使用GPU训练模型
公众号后台回复关键字：Pytorch，获取项目github地址。
Pytorch没有官方的高阶API。一般通过nn.Module来构建模型并编写自定义训练循环。
为了更加方便地训练模型，作者编写了仿keras的Pytorch模型接口：torchkeras， 作为Pytorch的高阶API。
本章我们主要详细介绍Pytorch的高阶API如下相关的内容。
构建模型的3种方法(继承nn.Module基类，使用nn.Sequential，辅助应用模型容器)
训练模型的3种方法(脚本风格，函数风格，torchkeras.Model类风格)
使用GPU训练模型(单GPU训练，多GPU训练)
本篇我们介绍使用GPU训练模型。
深度学习的训练过程常常非常耗时，一个模型训练几个小时是家常便饭，训练几天也是常有的事情，有时候甚至要训练几十天。
训练过程的耗时主要来自于两个部分，一部分来自数据准备，另一部分来自参数迭代。
当数据准备过程还是模型训练时间的主要瓶颈时，我们可以使用更多进程来准备数据。
当参数迭代过程成为训练时间的主要瓶颈时，我们通常的方法是应用GPU来进行加速。
Pytorch中使用GPU加速模型非常简单，只要将模型和数据移动到GPU上。核心代码只有以下几行。
# 定义模型
...
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device) # 移动模型到cuda
# 训练模型
...
features = features.to(device) # 移动数据到cuda
labels = labels.to(device) # 或者 labels = labels.cuda() if torch.cuda.is_available() else labels
...
如果要使用多个GPU训练模型，也非常简单。只需要在将模型设置为数据并行风格模型。则模型移动到GPU上之后，会在每一个GPU上拷贝一个副本，并把数据平分到各个GPU上进行训练。核心代码如下。
# 定义模型
...
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model) # 包装为并行风格模型
# 训练模型
...
features = features.to(device) # 移动数据到cuda
labels = labels.to(device) # 或者 labels = labels.cuda() if torch.cuda.is_available() else labels
...
以下是一些和GPU有关的基本操作汇总
在Colab笔记本中：修改->笔记本设置->硬件加速器 中选择 GPU
注：以下代码只能在Colab 上才能正确执行。
可点击如下链接，直接在colab中运行范例代码。
《torch使用gpu训练模型》
https://colab.research.google.com/drive/1FDmi44-U3TFRCt9MwGn4HIj2SaaWIjHu?usp=sharing
import torch
from torch import nn
# 1，查看gpu信息
if_cuda = torch.cuda.is_available()
print("if_cuda=",if_cuda)
gpu_count = torch.cuda.device_count()
print("gpu_count=",gpu_count)
if_cuda= True
gpu_count= 1
# 2，将张量在gpu和cpu间移动
tensor = torch.rand((100,100))
tensor_gpu = tensor.to("cuda:0") # 或者 tensor_gpu = tensor.cuda()
print(tensor_gpu.device)
print(tensor_gpu.is_cuda)
tensor_cpu = tensor_gpu.to("cpu") # 或者 tensor_cpu = tensor_gpu.cpu()
print(tensor_cpu.device)
cuda:0
True
cpu
# 3，将模型中的全部张量移动到gpu上
net = nn.Linear(2,1)
print(next(net.parameters()).is_cuda)
net.to("cuda:0") # 将模型中的全部参数张量依次到GPU上，注意，无需重新赋值为 net = net.to("cuda:0")
print(next(net.parameters()).is_cuda)
print(next(net.parameters()).device)
False
True
cuda:0
# 4，创建支持多个gpu数据并行的模型
linear = nn.Linear(2,1)
print(next(linear.parameters()).device)
model = nn.DataParallel(linear)
print(model.device_ids)
print(next(model.module.parameters()).device)
#注意保存参数时要指定保存model.module的参数
torch.save(model.module.state_dict(), "./data/model_parameter.pkl")
linear = nn.Linear(2,1)
linear.load_state_dict(torch.load("./data/model_parameter.pkl"))
cpu
[0]
cuda:0
# 5，清空cuda缓存
# 该方法在cuda超内存时十分有用
torch.cuda.empty_cache()
一，矩阵乘法范例
下面分别使用CPU和GPU作一个矩阵乘法，并比较其计算效率。
import time
import torch
from torch import nn
# 使用cpu
a = torch.rand((10000,200))
b = torch.rand((200,10000))
tic = time.time()
c = torch.matmul(a,b)
toc = time.time()
print(toc-tic)
print(a.device)
print(b.device)
0.6454010009765625
cpu
cpu
# 使用gpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
a = torch.rand((10000,200),device = device) #可以指定在GPU上创建张量
b = torch.rand((200,10000)) #也可以在CPU上创建张量后移动到GPU上
