wgan-gp

最新推荐文章于 2023-05-25 15:31:18 发布

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最新推荐文章于 2023-05-25 15:31:18 发布 · 381 阅读

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import random
import numpy as np
import torch
from matplotlib import pyplot as plt
from torch import nn, optim, autograd
from visdom import Visdom

# 生成real-data数据集
def data_generator():
    """
        预设数据样本分布为8个高斯分布叠加的分布模型
    """
    scale = 2.
    centers = [
        (1, 0), (-1, 0), (0, 1), (0, -1),
        (1. / np.sqrt(2), 1. / np.sqrt(2)),
        (1. / np.sqrt(2), -1. / np.sqrt(2)),
        (-1. / np.sqrt(2), 1. / np.sqrt(2)),
        (-1. / np.sqrt(2), -1. / np.sqrt(2))
    ]
    centers = [(scale * x, scale * y) for x, y in centers]
    while True:
        dataset = []
        for i in range(batch_size):
            point = np.random.randn(2) * 0.02
            center = random.choice(centers)
            point[0] += center[0]
            point[1] += center[1]
            dataset.append(point)
        dataset = np.array(dataset, dtype='float32')
        dataset /= 1.414  # stdev
        yield dataset

# hyper-parameters
hidden_d