def getNetwork(args,inputs,outputs): if (args.net_type == 'lenet'): net = BBBLeNet(outputs,inputs) # inputs is number of input channels file_name = 'lenet' elif (args.net_type == 'alexnet'): net = BBBAlexNet(outputs,inputs) file_name = 'alexnet-' elif (args.net_type == '3conv3fc'): net = BBB3Conv3FC(outputs,inputs) file_name = '3Conv3FC-' else: print('Error : Network should be either [LeNet / AlexNet /SqueezeNet/ 3Conv3FC') sys.exit(0) return net, file_name
for y in range(height): val = round(img[x][y], 2) if img[x][y] != 0 else 0 ax.annotate(str(val), xy=(y, x), horizontalalignment='center', verticalalignment='center', size=8, color='white' if img[x][y] < thresh else 'black') # In[ ]: # Architecture if (net_type == 'lenet'): net = BBBLeNet(outputs, inputs) elif (net_type == 'alexnet'): net = BBBAlexNet(outputs, inputs) elif (net_type == '3conv3fc'): net = BBB3Conv3FC(outputs, inputs) else: print('Error : Network should be either [LeNet / AlexNet / 3Conv3FC') # In[ ]: if use_cuda: net.cuda() # In[ ]: vi = GaussianVariationalInference(torch.nn.CrossEntropyLoss()) optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=weight_decay)