validationLoader = torch.utils.data.DataLoader(ValidationData, batch_size=250, shuffle=True, num_workers=8)
for batch in validationLoader:
batchLabel = batch[:,0]
batchData = batch[:,1:].reshape(250,1,28,28).float()
y_pred = model.forward(batchData)
loss = loss_fn(y_pred, batchLabel)
model.CurrentValidationLoss.append(float(loss.item()))
model.OverallValidationLoss.append(np.mean(np.array(model.CurrentValidationLoss)))
model.EvaluateNextEpoch()
print(np.mean(np.array(model.CurrentValidationLoss)))
if Testing:
resultlist = []
testLoader = torch.utils.data.DataLoader(TestingData, batch_size=1, num_workers=8)
for batch in testLoader:
batchLabel = batch[:,0]
batchData = batch[:,1:].reshape(1,1,28,28).float()
y_pred = model.predict(batchData)
resultlist.append(y_pred == batchLabel[0])
resultlist = np.array(resultlist)
total = len(resultlist)
tp = len(resultlist[resultlist == True])
fp = len(resultlist[resultlist == False])
print("Total test samples: " + str(total))
print("Correctly Predicted Samples: " + str(tp))
print("Falsely Predicted Samples: " + str(fp))
print("Overall Accuracy: " + str(tp/total * 100) + "%")
print("Overall Error: " + str(fp/total * 100) + "%")