b = b.to(device) #或者 b = b.cuda() if torch.cuda.is_available() else b
tic = time.time()
c = torch.matmul(a,b)
toc = time.time()
print(toc-tic)
print(a.device)
print(b.device)
0.014541149139404297
cuda:0
cuda:0
二，线性回归范例
下面对比使用CPU和GPU训练一个线性回归模型的效率
1，使用CPU
# 准备数据
n = 1000000 #样本数量
X = 10*torch.rand([n,2])-5.0 #torch.rand是均匀分布
w0 = torch.tensor([[2.0,-3.0]])
b0 = torch.tensor([[10.0]])
Y = [email protected]() + b0 + torch.normal( 0.0,2.0,size = [n,1]) # @表示矩阵乘法,增加正态扰动
# 定义模型
class LinearRegression(nn.Module):
def __init__(self):
super().__init__()
self.w = nn.Parameter(torch.randn_like(w0))
self.b = nn.Parameter(torch.zeros_like(b0))
#正向传播
def forward(self,x):
return [email protected]() + self.b
linear = LinearRegression()
# 训练模型
optimizer = torch.optim.Adam(linear.parameters(),lr = 0.1)
loss_func = nn.MSELoss()
def train(epoches):
tic = time.time()
for epoch in range(epoches):
optimizer.zero_grad()
Y_pred = linear(X)
loss = loss_func(Y_pred,Y)
loss.backward()
optimizer.step()
if epoch%50==0:
print({"epoch":epoch,"loss":loss.item()})
toc = time.time()
print("time used:",toc-tic)
train(500)
{'epoch': 0, 'loss': 3.996487855911255}
{'epoch': 50, 'loss': 3.9969770908355713}
{'epoch': 100, 'loss': 3.9964890480041504}
{'epoch': 150, 'loss': 3.996488332748413}
{'epoch': 200, 'loss': 3.996488094329834}
{'epoch': 250, 'loss': 3.996488332748413}
{'epoch': 300, 'loss': 3.996488332748413}
{'epoch': 350, 'loss': 3.996488094329834}
{'epoch': 400, 'loss': 3.996488332748413}
{'epoch': 450, 'loss': 3.996488094329834}
time used: 5.4090576171875
2，使用GPU
# 准备数据
n = 1000000 #样本数量
X = 10*torch.rand([n,2])-5.0 #torch.rand是均匀分布
w0 = torch.tensor([[2.0,-3.0]])
b0 = torch.tensor([[10.0]])
Y = [email protected]() + b0 + torch.normal( 0.0,2.0,size = [n,1]) # @表示矩阵乘法,增加正态扰动
# 移动到GPU上
print("torch.cuda.is_available() = ",torch.cuda.is_available())
X = X.cuda()
Y = Y.cuda()
print("X.device:",X.device)
print("Y.device:",Y.device)
torch.cuda.is_available() = True
X.device: cuda:0
Y.device: cuda:0
# 定义模型
class LinearRegression(nn.Module):
def __init__(self):
super().__init__()
self.w = nn.Parameter(torch.randn_like(w0))
self.b = nn.Parameter(torch.zeros_like(b0))
#正向传播
def forward(self,x):
return [email protected]() + self.b
linear = LinearRegression()
# 移动模型到GPU上
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
linear.to(device)
#查看模型是否已经移动到GPU上
print("if on cuda:",next(linear.parameters()).is_cuda)
if on cuda: True
# 训练模型
optimizer = torch.optim.Adam(linear.parameters(),lr = 0.1)
loss_func = nn.MSELoss()
def train(epoches):
tic = time.time()
for epoch in range(epoches):
optimizer.zero_grad()
Y_pred = linear(X)
loss = loss_func(Y_pred,Y)
loss.backward()
optimizer.step()
if epoch%50==0:
print({"epoch":epoch,"loss":loss.item()})
toc = time.time()
print("time used:",toc-tic)
train(500)
{'epoch': 0, 'loss': 3.9982845783233643}
{'epoch': 50, 'loss': 3.998818874359131}
{'epoch': 100, 'loss': 3.9982895851135254}
{'epoch': 150, 'loss': 3.9982845783233643}
{'epoch': 200, 'loss': 3.998284339904785}
