Hi People,
I am using the following code to create a simple neural network but it gives me the following error: mat1 and mat2 shapes cannot be multiplied (784x10 and 784x10)
Not sure why this is happening as matrix sizes are same i.e. 784*10
#import libraries
%matplotlib inline
import torch
import torchvision
import torchvision.transforms as transforms
#load data
transform = transforms.Compose(
[transforms.ToTensor(),

 transforms.Normalize((0.5), (0.5))])

batch_size = 10
trainset = torchvision.datasets.MNIST(root=’./data’, train=True,
                                    download=True, transform=transform)

trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                      shuffle=True, num_workers=2)

testset = torchvision.datasets.MNIST(root=’./data’, train=False,
                                   download=True, transform=transform)

testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                     shuffle=False, num_workers=2)

#wrap iterator around the batch
dataiter = iter(trainloader)
x_train, y_train = dataiter.next()
dataiter = iter(testloader)
x_valid, y_valid = dataiter.next()
#initialize weights and bias
import math
weights = torch.randn(784, 10) / math.sqrt(784)
weights.requires_grad_()
bias = torch.zeros(10, requires_grad=True)
#Define loss function
def log_softmax(x):
return x - x.exp().sum(-1).log().unsqueeze(-1)

def model(xb):
return log_softmax(xb @ weights + bias)

#create prediction
bs = 64  # batch size
xb = x_train[0:bs]  # a mini-batch from x
xb=xb.reshape(784,10)
preds = model(xb)  # predictions
preds[0], preds.shape
print(preds[0], preds.shape)