.0953 | test accuracy: 0.95
Epoch: 0 | train loss: 0.2775 | test accuracy: 0.92
Epoch: 0 | train loss: 0.4711 | test accuracy: 0.94
Epoch: 0 | train loss: 0.1225 | test accuracy: 0.95
Epoch: 0 | train loss: 0.1586 | test accuracy: 0.94
Epoch: 0 | train loss: 0.1500 | test accuracy: 0.93
[7 2 1 0 4 1 4 9 5 9] prediction number
[7 2 1 0 4 1 4 9 5 9] real number
Process finished with exit code 0
