"Ideally, I would end up with an equation that would allow me to perform the classification without the network".
If you could find such analytic equation without machine learning then why training a multi layer perceptron in the first place? Or to phrase it differently, the mlp you trained is that equation. And I'm not trying to be ironic, if you need an analytic explanation then don't use a multi layer perceptron, but move to decision trees algorithms for example, than you could literally plot the model itself (still hard to interpret depending on the amount of features you're using).
If instead you still want to stick with mlp, then something that you could do to better understand your model is plotting the decision boundaries learned by it. Sklearn has a nice tutorial on how to do it, I copied it and changes the svm with an mlp just to show that the approach works regardless of the model
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.neural_network import MLPClassifier
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target
h = 0.02 # step size in the mesh
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0 # SVM regularization parameter
mlp = MLPClassifier().fit(X, y)
# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# title for the plots
title = "MLP boundries"
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
# plt.subplot(1, 1)
# plt.subplots_adjust(wspace=0.4, hspace=0.4)
Z = mlp.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm)
plt.xlabel("Sepal length")
plt.ylabel("Sepal width")
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.title(title)
plt.show()
The code output this plot:
Your can see that the boundaries are a crude approximation of the mlp behavior, they are just estimated from a brute force prediction applied to all points of the 2D graph generated by 2 features of the input data. SO the boundaries will change depending also on the features you decide to plot. But it gives you an idea about the relationships learned by the mlp.
If you want more, I stress again that you should train a different model, like decision tree, random forest or XGboost, with these model you can compute scores about features importance and literally plot the decision thresholds learned by the models.
