@ptrblck why the results of softmax are in two columns?
like that :
softmax de test [array([0.35895655, 0.6410435 ], dtype=float32), array([0.5, 0.5], dtype=float32), array([0.38063172, 0.61936826], dtype=float32), array([0.30245292, 0.6975471 ], dtype=float32), array([0.00381802, 0.99618196], dtype=float32),…, array([0.5064734 , 0.49352652], dtype=float32).

The softmax operation normalizes the specified dim so that its sum equals to 1. and the results can be interpreted as a probability.
In your case the two values correspond to the probability for class0 and class1 and their sum is 1.
Take a look at the Softmax Wikipedia article (or any other resource) for more information.

I think visualizing tensors and arrays was already discussed in this thread.

I don’t know what shape the tensor in the current screenshot has, but as already described you will be able to visualize tensors using plt.imshow as long as they have a valid image shape.
I’m also unsure why the values are again negative, but assume you are not using the softmax operation anymore.

@ptrblck the values that I transmitted to you, are just the results of the convolution layers.
I want to substruct the results of the convolutional layers of x and x1.

out = self.cnn1(x)
out = self.batchnorm1(out)
out = self.relu(out)
out = self.maxpool1(out)
out = self.cnn2(out)
out = self.batchnorm2(out)
out = self.relu(out)
out = self.maxpool2(out)
out = self.cnn3(out)
out = self.batchnorm3(out)
out = self.relu(out)
out = self.cnn4(out)
out = self.batchnorm4(out)
out = self.relu(out)
out = self.cnn5(out)
out = self.batchnorm5(out)
out = self.relu(out)
out1 = self.cnn1(x2)
out1 = self.batchnorm1(out1)
out1 = self.relu(out1)
out1 = self.maxpool1(out1)
out1 = self.cnn2(out1)
out1 = self.batchnorm2(out1)
out1 = self.relu(out1)
out1 = self.maxpool2(out1)
out1 = self.cnn3(out1)
out1 = self.batchnorm3(out1)
out1 = self.relu(out1)
out1 = self.cnn4(out1)
out1 = self.batchnorm4(out1)
out1 = self.relu(out1)
out1 = self.cnn5(out1)
out1 = self.batchnorm5(out1)
out1 = self.relu(out1)
out2 = out - out1
print(out2)