{'epoch': 250, 'loss': 3.9982845783233643}
{'epoch': 300, 'loss': 3.9982845783233643}
{'epoch': 350, 'loss': 3.9982845783233643}
{'epoch': 400, 'loss': 3.9982845783233643}
{'epoch': 450, 'loss': 3.9982845783233643}
time used: 0.4889392852783203
三，torchkeras使用单GPU范例
下面演示使用torchkeras来应用GPU训练模型的方法。
其对应的CPU训练模型代码参见《6-2,训练模型的3种方法》
本例仅需要在它的基础上增加一行代码，在model.compile时指定 device即可。
1，准备数据
!pip install -U torchkeras
import torch
from torch import nn
import torchvision
from torchvision import transforms
import torchkeras
transform = transforms.Compose([transforms.ToTensor()])
ds_train = torchvision.datasets.MNIST(root="./data/minist/",train=True,download=True,transform=transform)
ds_valid = torchvision.datasets.MNIST(root="./data/minist/",train=False,download=True,transform=transform)
dl_train = torch.utils.data.DataLoader(ds_train, batch_size=128, shuffle=True, num_workers=4)
dl_valid = torch.utils.data.DataLoader(ds_valid, batch_size=128, shuffle=False, num_workers=4)
print(len(ds_train))
print(len(ds_valid))
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
#查看部分样本
from matplotlib import pyplot as plt
plt.figure(figsize=(8,8))
for i in range(9):
img,label = ds_train[i]
img = torch.squeeze(img)
ax=plt.subplot(3,3,i+1)
ax.imshow(img.numpy())
ax.set_title("label = %d"%label)
ax.set_xticks([])
ax.set_yticks([])
plt.show()
2，定义模型
class CnnModel(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.ModuleList([
nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Dropout2d(p = 0.1),
nn.AdaptiveMaxPool2d((1,1)),
nn.Flatten(),
nn.Linear(64,32),
nn.ReLU(),
nn.Linear(32,10)]
)
def forward(self,x):
for layer in self.layers:
x = layer(x)
return x
net = CnnModel()
model = torchkeras.Model(net)
model.summary(input_shape=(1,32,32))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 32, 30, 30] 320
MaxPool2d-2 [-1, 32, 15, 15] 0
Conv2d-3 [-1, 64, 11, 11] 51,264
MaxPool2d-4 [-1, 64, 5, 5] 0
Dropout2d-5 [-1, 64, 5, 5] 0
AdaptiveMaxPool2d-6 [-1, 64, 1, 1] 0
Flatten-7 [-1, 64] 0
Linear-8 [-1, 32] 2,080
ReLU-9 [-1, 32] 0
Linear-10 [-1, 10] 330
================================================================
Total params: 53,994
Trainable params: 53,994
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.003906
Forward/backward pass size (MB): 0.359695
Params size (MB): 0.205971
Estimated Total Size (MB): 0.569572
----------------------------------------------------------------
3，训练模型
from sklearn.metrics import accuracy_score
def accuracy(y_pred,y_true):
y_pred_cls = torch.argmax(nn.Softmax(dim=1)(y_pred),dim=1).data
return accuracy_score(y_true.cpu().numpy(),y_pred_cls.cpu().numpy())
# 注意此处要将数据先移动到cpu上，然后才能转换成numpy数组
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.compile(loss_func = nn.CrossEntropyLoss(),
optimizer= torch.optim.Adam(model.parameters(),lr = 0.02),
metrics_dict={"accuracy":accuracy},device = device) # 注意此处compile时指定了device
dfhistory = model.fit(3,dl_train = dl_train, dl_val=dl_valid, log_step_freq=100)
Start Training ...
