RuntimeError: mat1 dim 1 must match mat2 dim 0
I am still grappling with PyTorch, having played with Keras for a while (which feels a lot more intuitive).
Anyway - I have the nn.linear model code below, which works fine for just one input feature, where:
inputDim = 1  

I am now trying to expand the same code to include 2 features, and so I have included another column in my feature dataframe and also set:
inputDim = 2  

However, when I run the code, I get the dreaded error:
RuntimeError: mat1 dim 1 must match mat2 dim 0

This error references line 63, which is:
    outputs = model(inputs)

I have gone through several other posts here relating to this dimensionality error, but I still can't see what is wrong with my code. Any help would be appreciated.
The full code looks like this:
import numpy as np
import pandas as pd
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt


device = 'cuda' if torch.cuda.is_available() else 'cpu'

df = pd.read_csv('Adjusted Close - BAC-UBS-WFC.csv')
x = df[['BAC', 'UBS']]
y = df['WFC']

# number_of_features = x.shape[1]
# print(number_of_features)


x_train = np.array(x, dtype=np.float32)
x_train = x_train.reshape(-1, 1)

y_train = np.array(y, dtype=np.float32)
y_train = y_train.reshape(-1, 1)


class linearRegression(torch.nn.Module):
    def __init__(self, inputSize, outputSize):
        super(linearRegression, self).__init__()
        self.linear = torch.nn.Linear(inputSize, outputSize)

    def forward(self, x):
        out = self.linear(x)
        return out


inputDim = 2  
outputDim = 1  
learningRate = 0.01
epochs = 500

# Model instantiation
torch.manual_seed(42)
model = linearRegression(inputDim, outputDim)
if torch.cuda.is_available(): model.cuda()

criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learningRate)

# Model training
loss_series = []
for epoch in range(epochs):
    # Converting inputs and labels to Variable
    inputs = Variable(torch.from_numpy(x_train).cuda())
    labels = Variable(torch.from_numpy(y_train).cuda())

    # Clear gradient buffers because we don't want any gradient from previous epoch to carry forward, dont want to cummulate gradients
    optimizer.zero_grad()

    # get output from the model, given the inputs
    outputs = model(inputs)

    # get loss for the predicted output
    loss = criterion(outputs, labels)
    loss_series.append(loss.item())
    print(loss)
    # get gradients w.r.t to parameters
    loss.backward()

    # update parameters
    optimizer.step()

    print('epoch {}, loss {}'.format(epoch, loss.item()))

# Calculate predictions on training data
with torch.no_grad():  # we don't need gradients in the testing phase
    predicted = model(Variable(torch.from_numpy(x_train).cuda())).cpu().data.numpy()




General advice: For errors with dimension, it usually helps to print out dimensions at each step of the computation.
Most likely in this specific case, you have made mistake in reshaping the input with this x_train = x_train.reshape(-1, 1)
Your input is (N,1) but NN expects (N,2).
