[Python] Pytorch Convolution Network: problem with floating point type

I'm trying to train the CV-model with standard MNIST-data:

import torch
from torchvision.datasets import MNIST
import torchvision.transforms as transforms

img_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1305,), (0.3081,))
])

train_dataset = MNIST(root='../mnist_data/',
train=True,
download=True,
transform=img_transforms)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=10,
shuffle=True)


Model is declared as:

import torch.nn as nn

class MNIST_ConvNet(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = ConvLayer(1, 14, 5, activation=nn.Tanh(),
dropout=0.8)
self.conv2 = ConvLayer(14, 7, 5, activation=nn.Tanh(), flatten=True,
dropout=0.8)
self.dense1 = DenseLayer(28 * 28 * 7, 32, activation=nn.Tanh(),
dropout=0.8)
self.dense2 = DenseLayer(32, 10)

def forward(self, x: Tensor) -> Tensor:
assert_dim(x, 4)

x = self.conv1(x)
x = self.conv2(x)

x = self.dense1(x)
x = self.dense2(x)
return x


Then I invoke forward and estimate loss for this model, in accordance with pytorch approach:

import torch.optim as optim

model = MNIST_ConvNet()
for X_batch, y_batch in train_dataloader:

optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
optimizer.zero_grad()
output = model(X_batch)[0]
loss = nn.CrossEntropyLoss()
loss = loss(output, y_batch)


X_batch has the following content:

tensor([[[[-0.4236, -0.4236, -0.4236, ..., -0.4236, -0.4236, -0.4236],
[-0.4236, -0.4236, -0.4236, ..., -0.4236, -0.4236, -0.4236],
[-0.4236, -0.4236, -0.4236, ..., -0.4236, -0.4236, -0.4236],
...,


And for this line of code "self.loss(output, y_batch)", I receive the following error:

RuntimeError: Expected floating point type for target with class probabilities, got Long
​

To solve the problem, I tried update data type:

self.model(X_batch.type(torch.FloatTensor))[0]


But this does not working.

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