Hi Mangalam I am facing similar issue as you faced of shape mismatch when using my own data and changing the dimensions according to my data set. Could you please help me if you found the solution for this
On Monday, 23 March 2015 20:00:15 UTC-4, Mangalam Sankupellay wrote: > > Thanks Pascal. Will make the suggested changes & see how it goes. > > On Tuesday, 24 March 2015 06:00:42 UTC+10, Pascal Lamblin wrote: >> >> On Sun, Mar 22, 2015, Mangalam Sankupellay wrote: >> > I'm getting the following error when I run it with my own data >> > (file.pkl.gz). However, there's no error when I run mlp.py with my own >> > data. I'm unsure what are my mistakes. >> >> Here is the important part of the error message: >> >> > ValueError: the number of rows in the image (284) at run time is >> different >> > than at build time (12) for the ConvOp. >> >> It means that you have an input shape of 284 on some dimension, but the >> convolution expect it to be 12. From your code, it seems it happens on >> layer1: >> >> > # Construct the second convolutional pooling layer >> > # filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8) >> > # maxpooling reduces this further to (8/2, 8/2) = (4, 4) >> > # 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4) >> > layer1 = LeNetConvPoolLayer( >> > rng, >> > input=layer0.output, >> > image_shape=(batch_size, nkerns[0], 12, 12), >> > filter_shape=(nkerns[1], nkerns[0], 5, 5), >> > poolsize=(2, 2) >> > ) >> >> You have different options options: >> - not specify the input shape of the convolution of layer1 at all >> (pass None, or do not mention it) >> - specify only the shapes you are sure about, for instance (batch_size, >> nkerns[0], None, None) >> - pass the appropriate shapes. >> >> You will also have to fix the input shape of the fully-connected layer: >> >> > # the HiddenLayer being fully-connected, it operates on 2D matrices >> of >> > # shape (batch_size, num_pixels) (i.e matrix of rasterized images). >> > # This will generate a matrix of shape (batch_size, nkerns[1] * 4 * >> 4), >> > # or (500, 50 * 4 * 4) = (500, 800) with the default values. >> > layer2_input = layer1.output.flatten(2) >> > >> > # construct a fully-connected sigmoidal layer >> > layer2 = HiddenLayer( >> > rng, >> > input=layer2_input, >> > n_in=nkerns[1] * 4 * 4, >> > n_out=4, >> > activation=T.tanh >> > ) >> >> Hope this helps, >> -- >> Pascal >> > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
