def feature_fit(x_list,
y_list,
layer_sizes,
activations,
N_epochs=10,
learning_rate=1.0,
threshold=1e-3,
lambda_reg=0.0):
# given x,y pairs, construct the neural network, fit the data, and return the feature vector, including the normalization parameters
N = len(x_list)
y_norm = np.array(y_list)
neural_net = NN(layer_sizes, activations)
#neural_net.derivative_check(m=5, epsilon=1e-4, verbose=False)
inputs = np.reshape(np.array(x_list), (len(x_list), 1)).T
Js = []
for i in range(N_epochs):
A = neural_net.forward_prop(inputs)
dAdZ = 1
J = 1 / 2.0 * np.sum(np.power(A - y_norm, 2)) / N
Js.append(J)
dJdZ = -(A - y_norm) * dAdZ
neural_net.back_prop(dJdZ)
neural_net.update_weights(learning_rate=learning_rate,
lambda_reg=lambda_reg)
if J < threshold:
break
feature_vec = neural_net.flatten_parameters()
return feature_vec, J
from NN import NN import numpy as np import matplotlib.pylab as plt layer_sizes = [2,4,1] activations = ['relu','sigmoid'] N = NN(layer_sizes,activations) #print(N.biases[4].shape) input = np.array([[1,2,3],[3,5,4]]) N.forward_prop(input) N.back_prop(np.array([[1,2,3]])) N.derivative_check(m=6,verbose=False) N.update_weights()