================================================================================2020-06-27 00:24:29
{'step': 100, 'loss': 1.063, 'accuracy': 0.619}
{'step': 200, 'loss': 0.681, 'accuracy': 0.764}
{'step': 300, 'loss': 0.534, 'accuracy': 0.818}
{'step': 400, 'loss': 0.458, 'accuracy': 0.847}
+-------+-------+----------+----------+--------------+
| epoch | loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
| 1 | 0.412 | 0.863 | 0.128 | 0.961 |
+-------+-------+----------+----------+--------------+
================================================================================2020-06-27 00:24:35
{'step': 100, 'loss': 0.147, 'accuracy': 0.956}
{'step': 200, 'loss': 0.156, 'accuracy': 0.954}
{'step': 300, 'loss': 0.156, 'accuracy': 0.954}
{'step': 400, 'loss': 0.157, 'accuracy': 0.955}
+-------+-------+----------+----------+--------------+
| epoch | loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
| 2 | 0.153 | 0.956 | 0.085 | 0.976 |
+-------+-------+----------+----------+--------------+
================================================================================2020-06-27 00:24:42
{'step': 100, 'loss': 0.126, 'accuracy': 0.965}
{'step': 200, 'loss': 0.147, 'accuracy': 0.96}
{'step': 300, 'loss': 0.153, 'accuracy': 0.959}
{'step': 400, 'loss': 0.147, 'accuracy': 0.96}
+-------+-------+----------+----------+--------------+
| epoch | loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
| 3 | 0.146 | 0.96 | 0.119 | 0.968 |
+-------+-------+----------+----------+--------------+
================================================================================2020-06-27 00:24:48
Finished Training...
4，评估模型
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
def plot_metric(dfhistory, metric):
train_metrics = dfhistory[metric]
val_metrics = dfhistory['val_'+metric]
epochs = range(1, len(train_metrics) + 1)
plt.plot(epochs, train_metrics, 'bo--')
plt.plot(epochs, val_metrics, 'ro-')
plt.title('Training and validation '+ metric)
plt.xlabel("Epochs")
plt.ylabel(metric)
plt.legend(["train_"+metric, 'val_'+metric])
plt.show()
plot_metric(dfhistory,"loss")
plot_metric(dfhistory,"accuracy")
model.evaluate(dl_valid)
{'val_accuracy': 0.967068829113924, 'val_loss': 0.11601964030650598}
5，使用模型
model.predict(dl_valid)[0:10]
tensor([[ -9.2092, 3.1997, 1.4028, -2.7135, -0.7320, -2.0518, -20.4938,
14.6774, 1.7616, 5.8549],
[ 2.8509, 4.9781, 18.0946, 0.0928, -1.6061, -4.1437, 4.8697,
3.8811, 4.3869, -3.5929],
[-22.5231, 13.6643, 5.0244, -11.0188, -16.8147, -9.5894, -6.2556,
-10.5648, -12.1022, -19.4685],
[ 23.2670, -12.0711, -7.3968, -8.2715, -1.0915, -12.6050, 8.0444,
-16.9339, 1.8827, -0.2497],
[ -4.1159, 3.2102, 0.4971, -11.8064, 12.1460, -5.1650, -6.5918,
1.0088, 0.8362, 2.5132],
[-26.1764, 15.6251, 6.1191, -12.2424, -13.9725, -10.0540, -7.8669,
-5.9602, -11.1944, -18.7890],
[ -5.0602, 3.3779, -0.6647, -8.5185, 10.0320, -5.5107, -6.9579,
2.3811, 0.2542, 3.2860],
[ 4.1017, -0.4282, 7.2220, 3.3700, -3.6813, 1.1576, -1.8479,
0.7450, 3.9768, 6.2640],
[ 1.9689, -0.3960, 7.4414, -10.4789, 2.7066, 1.7482, 5.7971,
-4.5808, 3.0911, -5.1971],
[ -2.9680, -1.2369, -0.0829, -1.8577, 1.9380, -0.8374, -8.2207,
3.5060, 3.8735, 13.6762]], device='cuda:0')
6，保存模型
# save the model parameters
torch.save(model.state_dict(), "model_parameter.pkl")
model_clone = torchkeras.Model(CnnModel())
model_clone.load_state_dict(torch.load("model_parameter.pkl"))
model_clone.compile(loss_func = nn.CrossEntropyLoss(),
optimizer= torch.optim.Adam(model.parameters(),lr = 0.02),
