I’ll try to answer the original question of why model.add_loss() is being used instead of specifying a custom loss function to model.compile(loss=…). All loss functions in Keras always take two parameters y_true and y_pred. Have a look at the definition of the various standard loss functions available in Keras, they all have these two parameters. … Read more
You can use a “Python” layer: a layer implemented in python to feed data into your net. (See an example for adding a type: “Python” layer here). import sys, os sys.path.insert(0, os.environ[‘CAFFE_ROOT’]+’/python’) import caffe class myInputLayer(caffe.Layer): def setup(self,bottom,top): # read parameters from `self.param_str` … def reshape(self,bottom,top): # no “bottom”s for input layer if len(bottom)>0: raise … Read more
I know Jeff Donahue worked on LSTM models using Caffe. He also gave a nice tutorial during CVPR 2015. He has a pull-request with RNN and LSTM. Update: there is a new PR by Jeff Donahue including RNN and LSTM. This PR was merged on June 2016 to master.
Let’s first look at how the number of learnable parameters is calculated for each individual type of layer you have, and then calculate the number of parameters in your example. Input layer: All the input layer does is read the input image, so there are no parameters you could learn here. Convolutional layers: Consider a … Read more
Ok I think this is about the simplest way to explain it all. Your example is 1 image, size 2×2, with 1 channel. You have 1 filter, with size 1×1, and 1 channel (size is height x width x channels x number of filters). For this simple case the resulting 2×2, 1 channel image (size … Read more
Having two different functions is a convenience, as they produce the same result. The difference is simple: For sparse_softmax_cross_entropy_with_logits, labels must have the shape [batch_size] and the dtype int32 or int64. Each label is an int in range [0, num_classes-1]. For softmax_cross_entropy_with_logits, labels must have the shape [batch_size, num_classes] and dtype float32 or float64. Labels … Read more
When using the image dataset input layer (with either lmdb or leveldb backend) caffe only supports one integer label per input image. If you want to do regression, and use floating point labels, you should try and use the HDF5 data layer. See for example this question. In python you can use h5py package to … Read more
Units: The amount of “neurons”, or “cells”, or whatever the layer has inside it. It’s a property of each layer, and yes, it’s related to the output shape (as we will see later). In your picture, except for the input layer, which is conceptually different from other layers, you have: Hidden layer 1: 4 units … Read more
I decided to just implement a float head with an L1Loss in Pytorch and I created a simple but effective synthetic data set to test the implementation. The data set created images into which a number of small squares were randomly drawn. The training label was simply the number of squares divided by 10, a … Read more