25fbc56b910b394c02f0c2673630f602899ddc56,examples/mnist/conv_mnist.py,,,#,11

Before Change

	
	// Run the network on the input.
	choice = np.random.choice(per_class, size=10, replace=False)
	clamp = {"Y" : per_class * labels[i].long() + torch.Tensor(choice).long()}
	network.run(inpts=inpts, time=time)
	
	// Optionally plot various simulation information.

After Change

// 							      conv_layer,
// 							      w=w)

w = torch.zeros(1, n_filters, conv_size, conv_size, 1, n_filters, conv_size, conv_size)
for fltr1 in range(n_filters):
	for fltr2 in range(n_filters):
		if fltr1 != fltr2:
			for i in range(conv_size):

Instances