@mstokes42 If your confusion matrix is representative of your test/train set, it looks like you're using very few (~ 100) training examples with what appears to be LeNet-5. This is about 2 orders of magnitude too little data; in [Lecun98](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf) LeNet-5 was trained using MNIST (60k 32x32 training images).
The reason you're getting 100% accuracy on your train set and terrible accuracy on your test set is that LeNet-5 is memorizing your training data and not learning enough to generalize to your test set. Take a look at the Convolutional Neural networks section for some examples with training sets: https://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-scratch.html E.g. ``` batch_size = 64 num_inputs = 784 num_outputs = 10 def transform(data, label): return nd.transpose(data.astype(np.float32), (2,0,1))/255, label.astype(np.float32) train_data = gluon.data.DataLoader(gluon.data.vision.MNIST(train=True, transform=transform), batch_size, shuffle=True) test_data = gluon.data.DataLoader(gluon.data.vision.MNIST(train=False, transform=transform), batch_size, shuffle=False) ``` Additionally, if this is the issue, please feel free to close the issue. Thanks! [ Full content available at: https://github.com/apache/incubator-mxnet/issues/8601 ] This message was relayed via gitbox.apache.org for [email protected]
