利用GAN生成MNIST

小灰灰 2022-05-16 15:49 187阅读 0赞

# -*- 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)
            outputs = tf.tanh(logits)
            return logits, outputs
     
     
    # 判别
    def get_discriminator(img, n_units, reuse=False, alpha=0.01):
        with tf.variable_scope("discriminator", reuse=reuse):
            hidden1 = tf.layers.dense(img, n_units)
            hidden1 = tf.maximum(alpha * hidden1, hidden1)
            logits = tf.layers.dense(hidden1, 1)
            outputs = tf.sigmoid(logits)
            return logits, outputs
     
        
        
    img_size = mnist.train.images[0].shape[0]
    noise_size = 100
    g_units = 128
    d_units = 128
    alpha = 0.01
    learning_rate = 0.001
    smooth = 0.1
    
    
    tf.reset_default_graph()
    real_img, noise_img = get_inputs(img_size, noise_size)
    g_logits, g_outputs = get_generator(noise_img, g_units, img_size)
     
    d_logits_real, d_outputs_real = get_discriminator(real_img, d_units)
    d_logits_fake, d_outputs_fake = get_discriminator(g_outputs, d_units, reuse=True)
     
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_real, labels=tf.ones_like(d_logits_real)
    ) * (1 - smooth))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)
    ))
    d_loss = tf.add(d_loss_real, d_loss_fake)
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)
    ) * (1 - smooth))
     
    train_vars = tf.trainable_variables()
    g_vars = [var for var in train_vars if var.name.startswith("generator")]
    d_vars = [var for var in train_vars if var.name.startswith("discriminator")]
    
    #优化器 
    d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)
    g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)
     
     
    epochs = 5000
    samples = []
    n_sample = 10
    losses = []
    
    
    # 把MNISt数据中标签为0的图像提取出来，存到列表里
    i = j = 0
    while i<5000:
        if mnist.train.labels[j] == 3:
            samples.append(mnist.train.images[j])
            i += 1
        j += 1
    
    print(len(samples))
    size = samples[0].size
     
    with tf.Session() as sess:
    #    初始化参数
        tf.global_variables_initializer().run()
        for e in range(epochs):
            batch_images = samples[e] * 2 -1
            batch_noise = np.random.uniform(-1, 1, size=noise_size)
     
            _ = sess.run(d_train_opt, feed_dict={real_img:[batch_images], noise_img:[batch_noise]})
            _ = sess.run(g_train_opt, feed_dict={noise_img:[batch_noise]})
     
        sample_noise = np.random.uniform(-1, 1, size=noise_size)
        g_logit, g_output = sess.run(get_generator(noise_img, g_units, img_size,
                                             reuse=True), feed_dict={
            noise_img:[sample_noise]
        })
        print(g_logit.size)
        g_output = (g_output+1)/2
        plt.imshow(g_output.reshape([28, 28]), cmap='Greys_r')
        plt.show()

运行结果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI3ODU1MjE5_size_16_color_FFFFFF_t_70][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI3ODU1MjE5_size_16_color_FFFFFF_t_70]: /images/20220503/a02e023c8b5142aa83254437a2ef78a0.png