Metadata-Version: 2.1
Name: torchkeras
Version: 3.0.1
Summary: pytorch❤️ keras
Home-page: https://github.com/lyhue1991/torchkeras
Author: PythonAiRoad, Laugh
Author-email: lyhue1991@163.com
License: UNKNOWN
Description: # 1，Introduction
        
        
        The torchkeras library is a simple tool for training neural network in pytorch jusk like in a keras style. 😋😋
        
        With torchkeras, You need not to write your training loop with many lines of code, all you need to do is just 
        
        like this three steps as below:
        
        (i) create your network and wrap it and the loss_fn together with torchkeras.KerasModel like this: `model = torchkeras.KerasModel(net,loss_fn)` 
        
        (ii) fit your model with the training data and validate data.
        
        **This project seems somehow powerful, but the source code is very simple.**
        
        **Actually, less than 200 lines of Python code.**
        
        **If you want to understand or modify some details of this project, feel free to read and change the source code!!!**
        
        
        
        
        
        # 2,  Use example
        
        
        You can install torchkeras using pip:
        `pip install torchkeras`
        
        
        Here is a complete examples using torchkeras! 
        
        ```python
        import numpy as np 
        import pandas as pd 
        from matplotlib import pyplot as plt
        import torch
        from torch import nn
        import torch.nn.functional as F
        from torch.utils.data import Dataset,DataLoader,TensorDataset
        
        import torchkeras #Attention this line 
        
        
        ```
        
        ### (1) prepare data 
        
        ```python
        %matplotlib inline
        %config InlineBackend.figure_format = 'svg'
        
        #number of samples
        n_positive,n_negative = 2000,2000
        
        #positive samples
        r_p = 5.0 + torch.normal(0.0,1.0,size = [n_positive,1]) 
        theta_p = 2*np.pi*torch.rand([n_positive,1])
        Xp = torch.cat([r_p*torch.cos(theta_p),r_p*torch.sin(theta_p)],axis = 1)
        Yp = torch.ones_like(r_p)
        
        #negative samples
        r_n = 8.0 + torch.normal(0.0,1.0,size = [n_negative,1]) 
        theta_n = 2*np.pi*torch.rand([n_negative,1])
        Xn = torch.cat([r_n*torch.cos(theta_n),r_n*torch.sin(theta_n)],axis = 1)
        Yn = torch.zeros_like(r_n)
        
        #concat positive and negative samples
        X = torch.cat([Xp,Xn],axis = 0)
        Y = torch.cat([Yp,Yn],axis = 0)
        
        
        #visual samples
        plt.figure(figsize = (6,6))
        plt.scatter(Xp[:,0],Xp[:,1],c = "r")
        plt.scatter(Xn[:,0],Xn[:,1],c = "g")
        plt.legend(["positive","negative"]);
        ```
        
        ![](./data/input_data.png)
        
        ```python
        # split samples into train and valid data.
        ds = TensorDataset(X,Y)
        ds_train,ds_val = 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_val = DataLoader(ds_val,batch_size = 100,num_workers=2)
        
        ```
        
        ```python
        for features,labels in dl_train:
            break
        print(features.shape)
        print(labels.shape)
        ```
        
        ```python
        
        ```
        
        ### (2) create the  model
        
        ```python
        class Net(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)
                
            def forward(self,x):
                x = F.relu(self.fc1(x))
                x = F.relu(self.fc2(x))
                y = nn.Sigmoid()(self.fc3(x))
                return y
                
        net = Net()
        
        from torchmetrics import Metric 
        class Accuracy(Metric):
            def __init__(self, dist_sync_on_step=False):
                super().__init__(dist_sync_on_step=dist_sync_on_step)
        
                self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
                self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
        
            def update(self, preds: torch.Tensor, targets: torch.Tensor):
                assert preds.shape == targets.shape
        
                self.correct += torch.sum((preds>=0.5)==(targets>0.5))
                self.total += targets.numel()
        
            def compute(self):
                return self.correct.float() / self.total 
            
        ```
        
        ```python
        model = torchkeras.KerasModel(net,
                                      loss_fn = nn.BCELoss(),
                                      optimizer= torch.optim.Adam(net.parameters(),lr = 0.05),
                                      metrics_dict = {"acc":Accuracy()}
                                     )
        
        from torchkeras.summary import summary
        
        summary(model,input_data=features);
        ```
        
        ### (3) train the model
        
        ```python
        dfhistory=model.fit(epochs=30, train_data=dl_train, 
                            val_data=dl_val, patience=3, 
                            monitor="val_acc",mode="max")
        ```
        
        ```python
        # visual the results
        fig, (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")
        ```
        
        ![](./data/training_result.png)
        
        
        ### (4) evaluate the model
        
        ```python
        %matplotlib inline
        %config InlineBackend.figure_format = 'svg'
        
        import matplotlib.pyplot as plt
        
        def plot_metric(dfhistory, metric):
            train_metrics = dfhistory["train_"+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()
        ```
        
        ```python
        plot_metric(dfhistory,"loss")
        ```
        
        ![](./data/loss_curve.png)
        
        ```python
        plot_metric(dfhistory,"acc")
        ```
        
        ![](./data/accuracy_curve.png)
        
        
        ```python
        model.evaluate(dl_val)
        ```
        
        ```
        {'val_loss': 0.13576620258390903, 'val_accuracy': 0.9441666702429453}
        ```
        
        
        ### (5) use the model
        
        ```python
        model.predict(dl_val)[0:10]
        ```
        
        ```
        tensor([[0.8767],
                [0.0154],
                [0.9976],
                [0.9990],
                [0.9984],
                [0.0071],
                [0.3529],
                [0.4061],
                [0.9938],
                [0.9997]])
        ```
        
        ```python
        for features,labels in dl_valid:
            with torch.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
        
        ```python
        # save the model parameters
        
        model_clone = torchkeras.KerasModel(Net(),loss_fn = nn.BCELoss(),
                     optimizer= torch.optim.Adam(model.parameters(),lr = 0.01),
                     metrics_dict={"acc":Accuracy()})
        model_clone.net.load_state_dict(torch.load("checkpoint.pt"))
        model_clone.evaluate(dl_val)
        ```
        
        ```
        {'val_loss': 0.17422042911251387, 'val_accuracy': 0.9358333299557368}
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
