Facial-Expression-Recognition | Facial-Expression-Recognition in TensorFlow | Computer Vision library

So, I am trying to make a emotion classifier using the 7 face expressions. I know that in order to use integer labels instead of 0 and 1 one needs to use the sparse_categorical_crossentropy and need to put the out layer activation as softmax but it isn't working out as expected.

I am using the data set from here https://www.kaggle.com/ashishpatel26/facial-expression-recognitionferchallenge

I'm confused about the in-feature of nn.linear. For out-feature of the model VGG-19's last nn.MaxPool2d, the tensor size is (512, 7, 7). The model below uses pooling function and resizes the tensor to (512, 49), then uses nn.linear(512, 7) directly. Why can't it work successfully without mismatch problem?

source

I was recently doing an image extraction part from a .csv file,the file contained a column named pixels with 48x48 values given as strings, so normally seeing a .csv file I used pandas.read_csv to try to convert pixels column to later on images, converting to PIL images.

I'm working on Kaggle's fer2013 dataset. Here's a link to the dataset.

I'm using TFLearn framework, I convert the Labels(7 class labels) to hot_shot and everything works fine until I run it in the networks and I get the error: Cannot feed value of shape (64, 7) for Tensor 'targets/Y:0', which has shape '(?,)'

I read previous similar questions and I understand that I'm trying to feed the network a tensor of shape which is different than what it expects, my problem here is I don't know how to reshape what it expects, or at least the shape of what it expects so I can reshape my tensor to.

Here's my code.