You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hello, everyone. I would like to ask if I can create neural network models with multiple inputs and mixed data inputs by DEEPXDE.
the next is a demo of deepxde (Inverse problem for the Poisson equation with unknown forcing field):
""""
Next, we choose the networks. We use two networks, one to train for u(x) and the other to train for q(x). Here, we use two fully connected neural networks of depth 4 (i.e., 3 hidden layers) and width 20.
Hello, everyone. I would like to ask if I can create neural network models with multiple inputs and mixed data inputs by DEEPXDE.
the next is a demo of deepxde (Inverse problem for the Poisson equation with unknown forcing field):
""""
Next, we choose the networks. We use two networks, one to train for u(x) and the other to train for q(x). Here, we use two fully connected neural networks of depth 4 (i.e., 3 hidden layers) and width 20.
net = dde.nn.PFNN([1, [20, 20], [20, 20], [20, 20], 2], "tanh", "Glorot uniform")
""""
like the above demo, can i realize my idea by seting 1 to 2 ?
The text was updated successfully, but these errors were encountered: