[FIXED] Pytorch ;Optimizer got an empty parameter list

Issue

Am new in the deep learning concept using pytorch and am try to build a binary classifier model. I have tried some of the solution here on stack overflow but I can't seem to solve it. Maybe it is due to the nature of my code. Can someone figure out what could be the cause of this error in my code Here is my code

import torch
import torch.nn as nn
import numpy as np
from sklearn.datasets import make_blobs
import matplotlib.pyplot as pyp

# creating a dummy dataset from the make_blobs dataset
number_of_samples=5000
#divide the dataset into training(80%) and testing(20%)
training_number=int(number_of_samples*0.8)
#creating the dummy datasest
x,y=make_blobs(n_samples=number_of_samples,centers=2,n_features=64,cluster_std=10,random_state=2020)
y=y.reshape(-1,1)
#converting the numpy arrays into torch tensors
x,y=torch.from_numpy(x),torch.from_numpy(y)
x,y=x.float(),y.float()
#splitting the datasets into training and testing
x_train,x_test=x[:training_number],x[training_number:]
y_train,y_test=y[:training_number],y[training_number:]

#printing the shapes of each dataset
print("x_train shape:",x_train.shape)
print("x_test shape:",x_test.shape)
print("y_train shape:",y_train.shape)
print("y_test shape:",y_test.shape)

#a class to define the neural network us torch nn module
#neural network will have 3 hidden layers and 1 output layer
#hidden layers will have 64,256 and 1024 neurons
#output layer will have a single neuron
class neuralnetwork(nn.Module):
    def _init_(self):
        super().__init__()
        torch.manual_seed(2020)
        self.fc1 = nn.Linear(64, 256)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(256, 1024)
        self.relu2 = nn.ReLU()
        self.out = nn.Linear(1024, 1)
        self.final = nn.Sigmoid()
    
    def forward(self, x):
        op = self.fc1(x) 
        op = self.relu1(op)
        op = self.fc2(op)
        op = self.relu2(op)
        op = self.out(op)
        y = self.final(op)
        return y

#defining the loss,optimizer and training function for the neural network
def train_network(model,optimizer,loss_function,num_epochs,batch_size,x_train,y_train):
    #start model training
    model.train()
    loss_for_every_epoch=nn.ModuleList()
    for epoch in range(num_epochs):
        train_loss=0.0
        for i in range(0,x_train.shape[0],batch_size):
            #extract train batch from x and y
            input_data=x_train[i:min(x_train.shape[0]),i+batch_size]
            labels=y_train[i:min(y_train.shape[0]),i+batch_size]
            #set gradients to zero before beginning optimization
            optimizer.zero_grad()
            #forwad pass 
            output_data=model(input_data)
            #calculate loss
            loss=loss_function(output_data,labels)
            #backpropagate
            loss.backward()
            #update weights
            optimizer.step()
            train_loss+=loss.item()*batch_size
        print("Epoch: {} - Loss:{:.4f}".format(epoch+1,train_loss )) 
        loss_for_every_epoch.extend([train_loss])
    #predict
    y_test_prediction=model(x_test)
    a=np.where(y_test_prediction>0.5,1,0)
    return loss_for_every_epoch

#create an object of the class
model=neuralnetwork()
#define the loss function
loss_function = nn.BCELoss()#binary cross entropy loss function
#define optimizer
adam_optimizer=torch.optim.Adam(params=model.parameters(),lr=0.001)
#define epochs and batch size
number_of_epochs=100
batch_size=16
#Calling the function for training and pass model, optimizer, loss and related paramters
adam_loss=train_network(model,adam_optimizer,loss_function,number_of_epochs,batch_size,x_train,y_train)

I get the error

Value error:optimizer got an empty parameter list

The error is mainly generated from this section of code

 #create an object of the class
    model=neuralnetwork()
    #define the loss function
    loss_function = nn.BCELoss()#binary cross entropy loss function
    #define optimizer
    adam_optimizer=torch.optim.Adam(params=model.parameters(),lr=0.001)
    #define epochs and batch size
    number_of_epochs=100
    batch_size=16
    #Calling the function for training and pass model, optimizer, loss and related paramters
    adam_loss=train_network(model,adam_optimizer,loss_function,number_of_epochs,batch_size,x_train,y_train)

What could be the cause in my code. Here is the full stack trade

    Traceback (most recent call last)
g:\My Drive\CODE\pythondatascience\simpleneuralnetwork.ipynb Cell 7' in <cell line: 6>()
      4 loss_function = nn.BCELoss()#binary cross entropy loss function
      5 #define optimizer
----> 6 adam_optimizer=torch.optim.Adam(params=model.parameters(),lr=0.001)
      7 #define epochs and batch size
      8 number_of_epochs=100

File c:\Users\DAVE\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\optim\adam.py:81, in Adam.__init__(self, params, lr, betas, eps, weight_decay, amsgrad, maximize)
     78     raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
     79 defaults = dict(lr=lr, betas=betas, eps=eps,
     80                 weight_decay=weight_decay, amsgrad=amsgrad, maximize=maximize)
---> 81 super(Adam, self).__init__(params, defaults)

File c:\Users\DAVE\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\optim\optimizer.py:49, in Optimizer.__init__(self, params, defaults)
     47 param_groups = list(params)
     48 if len(param_groups) == 0:
---> 49     raise ValueError("optimizer got an empty parameter list")
     50 if not isinstance(param_groups[0], dict):
     51     param_groups = [{'params': param_groups}]

ValueError: optimizer got an empty parameter list

Solution

It should be def __init__, not def _init_ in neuralnetwork class. You are not initializing your model object at all. Thus, it does not have any parameters.

Answered By - The Exile