What is the purpose of the add_loss function in Keras?

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

caffe data layer example step by step

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

How to calculate the number of parameters for convolutional neural network?

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

What’s the difference between sparse_softmax_cross_entropy_with_logits and softmax_cross_entropy_with_logits?

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

Keras input explanation: input_shape, units, batch_size, dim, etc

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