Train SqueezeNet model using MNIST dataset Pytorch
The initialization of the pretrained weights is possible but you'll get trouble with the strides and kernel sizes since MNIST images are 28X28 pixels. Most probably the reduction will lead to  (batch_sizex1x1xchannel) feature maps before the net is at its infernece layer which will then cause an error.
I want to train SqueezeNet 1.1 model using MNIST dataset instead of ImageNet dataset. 
Can i have the same model as torchvision.models.squeezenet? 
Thanks!