I am trying to visualise my NN architecture. I use this code : PINNs/Burgers Inference (PyTorch).ipynb at master · jayroxis/PINNs (github.com) which is available on Github.
This is my NN:
class DNN(torch.nn.Module):
def __init__(self, layers):
super(DNN, self).__init__()
# parameters
self.depth = len(layers) - 1
# set up layer order dict
self.activation = torch.nn.Tanh
layer_list = list()
for i in range(self.depth - 1):
layer_list.append(
('layer_%d' % i, torch.nn.Linear(layers[i], layers[i+1]))
)
layer_list.append(('activation_%d' % i, self.activation()))
layer_list.append(
('layer_%d' % (self.depth - 1), torch.nn.Linear(layers[-2], layers[-1]))
)
layerDict = OrderedDict(layer_list)
# deploy layers
self.layers = torch.nn.Sequential(layerDict)
def forward(self, x):
out = self.layers(x)
return out
How can I visualise it, best as a PNG file? I would like to see the layers, neurons, weights biases etc.
Thanks in advance