metrics_dict={"accuracy":accuracy},device = device) # 注意此处compile时指定了device
model_clone.evaluate(dl_valid)
{'val_accuracy': 0.967068829113924, 'val_loss': 0.11601964030650598}
四，torchkeras使用多GPU范例
注：以下范例需要在有多个GPU的机器上跑。如果在单GPU的机器上跑，也能跑通，但是实际上使用的是单个GPU。
1，准备数据
import torch
from torch import nn
import torchvision
from torchvision import transforms
import torchkeras
transform = transforms.Compose([transforms.ToTensor()])
ds_train = torchvision.datasets.MNIST(root="./data/minist/",train=True,download=True,transform=transform)
ds_valid = torchvision.datasets.MNIST(root="./data/minist/",train=False,download=True,transform=transform)
dl_train = torch.utils.data.DataLoader(ds_train, batch_size=128, shuffle=True, num_workers=4)
dl_valid = torch.utils.data.DataLoader(ds_valid, batch_size=128, shuffle=False, num_workers=4)
print(len(ds_train))
print(len(ds_valid))
2，定义模型
class CnnModule(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.ModuleList([
nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Dropout2d(p = 0.1),
nn.AdaptiveMaxPool2d((1,1)),
nn.Flatten(),
nn.Linear(64,32),
nn.ReLU(),
nn.Linear(32,10)]
)
def forward(self,x):
for layer in self.layers:
x = layer(x)
return x
net = nn.DataParallel(CnnModule()) #Attention this line!!!
model = torchkeras.Model(net)
model.summary(input_shape=(1,32,32))
3，训练模型
from sklearn.metrics import accuracy_score
def accuracy(y_pred,y_true):
y_pred_cls = torch.argmax(nn.Softmax(dim=1)(y_pred),dim=1).data
return accuracy_score(y_true.cpu().numpy(),y_pred_cls.cpu().numpy())
# 注意此处要将数据先移动到cpu上，然后才能转换成numpy数组
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.compile(loss_func = nn.CrossEntropyLoss(),
optimizer= torch.optim.Adam(model.parameters(),lr = 0.02),
metrics_dict={"accuracy":accuracy},device = device) # 注意此处compile时指定了device
dfhistory = model.fit(3,dl_train = dl_train, dl_val=dl_valid, log_step_freq=100)
Start Training ...
================================================================================2020-06-27 00:24:29
{'step': 100, 'loss': 1.063, 'accuracy': 0.619}
{'step': 200, 'loss': 0.681, 'accuracy': 0.764}
{'step': 300, 'loss': 0.534, 'accuracy': 0.818}
{'step': 400, 'loss': 0.458, 'accuracy': 0.847}
+-------+-------+----------+----------+--------------+
| epoch | loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
| 1 | 0.412 | 0.863 | 0.128 | 0.961 |
+-------+-------+----------+----------+--------------+
================================================================================2020-06-27 00:24:35
{'step': 100, 'loss': 0.147, 'accuracy': 0.956}
{'step': 200, 'loss': 0.156, 'accuracy': 0.954}
{'step': 300, 'loss': 0.156, 'accuracy': 0.954}
{'step': 400, 'loss': 0.157, 'accuracy': 0.955}
+-------+-------+----------+----------+--------------+
| epoch | loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
| 2 | 0.153 | 0.956 | 0.085 | 0.976 |
+-------+-------+----------+----------+--------------+
================================================================================2020-06-27 00:24:42
{'step': 100, 'loss': 0.126, 'accuracy': 0.965}
{'step': 200, 'loss': 0.147, 'accuracy': 0.96}
{'step': 300, 'loss': 0.153, 'accuracy': 0.959}
{'step': 400, 'loss': 0.147, 'accuracy': 0.96}
+-------+-------+----------+----------+--------------+
| epoch | loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
| 3 | 0.146 | 0.96 | 0.119 | 0.968 |
+-------+-------+----------+----------+--------------+
================================================================================2020-06-27 00:24:48
Finished Training...
