"Load in data"
mnist_data = np.load('../simple_regularization/data/mnist.npy')[()]
trainData, trainLabels = mnist_data['train'] # training set
testData, testLabels = mnist_data['test']
totData = torch.cat((trainData, testData), 0)
totLabels = torch.cat((trainLabels, testLabels))
# In[]
"Compute the eigenspectra of single layer mlp"
singleMlpSpectra = []
for activation in range(len(singleMlpModels)):
activationSpectra = []
for arch in range(len(singleMlpModels[activation])):
realizationSpectra = []
for model in singleMlpModels[activation][arch]:
hidden, _ = model.bothOutputs(totData)
tempSpectra = []
for h in hidden:
h = h.detach().numpy()
h = h - np.mean(h, 0)
cov = h.T @ h / h[:, 0].size
cov = (cov + cov.T) / 2
temp, _ = np.linalg.eigh(cov)
tempSpectra.append(copy.deepcopy(temp[::-1]))
realizationSpectra.append(copy.deepcopy(tempSpectra[::-1]))
activationSpectra.append(copy.deepcopy(realizationSpectra))
singleMlpSpectra.append(copy.deepcopy(activationSpectra))
# In[]
"Compute the eigenspectra of double layer mlp"
doubleMlpSpectra = []
