tensorflow 1.01中GAN(生成对抗网络)手写字体生成例子(MINST)的测试

sparkexpert

发布于 2018-01-09 11:55:01

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发布于 2018-01-09 11:55:01

为了更好地掌握GAN的例子，从网上找了段代码进行跑了下，测试了效果。具体过程如下：

代码文件如下：

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import numpy as np from skimage.io import imsave import os import shutil img_height = 28 img_width = 28 img_size = img_height * img_width to_train = True to_restore = False output_path = "output" # 总迭代次数500 max_epoch = 500 h1_size = 150 h2_size = 300 z_size = 100 batch_size = 256 # generate (model 1) def build_generator(z_prior): w1 = tf.Variable(tf.truncated_normal([z_size, h1_size], stddev=0.1), name="g_w1", dtype=tf.float32) b1 = tf.Variable(tf.zeros([h1_size]), name="g_b1", dtype=tf.float32) h1 = tf.nn.relu(tf.matmul(z_prior, w1) + b1) w2 = tf.Variable(tf.truncated_normal([h1_size, h2_size], stddev=0.1), name="g_w2", dtype=tf.float32) b2 = tf.Variable(tf.zeros([h2_size]), name="g_b2", dtype=tf.float32) h2 = tf.nn.relu(tf.matmul(h1, w2) + b2) w3 = tf.Variable(tf.truncated_normal([h2_size, img_size], stddev=0.1), name="g_w3", dtype=tf.float32) b3 = tf.Variable(tf.zeros([img_size]), name="g_b3", dtype=tf.float32) h3 = tf.matmul(h2, w3) + b3 x_generate = tf.nn.tanh(h3) g_params = [w1, b1, w2, b2, w3, b3] return x_generate, g_params # discriminator (model 2) def build_discriminator(x_data, x_generated, keep_prob): # tf.concat x_in = tf.concat([x_data, x_generated],0) w1 = tf.Variable(tf.truncated_normal([img_size, h2_size], stddev=0.1), name="d_w1", dtype=tf.float32) b1 = tf.Variable(tf.zeros([h2_size]), name="d_b1", dtype=tf.float32) h1 = tf.nn.dropout(tf.nn.relu(tf.matmul(x_in, w1) + b1), keep_prob) w2 = tf.Variable(tf.truncated_normal([h2_size, h1_size], stddev=0.1), name="d_w2", dtype=tf.float32) b2 = tf.Variable(tf.zeros([h1_size]), name="d_b2", dtype=tf.float32) h2 = tf.nn.dropout(tf.nn.relu(tf.matmul(h1, w2) + b2), keep_prob) w3 = tf.Variable(tf.truncated_normal([h1_size, 1], stddev=0.1), name="d_w3", dtype=tf.float32) b3 = tf.Variable(tf.zeros([1]), name="d_b3", dtype=tf.float32) h3 = tf.matmul(h2, w3) + b3 y_data = tf.nn.sigmoid(tf.slice(h3, [0, 0], [batch_size, -1], name=None)) y_generated = tf.nn.sigmoid(tf.slice(h3, [batch_size, 0], [-1, -1], name=None)) d_params = [w1, b1, w2, b2, w3, b3] return y_data, y_generated, d_params # def show_result(batch_res, fname, grid_size=(8, 8), grid_pad=5): batch_res = 0.5 * batch_res.reshape((batch_res.shape[0], img_height, img_width)) + 0.5 img_h, img_w = batch_res.shape[1], batch_res.shape[2] grid_h = img_h * grid_size[0] + grid_pad * (grid_size[0] - 1) grid_w = img_w * grid_size[1] + grid_pad * (grid_size[1] - 1) img_grid = np.zeros((grid_h, grid_w), dtype=np.uint8) for i, res in enumerate(batch_res): if i >= grid_size[0] * grid_size[1]: break img = (res) * 255 img = img.astype(np.uint8) row = (i // grid_size[0]) * (img_h + grid_pad) col = (i % grid_size[1]) * (img_w + grid_pad) img_grid[row:row + img_h, col:col + img_w] = img imsave(fname, img_grid) def train(): # load data（mnist手写数据集） mnist = input_data.read_data_sets('MNIST_data', one_hot=True) x_data = tf.placeholder(tf.float32, [batch_size, img_size], name="x_data") z_prior = tf.placeholder(tf.float32, [batch_size, z_size], name="z_prior") keep_prob = tf.placeholder(tf.float32, name="keep_prob") global_step = tf.Variable(0, name="global_step", trainable=False) # 创建生成模型 x_generated, g_params = build_generator(z_prior) # 创建判别模型 y_data, y_generated, d_params = build_discriminator(x_data, x_generated, keep_prob) # 损失函数的设置 d_loss = - (tf.log(y_data) + tf.log(1 - y_generated)) g_loss = - tf.log(y_generated) optimizer = tf.train.AdamOptimizer(0.0001) # 两个模型的优化函数 d_trainer = optimizer.minimize(d_loss, var_list=d_params) g_trainer = optimizer.minimize(g_loss, var_list=g_params) init = tf.initialize_all_variables() saver = tf.train.Saver() # 启动默认图 sess = tf.Session() # 初始化 sess.run(init) if to_restore: chkpt_fname = tf.train.latest_checkpoint(output_path) saver.restore(sess, chkpt_fname) else: if os.path.exists(output_path): shutil.rmtree(output_path) os.mkdir(output_path) z_sample_val = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32) steps = 60000 / batch_size for i in range(sess.run(global_step), max_epoch): for j in np.arange(steps): # for j in range(steps): print("epoch:%s, iter:%s" % (i, j)) # 每一步迭代，我们都会加载256个训练样本，然后执行一次train_step x_value, _ = mnist.train.next_batch(batch_size) x_value = 2 * x_value.astype(np.float32) - 1 z_value = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32) # 执行生成 sess.run(d_trainer, feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)}) # 执行判别 if j % 1 == 0: sess.run(g_trainer, feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)}) x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_sample_val}) show_result(x_gen_val, "output/sample{0}.jpg".format(i)) z_random_sample_val = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32) x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_random_sample_val}) show_result(x_gen_val, "output/random_sample{0}.jpg".format(i)) sess.run(tf.assign(global_step, i + 1)) saver.save(sess, os.path.join(output_path, "model"), global_step=global_step) def test(): z_prior = tf.placeholder(tf.float32, [batch_size, z_size], name="z_prior") x_generated, _ = build_generator(z_prior) chkpt_fname = tf.train.latest_checkpoint(output_path) init = tf.initialize_all_variables() sess = tf.Session() saver = tf.train.Saver() sess.run(init) saver.restore(sess, chkpt_fname) z_test_value = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32) x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_test_value}) show_result(x_gen_val, "output/test_result.jpg") if __name__ == '__main__': if to_train: train() else: test()

按照500次迭代，每次迭代产生一张手写体图片，然后进行判别反馈，这样持续下去，可以看到不同迭代次数的效果。

(第1张)