How can we make a neural network to predict a continuous variable which has values?

To predict a continuous value, you need to adjust your model (regardless whether it is Recurrent or Not) to the following conditions:

1. Use a linear activation function for the final layer.
2. Chose an appropriate cost function (square error loss is typically used to measure the error of predicting real values)

Can neural network predict continuous value?

MNIST data When you prepare your own dataset, you can train a neural network that estimates continuous values by preparing a dataset that provides a correct value y for image x.

Can neural networks be used for linear regression?

We can think of linear regression models as neural networks consisting of just a single artificial neuron, or as single-layer neural networks. Since for linear regression, every input is connected to every output (in this case there is only one output), we can regard this transformation (the output layer in Fig. 3.1.

How do you predict a continuous variable?

Regression Analysis. Regression analysis is used to predict a continuous target variable from one or multiple independent variables. Typically, regression analysis is used with naturally-occurring variables, rather than variables that have been manipulated through experimentation.

How does neural network predict?

Neural networks can be used to make predictions on time series data such as weather data. A neural network can be designed to detect pattern in input data and produce an output free of noise. The output layer collects the predictions made in the hidden layer and produces the final result: the model’s prediction.

Which of these are used to predict continuous value variables?

1) Linear Regression Linear regression algorithm is used if the labels are continuous, like the number of flights daily from an airport, etc. The representation of linear regression is y = b*x + c. In the above representation, ‘y’ is the independent variable, whereas ‘x’ is the dependent variable.