Hi there
I have trained a conv NN, for 1D (signal) data. my data size is (65535,1,94) which 65535 is number of my samples, 1 is input channel and 94 is length of the signal.
Now I am going to use my trained model for another data set that has the same dimension.
I tried to run this but it did not work:
out_put = torch.zeros(65535,1,94)
for i in range (65535):
out_out[i,1,94]  = model (in_put [i,1,94])
below u can also see my network
Autoencoder(
(encoder): Sequential(
(0): Conv1d(1, 5, kernel_size=(5,), stride=(2,))
(1): MaxPool1d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)
(2): ReLU(inplace=True)
(3): Conv1d(5, 10, kernel_size=(5,), stride=(2,))
(4): MaxPool1d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)
(5): ReLU(inplace=True)
(6): Conv1d(10, 15, kernel_size=(5,), stride=(2,))
(7): MaxPool1d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)
(8): ReLU(inplace=True)
(9): Conv1d(15, 20, kernel_size=(4,), stride=(1,))
(10): ReLU(inplace=True)
)
(decoder): Sequential(
(0): ConvTranspose1d(20, 15, kernel_size=(1,), stride=(4,))
(1): ReLU(inplace=True)
(2): ConvTranspose1d(15, 10, kernel_size=(2,), stride=(4,))
(3): ReLU(inplace=True)
(4): ConvTranspose1d(10, 5, kernel_size=(9,), stride=(2,))
(5): ReLU(inplace=True)
(6): ConvTranspose1d(5, 1, kernel_size=(10,), stride=(2,))
(7): ReLU(inplace=True)
)
)
is anyone has any idea about this ?
thank you