torchkeras 3.0.1

pytorch❤️ keras

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!

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

%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"]);

# 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)

for features,labels in dl_train:
    break
print(features.shape)
print(labels.shape)

(2) create the model

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 

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

dfhistory=model.fit(epochs=30, train_data=dl_train, 
                    val_data=dl_val, patience=3, 
                    monitor="val_acc",mode="max")

# 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")

(4) evaluate the model

%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()

plot_metric(dfhistory,"loss")

plot_metric(dfhistory,"acc")

model.evaluate(dl_val)

{'val_loss': 0.13576620258390903, 'val_accuracy': 0.9441666702429453}

(5) use the model

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]])

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

# 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}