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import torch
def target_transform(labels):
count_labels = torch.zeros(60)
for shape_index in range(len(labels)):
count_labels[int(shape_index * 10 + labels[shape_index])] = 1
return count_labels
def loss_function(output, target):
a = torch.tensor(range(10)).repeat(6 * target.shape[0]).reshape(target.shape)
errors = a - target
return torch.sum(output * errors ** 2) / output.shape[0]
def count_correct(output, target):
output = output.reshape(output.shape[0], 6, 10)
target = target.reshape(target.shape[0], 6, 10)
target = torch.argmax(target, dim=2)
predictions = torch.argmax(output, dim=2)
correct = torch.min(predictions.reshape(target.shape) == target, dim=1).values
return torch.sum(correct).tolist()
def finalizer(x):
x = x.reshape(x.shape[0], 6, 10)
x = torch.softmax(x, 1)
return x.reshape(x.shape[0], 60)
outputs = 60
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