if x <= 0, output is 0. if x > 0, output is 1 The ReLU function is defined as: For x > 0 the output is x, i.e. f(x) = max(0,x) So for the derivative f ‘(x) it’s actually: if x < 0, output is 0. if x > 0, output is 1. The … Read more
In the graph, I’d suggest to move keep_prob = tf.placeholder(tf.float32) outside of the model function to make it global. with graph.as_default(): … x = tf.placeholder(“float”, [None, n_input]) y = tf.placeholder(“float”, [None, n_classes]) keep_prob = tf.placeholder(tf.float32) def model(x, weights_hiden, weights_out, biases_hidden, biases_out, keep_prob): # hidden layer with RELU activation layer_1 = tf.nn.relu(tf.add(tf.matmul(x, weights_hiden), biases_hidden)) # apply … Read more
Well, max-pooling and monotonely increasing non-linearities commute. This means that MaxPool(Relu(x)) = Relu(MaxPool(x)) for any input. So the result is the same in that case. So it is technically better to first subsample through max-pooling and then apply the non-linearity (if it is costly, such as the sigmoid). In practice it is often done the … Read more
https://archive.ics.uci.edu/ml is the University of California Irvine repository of machine learning datasets. It’s a really great resource, and I believe that they are all in CSV files.
This is kind of unusual, as commonly “Batch” is exactly defined as all operations of the network being invariant along that dimension. So you could, for example, just introduce another dimension. So you have the “former batch dimension” in which your operation is not invariant. For this keep your current implementation. Then, parallelize over the … Read more
The “forward pass” refers to calculation process, values of the output layers from the inputs data. It’s traversing through all neurons from first to last layer. A loss function is calculated from the output values. And then “backward pass” refers to process of counting changes in weights (de facto learning), using gradient descent algorithm (or … Read more
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
We explicitly need to call zero_grad() because, after loss.backward() (when gradients are computed), we need to use optimizer.step() to proceed gradient descent. More specifically, the gradients are not automatically zeroed because these two operations, loss.backward() and optimizer.step(), are separated, and optimizer.step() requires the just computed gradients. In addition, sometimes, we need to accumulate gradient among … Read more
Keras can predict multiple values at the same time, like if you input a vector of 100 elements, Keras can compute one prediction for each element, giving 100 outputs. This computation can also be done in batches, defined by the batch_size. This is just in case you cannot fit all the data in the CPU/GPU … Read more
The point of stating that a mathematical model is Turing Complete is to reveal the capability of the model to perform any calculation, given a sufficient amount of resources (i.e. infinite), not to show whether a specific implementation of a model does have those resources. Non-Turing complete models would not be able to handle a … Read more