torchkeras 1.5.3

1、引言

torchkeras库是一个简单的工具，用于在pytorch-jusk中以keras风格训练神经网络。在

使用torchkeras，您不需要用很多行代码编写训练循环，您只需要做的就是

如下三个步骤：

（i）创建您的人际网络并用火炬。型号像这样：model = torchkeras.Model(net)

（ii）编译模型以绑定损失函数、优化器和度量函数。在

（iii）用训练数据拟合模型并验证数据。在

这个项目似乎有点强大，但源代码非常简单。

实际上，不到300行Python代码。

如果您想了解或修改此项目的某些细节，请随时阅读并更改源代码！！！

2、举例说明

您可以使用pip安装火炬： pip install torchkeras

这里有一个完整的例子使用火炬！在

importnumpyasnpimportpandasaspdfrommatplotlibimportpyplotaspltimporttorchfromtorchimportnnimporttorch.nn.functionalasFfromtorch.utils.dataimportDataset,DataLoader,TensorDatasetimporttorchkeras#Attention this line 

（1） 准备数据

^{pr2}$

# split samples into train and valid data.ds=TensorDataset(X,Y)ds_train,ds_valid=torch.utils.data.random_split(ds,[int(len(ds)*0.7),len(ds)-int(len(ds)*0.7)])dl_train=DataLoader(ds_train,batch_size=100,shuffle=True,num_workers=2)dl_valid=DataLoader(ds_valid,batch_size=100,num_workers=2)

（2） 创建模型

classNet(nn.Module):def__init__(self):super().__init__()self.fc1=nn.Linear(2,4)self.fc2=nn.Linear(4,8)self.fc3=nn.Linear(8,1)defforward(self,x):x=F.relu(self.fc1(x))x=F.relu(self.fc2(x))y=nn.Sigmoid()(self.fc3(x))returnynet=Net()### Attention heremodel=torchkeras.Model(net)model.summary(input_shape=(2,))

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Linear-1                    [-1, 4]              12
            Linear-2                    [-1, 8]              40
            Linear-3                    [-1, 1]               9
================================================================
Total params: 61
Trainable params: 61
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.000008
Forward/backward pass size (MB): 0.000099
Params size (MB): 0.000233
Estimated Total Size (MB): 0.000340
----------------------------------------------------------------

（3） 训练模型

# define metricdefaccuracy(y_pred,y_true):y_pred=torch.where(y_pred>0.5,torch.ones_like(y_pred,dtype=torch.float32),torch.zeros_like(y_pred,dtype=torch.float32))acc=torch.mean(1-torch.abs(y_true-y_pred))returnacc# if gpu is available, use gpudevice=torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")model.compile(loss_func=nn.BCELoss(),optimizer=torch.optim.Adam(model.parameters(),lr=0.01),metrics_dict={"accuracy":accuracy},device=device)dfhistory=model.fit(30,dl_train=dl_train,dl_val=dl_valid,log_step_freq=20)

Start Training ...

================================================================================2020-06-21 20:40:23
{'step': 10, 'loss': 0.217, 'accuracy': 0.905}
{'step': 20, 'loss': 0.215, 'accuracy': 0.914}

 +-------+-------+----------+----------+--------------+
| epoch |  loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
|   1   | 0.212 |  0.914   |  0.186   |    0.927     |
+-------+-------+----------+----------+--------------+

================================================================================2020-06-21 20:40:23
{'step': 10, 'loss': 0.211, 'accuracy': 0.912}
{'step': 20, 'loss': 0.193, 'accuracy': 0.919}

 +-------+-------+----------+----------+--------------+
| epoch |  loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
|   2   | 0.194 |  0.919   |  0.188   |    0.935     |
+-------+-------+----------+----------+--------------+

================================================================================2020-06-21 20:40:23
{'step': 10, 'loss': 0.217, 'accuracy': 0.913}
{'step': 20, 'loss': 0.205, 'accuracy': 0.92}

 +-------+-------+----------+----------+--------------+
| epoch |  loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
|   3   | 0.195 |  0.921   |  0.176   |    0.931     |
+-------+-------+----------+----------+--------------+

================================================================================2020-06-21 20:40:23
{'step': 10, 'loss': 0.164, 'accuracy': 0.932}
{'step': 20, 'loss': 0.197, 'accuracy': 0.917}