4，评估模型
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
def plot_metric(dfhistory, metric):
train_metrics = dfhistory[metric]
val_metrics = dfhistory['val_'+metric]
epochs = range(1, len(train_metrics) + 1)
plt.plot(epochs, train_metrics, 'bo--')
plt.plot(epochs, val_metrics, 'ro-')
plt.title('Training and validation '+ metric)
plt.xlabel("Epochs")
plt.ylabel(metric)
plt.legend(["train_"+metric, 'val_'+metric])
plt.show()
plot_metric(dfhistory, "loss")
plot_metric(dfhistory,"accuracy")
model.evaluate(dl_valid)
{'val_accuracy': 0.9603441455696202, 'val_loss': 0.14203246376371081}
5，使用模型
model.predict(dl_valid)[0:10]
tensor([[ -9.2092, 3.1997, 1.4028, -2.7135, -0.7320, -2.0518, -20.4938,
14.6774, 1.7616, 5.8549],
[ 2.8509, 4.9781, 18.0946, 0.0928, -1.6061, -4.1437, 4.8697,
3.8811, 4.3869, -3.5929],
[-22.5231, 13.6643, 5.0244, -11.0188, -16.8147, -9.5894, -6.2556,
-10.5648, -12.1022, -19.4685],
[ 23.2670, -12.0711, -7.3968, -8.2715, -1.0915, -12.6050, 8.0444,
-16.9339, 1.8827, -0.2497],
[ -4.1159, 3.2102, 0.4971, -11.8064, 12.1460, -5.1650, -6.5918,
1.0088, 0.8362, 2.5132],
[-26.1764, 15.6251, 6.1191, -12.2424, -13.9725, -10.0540, -7.8669,
-5.9602, -11.1944, -18.7890],
[ -5.0602, 3.3779, -0.6647, -8.5185, 10.0320, -5.5107, -6.9579,
2.3811, 0.2542, 3.2860],
[ 4.1017, -0.4282, 7.2220, 3.3700, -3.6813, 1.1576, -1.8479,
0.7450, 3.9768, 6.2640],
[ 1.9689, -0.3960, 7.4414, -10.4789, 2.7066, 1.7482, 5.7971,
-4.5808, 3.0911, -5.1971],
[ -2.9680, -1.2369, -0.0829, -1.8577, 1.9380, -0.8374, -8.2207,
3.5060, 3.8735, 13.6762]], device='cuda:0')
6，保存模型
# save the model parameters
torch.save(model.net.module.state_dict(), "model_parameter.pkl")
net_clone = CnnModel()
net_clone.load_state_dict(torch.load("model_parameter.pkl"))
model_clone = torchkeras.Model(net_clone)
model_clone.compile(loss_func = nn.CrossEntropyLoss(),
optimizer= torch.optim.Adam(model.parameters(),lr = 0.02),
metrics_dict={"accuracy":accuracy},device = device)
model_clone.evaluate(dl_valid)
{'val_accuracy': 0.9603441455696202, 'val_loss': 0.14203246376371081}
如果本书对你有所帮助，想鼓励一下作者，记得给本项目加一颗星星star⭐️，并分享给你的朋友们喔????!
如果对本书内容理解上有需要进一步和作者交流的地方，可以在公众号后台回复关键字：加群，加入读者交流群和大家讨论。
公众号后台回复关键字：pytorch，获取项目github地址。
