A training step is one gradient update. In one step batch_size examples are processed. An epoch consists of one full cycle through the training data. This is usually many steps. As an example, if you have 2,000 images and use a batch size of 10 an epoch consists of: 2,000 images / (10 images / … Read more
If you read the Keras documentation entry for Dense, you will see that this call: Dense(16, input_shape=(5,3)) would result in a Dense network with 3 inputs and 16 outputs which would be applied independently for each of 5 steps. So, if D(x) transforms 3 dimensional vector to 16-d vector, what you’ll get as output from … Read more
It’s explained well here. If the input variables are combined linearly, as in an MLP [multilayer perceptron], then it is rarely strictly necessary to standardize the inputs, at least in theory. The reason is that any rescaling of an input vector can be effectively undone by changing the corresponding weights and biases, leaving you with … Read more
I want to explain with picture from C3D. In a nutshell, convolutional direction & output shape is important! ↑↑↑↑↑ 1D Convolutions – Basic ↑↑↑↑↑ just 1-direction (time-axis) to calculate conv input = [W], filter = [k], output = [W] ex) input = [1,1,1,1,1], filter = [0.25,0.5,0.25], output = [1,1,1,1,1] output-shape is 1D array example) graph … Read more
In general, in classification you have a set of predefined classes and want to know which class a new object belongs to. Clustering tries to group a set of objects and find whether there is some relationship between the objects. In the context of machine learning, classification is supervised learning and clustering is unsupervised learning. … Read more
The reason for this apparent performance discrepancy between categorical & binary cross entropy is what user xtof54 has already reported in his answer below, i.e.: the accuracy computed with the Keras method evaluate is just plain wrong when using binary_crossentropy with more than 2 labels I would like to elaborate more on this, demonstrate the … Read more
First of all, you need to identify your problem. It depends upon what kind of data you have and what your desired task is. If you are Predicting Category : You have Labeled Data You need to follow Classification Approach and its algorithms You don’t have Labeled Data You need to go for Clustering Approach … Read more
There are two competing concerns: with less training data, your parameter estimates have greater variance. With less testing data, your performance statistic will have greater variance. Broadly speaking you should be concerned with dividing data such that neither variance is too high, which is more to do with the absolute number of instances in each … Read more
The lower the loss, the better a model (unless the model has over-fitted to the training data). The loss is calculated on training and validation and its interperation is how well the model is doing for these two sets. Unlike accuracy, loss is not a percentage. It is a summation of the errors made for … Read more
Linear regression output as probabilities It’s tempting to use the linear regression output as probabilities but it’s a mistake because the output can be negative, and greater than 1 whereas probability can not. As regression might actually produce probabilities that could be less than 0, or even bigger than 1, logistic regression was introduced. Source: … Read more