 +-------+-------+----------+----------+--------------+
| epoch |  loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
|   4   | 0.197 |  0.917   |  0.178   |    0.935     |
+-------+-------+----------+----------+--------------+

================================================================================2020-06-21 20:40:24
{'step': 10, 'loss': 0.192, 'accuracy': 0.926}
{'step': 20, 'loss': 0.182, 'accuracy': 0.931}

 +-------+-------+----------+----------+--------------+
| epoch |  loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
|   5   | 0.193 |  0.924   |  0.188   |    0.928     |
+-------+-------+----------+----------+--------------+

================================================================================2020-06-21 20:40:44
{'step': 10, 'loss': 0.175, 'accuracy': 0.932}
{'step': 20, 'loss': 0.188, 'accuracy': 0.924}

 +-------+-------+----------+----------+--------------+
| epoch |  loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
|   97  | 0.184 |  0.923   |  0.176   |    0.935     |
+-------+-------+----------+----------+--------------+

================================================================================2020-06-21 20:40:44
{'step': 10, 'loss': 0.21, 'accuracy': 0.913}
{'step': 20, 'loss': 0.192, 'accuracy': 0.918}

 +-------+------+----------+----------+--------------+
| epoch | loss | accuracy | val_loss | val_accuracy |
+-------+------+----------+----------+--------------+
|   98  | 0.19 |  0.922   |  0.179   |    0.934     |
+-------+------+----------+----------+--------------+

================================================================================2020-06-21 20:40:45
{'step': 10, 'loss': 0.186, 'accuracy': 0.923}
{'step': 20, 'loss': 0.181, 'accuracy': 0.928}

 +-------+-------+----------+----------+--------------+
| epoch |  loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
|   99  | 0.182 |  0.926   |  0.178   |    0.938     |
+-------+-------+----------+----------+--------------+

================================================================================2020-06-21 20:40:45
{'step': 10, 'loss': 0.16, 'accuracy': 0.93}
{'step': 20, 'loss': 0.173, 'accuracy': 0.93}

 +-------+-------+----------+----------+--------------+
| epoch |  loss | accuracy | val_loss | val_accuracy |
+-------+-------+----------+----------+--------------+
|  100  | 0.185 |  0.925   |  0.174   |    0.936     |
+-------+-------+----------+----------+--------------+

================================================================================2020-06-21 20:40:45
Finished Training...

# visual the resultsfig,(ax1,ax2)=plt.subplots(nrows=1,ncols=2,figsize=(12,5))ax1.scatter(Xp[:,0],Xp[:,1],c="r")ax1.scatter(Xn[:,0],Xn[:,1],c="g")ax1.legend(["positive","negative"]);ax1.set_title("y_true")Xp_pred=X[torch.squeeze(model.forward(X)>=0.5)]Xn_pred=X[torch.squeeze(model.forward(X)<0.5)]ax2.scatter(Xp_pred[:,0],Xp_pred[:,1],c="r")ax2.scatter(Xn_pred[:,0],Xn_pred[:,1],c="g")ax2.legend(["positive","negative"]);ax2.set_title("y_pred")

（4） 评估模型

%matplotlibinline%configInlineBackend.figure_format='svg'importmatplotlib.pyplotaspltdefplot_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_loss': 0.13576620258390903, 'val_accuracy': 0.9441666702429453}
（5） 使用模型
model.predict(dl_valid)[0:10]
tensor([[0.8767],
        [0.0154],
        [0.9976],
        [0.9990],
        [0.9984],
        [0.0071],
        [0.3529],
        [0.4061],
        [0.9938],
        [0.9997]])
forfeatures,labelsindl_valid:withtorch.no_grad():predictions=model.forward(features)print(predictions[0:10])break
tensor([[0.9979],
        [0.0011],
        [0.9782],
        [0.9675],
        [0.9653],
        [0.9906],
        [0.1774],
        [0.9994],
        [0.9178],
        [0.9579]])
(6) save the model
# save the model parameterstorch.save(model.state_dict(),"model_parameter.pkl")model_clone=torchkeras.Model(Net())model_clone.load_state_dict(torch.load("model_parameter.pkl"))model_clone.compile(loss_func=nn.BCELoss(),optimizer=torch.optim.Adam(model.parameters(),lr=0.01),metrics_dict={"accuracy":accuracy})model_clone.evaluate(dl_valid)
{'val_loss': 0.17422042911251387, 'val_accuracy': 0.9358333299557368}
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