利用GAN生成MNIST

最新推荐文章于 2024-10-15 18:34:14 发布

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最新推荐文章于 2024-10-15 18:34:14 发布 · 528 阅读

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#tensorflow #python #GAN

# -*- coding: utf-8 -*-
"""
Created on Tue Oct 30 10:34:40 2018
@author: Grey
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt
import numpy as np
 
mnist = input_data.read_data_sets("./data/")
img = mnist.train.images[50]#第50张图片的像素

 
def get_inputs(real_size, noise_size):
#    占位，实际图像和随机噪声z
    real_img = tf.placeholder(tf.float32, [None, real_size], name="real_img")
    noise_img = tf.placeholder(tf.float32, [None, noise_size], name="noise_img")
    return real_img, noise_img
 
 
# 生成
def get_generator(noise_img, n_units, out_dim, reuse=False, alpha=0.01):
    with tf.variable_scope("generator", reuse=reuse):
        hidden1 = tf.layers.dense(noise_img, n_units)  # 全连接层
        hidden1 = tf.maximum(alpha * hidden1, hidden1)
#        防止过拟合，加入dropout
        hidden1 = tf.layers.dropout(hidden1, rate=0.2)
        logits = tf.layers.dense(hidden1, out_dim)
        out