Im getting this error when passing the input data to the Linear (Fully Connected Layer) in PyTorch: I fully understand the problem since the input data has a shape (N,C,H,W) (from a Convolutional+MaxPool layer) where: N: Data Samples C: Channels of the data H,W: Height and Width Nevertheless I was expecting PyTorch to do the “reshaping” of the data form:
Tag: deep-learning
How do you compute accuracy in a regression model, after rounding predictions to classes, in keras?
How would you create and display an accuracy metric in keras for a regression problem, for example after you round the predictions to the nearest integer class? While accuracy is not itself effectively defined conventionally for a regression problem, to determine ordinal classes/labels for data, it is suitable to treat the problem as a regression. But then it would be
TensorFlow ‘module’ object has no attribute ‘global_variables_initializer’
I’m new to Tensorflow I’m running a Deep learning Assignment from Udacity on iPython notebook. link And it has an error. Please help! How can I fix this? Thank you. Answer In older versions, it was called tf.initialize_all_variables.
What is the difference between ‘SAME’ and ‘VALID’ padding in tf.nn.max_pool of tensorflow?
What is the difference between ‘SAME’ and ‘VALID’ padding in tf.nn.max_pool of tensorflow? In my opinion, ‘VALID’ means there will be no zero padding outside the edges when we do max pool. According to A guide to convolution arithmetic for deep learning, it says that there will be no padding in pool operator, i.e. just use ‘VALID’ of tensorflow. But
tflearn / tensorflow does not learn xor
Following code was written to learn the XOR function, but about half of the time the network does not learn and the loss after each epoch stays the same. Sometimes I get correct results like this: But often this: My 2x2x1 network should be able to perform XOR, and there is even some evidence that suggests that this network should