引自免费微信小程序：皆我百晓生
在提供的代码片段中，出现了几个问题。首先，让我们解决FileNotFoundError的问题，然后优化代码并提供完整的示例。
错误发生的原因是save_result函数试图保存图像到一个不存在的目录。确保目录'../tmp/gan_images/'已经存在。如果目录不存在，可以添加一行创建这个目录的代码。另外，在save_result函数中有一个拼写错误：将val block size更正为val_block_size。
下面是修改后的代码：
import os
import tensorflow as tf
from PIL import Image
import numpy as np
def create_directory(path):
if not os.path.exists(path):
os.makedirs(path)
def preprocess(val_out):
return ((val_out + 1.0) * 127.5).astype(np.uint8)
def save_result(val_out, val_block_size, image_path, color_mode="p"):
preprocessed = preprocess(val_out)
final_image = np.array([])
single_row = np.array([])
for b in range(val_out.shape[0]):
if single_row.size == 0:
single_row = preprocessed[b,:,:,:]
else:
single_row = np.concatenate((single_row, preprocessed[b,:,:,:]), axis=1)
if (b+1) % val_block_size == 0:
if final_image.size == 0:
final_image = single_row
else:
final_image = np.concatenate((final_image, single_row), axis=0)
single_row = np.array([])
Image.fromarray(final_image).save(image_path)
def train_step(batch_x):
batch_z = tf.random.normal([batch_size, z_dim])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
fake_image = generator(batch_z, training=True)
d_fake_logits = discriminator(fake_image, training=True)
d_real_logits = discriminator(batch_x, training=True)
d_loss = d_loss_fn(d_real_logits, d_fake_logits)
g_loss = glossfn(d_fake_logits)
grads_d = disc_tape.gradient(d_loss, discriminator.trainable_variables)
grads_g = gen_tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(zip(grads_g, generator.trainable_variables))
d_optimizer.apply_gradients(zip(grads_d, discriminator.trainable_variables))
return d_loss, g_loss
# 假设你已经有了generator、discriminator、g_loss_fn、d_loss_fn、g_optimizer、d_optimizer、dataset、z_dim、batch_size变量定义
create_directory('../tmp/gan_images')
for n, data in dataset.enumerate():
d_loss, g_loss = train_step(data)
print('.', end='')
if n % 100 == 0:
print()
print(n.numpy(), 'd-loss:', float(d_loss), 'g-loss:', float(g_loss))
# 可视化
z = tf.random.normal([100, z_dim])
fake_image = generator(z, training=False)
img_path = os.path.join('../tmp/gan_images', 'gan-%d.png' % n)
save_result(fake_image.numpy(), 10, img_path, color_mode="p")
这段代码修复了路径问题，并且进行了相应的格式调整。你需要根据实际情况替换generator、discriminator、g_loss_fn、d_loss_fn、g_optimizer、d_optimizer、dataset、z_dim和batch_size这些变量的定义。同时，确保你已安装了Pillow库（pip install pillow），以便进行图像处理。如果你使用的是Jupyter notebook或者其他支持Markdown的环境，你可能还需要安装matplotlib库（pip install matplotlib）以便于显示图像。
