tensorflow学习——DCGAN手写体生成_dcgan用mnist数据集实现手写字体生成-优快云博客

本文链接：https://blog.youkuaiyun.com/ei1990/article/details/77169851

本文详细介绍了使用TensorFlow实现Deep Convolutional Generative Adversarial Networks (DCGAN)进行手写体生成的过程。通过训练，DCGAN能够学习到手写数字的特征，并生成新的、逼真的手写数字图像。内容包括DCGAN的模型结构、训练策略以及生成结果的展示。

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import numpy as np
import tensorflow as tf
import pickle
import matplotlib.pyplot as plt

print("TensorFlow Version: {}".format(tf.__version__))
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data/')

# ## Train
# 定义参数
batch_size = 64
noise_size = 100
epochs = 5
n_samples = 25
learning_rate = 0.001
beta1 = 0.4


class DCGAN():
    @staticmethod
    def get_inputs(noise_dim, image_height, image_width, image_depth):
            inputs_real = tf.placeholder(tf.float32, [None, image_height, image_width, image_depth], name='inputs_real')
            inputs_noise = tf.placeholder(tf.float32, [None, noise_dim], name='inputs_noise')

            return inputs_real, inputs_noise

    def get_generator(self, noise_img, output_dim, is_train=True, alpha=0.01):
            """
            @Author: Nelson Zhao
            --------------------
            :param noise_img: 噪声信号，tensor类型
            :param output_dim: 生成图片的depth
            :param is_train: 是否为训练状态，该参数主要用于作为batch_normalization方法中的参数使用
            :param alpha: Leaky ReLU系数
            """
            with tf.variable_scope("generator") as scope0:
                if not is_train:
                    scope0.reuse_variables()
                # none*100  to none*4 x 4 x 512
                # 全连接层
                layer1 = tf.layers.dense(noise_img, 4*4*512)
                layer1 = tf.reshape(layer1, [-1, 4, 4, 512])
                # batch normalization
                layer1 = tf.layers.batch_normalization(layer1, training=is_train)
                # Leaky ReLU
                layer1 = tf.maximum(alpha * layer1, layer1)
                # dropout
                layer1 = tf.nn.dropout(layer1, keep_prob=0.8)