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

Before Change

	inpts = {"X" : sample}
	
	// 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.
	if plot:

After Change

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):
				for j in range(conv_size):
					w[0, fltr1, i, j, 0, fltr2, i, j] = -100.0
					
print(w)

recurrent_conn = Connection(conv_layer,
					        conv_layer,

Instances