def NeuralNetwork(X, z, test=False):
"""Wrapper for a neural network. Trains a neural network using X and z.
Args:
X (np.ndarray): Input data the network is to be trained on.
z (np.ndarray): Response data the network is to be trained against.
test (bool, optional): If true, will search a hard-coded parameter-
space for optimal parameters instead of
training a network. Defaults to False.
Returns:
(float, list): (score reached, [testing set prediction, testing set])
"""
if not test:
hiddenLayers = 2
hiddenNeurons = 64
epochN = 500
minibatchSize = 32
eta = (None, 1e-03)
lmbd = 1e-06
alpha = 1e-00
activationFunction = sigmoid
outputFunction = softMax
Xtr, Xte, ztr, zte = train_test_split(X, z)
network = NN(hiddenNN=hiddenNeurons, hiddenLN=hiddenLayers)
network.giveInput(Xtr, ztr)
network.giveParameters(epochN=epochN,
minibatchSize=minibatchSize,
eta=etaDefinerDefiner(eta[0], eta[1]),
lmbd=lmbd,
alpha=alpha,
activationFunction=activationFunction,
outputFunction=outputFunction)
network.train(splitData=False)
network.predict(Xte, zte)
return network.score, [network.predictedLabel, zte]
else:
# Benchmarking parameters; random search
parameters = {
"hiddenLN": [0, 1, 2, 4],
"hiddenNN": [16, 32, 64, 128, 256],
"epochN": [500],
"minibatchSize": [32, 64],
"eta": [[j, i**k] for i in np.logspace(0, 6, 7)
for j, k in [(1, 1), (None, -1)]],
"lmbd":
np.logspace(-1, -6, 3),
"alpha":
np.logspace(-0, -1, 1),
"activationFunction": [sigmoid, ReLU_leaky, ReLU],
"outputFunction": [softMax],
"#repetitions":
5,
"datafraction":
1
}
optimalScore, optimalParams, optimalParamSTR = benchmarkNN(
X,
z,
parameters,
NN,
mode="classification",
randomSearch=False,
writingPermissions=False,
N=int(1e3))
print("Optimal Neural Network parameters:",
optimalScore,
optimalParamSTR,
sep="\n",
end="\n\n")
import numpy as np
from NeuralNetwork import NN
network = NN(3, 3, 3, 100000, .00001, .0001, 0, 0)
X = np.matrix('1,2,3;4,3,6;1,2,5;0,1,2;1,4,5;1,0,1')
y = np.matrix('0;2;1;1;1;2')
X_cv = np.matrix('1,0,0;1,2,4;10,7,4')
y_cv = np.matrix('0;1;1')
print(network.fit(X, y))
print(network.predict(X_cv))
network.cross_validate(X_cv, y_cv)