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