Error when checking model input: expected convolution2d_input_1 to have 4 dimensions, but got array with shape (32, 32, 3)

The input shape you have defined is the shape of a single sample. The model itself expects some array of samples as input (even if its an array of length 1). Your output really should be 4-d, with the 1st dimension to enumerate the samples. i.e. for a single image you should return a shape … Read more

What’s the difference between “hidden” and “output” in PyTorch LSTM?

I made a diagram. The names follow the PyTorch docs, although I renamed num_layers to w. output comprises all the hidden states in the last layer (“last” depth-wise, not time-wise). (h_n, c_n) comprises the hidden states after the last timestep, t = n, so you could potentially feed them into another LSTM. The batch dimension … Read more

how to format the image data for training/prediction when images are different in size?

You didn’t say what architecture you’re talking about. Since you said you want to classify images, I’m assuming it’s a partly convolutional, partly fully connected network like AlexNet, GoogLeNet, etc. In general, the answer to your question depends on the network type you are working with. If, for example, your network only contains convolutional units … Read more