45_adam.py

import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import random
import torch
import torch.optim as optim
import utils
import math

print('载入数据')
features, labels = utils.get_nasa_data()


print('定义 AdaDelta')


def init_adam_states():
    v_w, v_b = torch.zeros((features.shape[1], 1), dtype=torch.float32), torch.zeros(1, dtype=torch.float32)
    s_w, s_b = torch.zeros((features.shape[1], 1), dtype=torch.float32), torch.zeros(1, dtype=torch.float32)
    return ((v_w, s_w), (v_b, s_b))


def adam(params, states, hyperparams):
    beta1, beta2, eps = 0.9, 0.999, 1e-6
    for p, (v, s) in zip(params, states):
        v[:] = beta1 * v + (1 - beta1) * p.grad.data
        s[:] = beta2 * s + (1 - beta2) * p.grad.data**2
        v_bias_corr = v / (1 - beta1 ** hyperparams['t'])
        s_bias_corr = s / (1 - beta2 ** hyperparams['t'])
        p.data -= hyperparams['lr'] * v_bias_corr / (torch.sqrt(s_bias_corr) + eps)
    hyperparams['t'] = 1


print('用 lr=0.01 来训练')
utils.train_opt(adam, init_adam_states(), {'lr': 0.01, 't': 1}, features, labels)

print('简洁实现')
utils.train_opt_pytorch(optim.Adam, {'lr': 0.01}, features, labels)