Here is the help you requested. There are many other methods like random forest etc which is far more reliable and accurate. We can work together to publish.

To extract mathematical equations from an Artificial Neural Network (ANN), you can use a technique called sensitivity analysis. Sensitivity analysis involves calculating the sensitivity of the output of the network with respect to changes in the input. By calculating the sensitivity of the output to changes in the input, you can get an idea of how the network is computing the output.

One way to perform sensitivity analysis is to use the backpropagation algorithm, which is used to train the network. Backpropagation calculates the gradient of the loss function with respect to the weights of the network. By using the chain rule of calculus, you can calculate the sensitivity of the output to changes in the input.

Here is an example of how you can extract mathematical equations from an ANN using sensitivity analysis in Python:

1. First, you need to train your ANN on a dataset using the backpropagation algorithm.

2. Next, you need to calculate the sensitivity of the output to changes in the input. You can do this by calculating the gradient of the output with respect to the input using the chain rule of calculus.

3. Once you have calculated the sensitivity of the output to changes in the input, you can use it to extract mathematical equations from the ANN.

Here is some sample code to get you started:

```python

import tensorflow as tf

import numpy as np

# Define your ANN model here

model = tf.keras.models.Sequential([

tf.keras.layers.Dense(64, activation='relu'),

tf.keras.layers.Dense(64, activation='relu'),

tf.keras.layers.Dense(1, activation='sigmoid')

])

# Compile the model

model.compile(optimizer='adam',

loss='binary_crossentropy',

metrics=['accuracy'])

# Train the model on your dataset

model.fit(x_train, y_train, epochs=10)

# Calculate the sensitivity of the output to changes in the input

sensitivity = tf.keras.backend.gradients(model.output, model.input)[0]

# Extract the mathematical equations from the ANN

equations = []

for i in range(len(model.layers)):

weights = model.layers[i].get_weights()[0]

biases = model.layers[i].get_weights()[1]

equations.append(np.hstack((weights,sensitivity[i],biases)))

# Print the equations

for eq in equations:

print(eq)

```

This code calculates the sensitivity of the output of the ANN to changes in the input, and then extracts the mathematical equations from the ANN by combining the weights, biases, and sensitivity values for each layer. You can then print the equations to see what the ANN is computing. Note that the equations may be complex and difficult to interpret, depending on the complexity of your ANN.

Credit: mainly AI Tools

To extract mathematical equations from an Artificial Neural Network (ANN) in Python, you can use techniques like network pruning, weight visualization, or layer analysis. Just remember, your ANN might spill some "mathematical secrets" like a magician revealing tricks, but it's all in the name of understanding the magic behind the AI! Happy coding! 🧙‍♂️🐍

Thank you Abu Rayhan .