## Deep learning numerical regression, no images, custom loss function

I want to define a neural network or deep learning. Firstly, I have [500 * 4] data with a sample size of 500, each with 4 features (x1, x2, x3, x4).

The output variables are y1 and y2 ([500 * 2]), but I don’t have any output data, I only have their range of values (such as y1 in range (0-1)).

I have the variable z, which is the measured data, z=5 * e ^ (y1)+7 * sin (y2)

The loss function will be defined as : z(measure) – z (y1, y2)

The purpose of this neural network is to estimate y1 and y2 based on x1, x2, x3, x4.

For instance:

I know information about 500 cats, which are: x1 (height), x2 (weight), x3 (food intake), x4 (excretion).

I also know the age of these 500 cats: z

Now, I want to estimate y1 (cancer probability) and y2 (hair loss). The range of y1 is 0-1, and the range of y2 is -10 to 10

Do you know how to establish such deep learning or neural networks? Is there a simple example?I want to define a neural network or deep learning. Firstly, I have [500 * 4] data with a sample size of 500, each with 4 features (x1, x2, x3, x4).

The output variables are y1 and y2 ([500 * 2]), but I don’t have any output data, I only have their range of values (such as y1 in range (0-1)).

I have the variable z, which is the measured data, z=5 * e ^ (y1)+7 * sin (y2)

The loss function will be defined as : z(measure) – z (y1, y2)

The purpose of this neural network is to estimate y1 and y2 based on x1, x2, x3, x4.

For instance:

I know information about 500 cats, which are: x1 (height), x2 (weight), x3 (food intake), x4 (excretion).

I also know the age of these 500 cats: z

Now, I want to estimate y1 (cancer probability) and y2 (hair loss). The range of y1 is 0-1, and the range of y2 is -10 to 10

Do you know how to establish such deep learning or neural networks? Is there a simple example? I want to define a neural network or deep learning. Firstly, I have [500 * 4] data with a sample size of 500, each with 4 features (x1, x2, x3, x4).

The output variables are y1 and y2 ([500 * 2]), but I don’t have any output data, I only have their range of values (such as y1 in range (0-1)).

I have the variable z, which is the measured data, z=5 * e ^ (y1)+7 * sin (y2)

The loss function will be defined as : z(measure) – z (y1, y2)

The purpose of this neural network is to estimate y1 and y2 based on x1, x2, x3, x4.

For instance:

I know information about 500 cats, which are: x1 (height), x2 (weight), x3 (food intake), x4 (excretion).

I also know the age of these 500 cats: z

Now, I want to estimate y1 (cancer probability) and y2 (hair loss). The range of y1 is 0-1, and the range of y2 is -10 to 10

Do you know how to establish such deep learning or neural networks? Is there a simple example? deep learning, loss function, regression MATLAB Answers — New Questions