Question

我是深度学习领域的初学者。我正在尝试根据TensorFlow网站上给出的示例开发一个“对抗式生成网络”。不幸的是，我遇到一些错误，无法使其运行。我在这里发布可以执行的代码，并提供需要解决的错误。

import numpy as np 
import matplotlib.pyplot as plt 
import pandas as pd
import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from keras.layers import Input, Dense, Reshape, Flatten, Dropout 
from keras.layers import BatchNormalization, Activation
from keras.layers.advanced_activations import LeakyReLU 
from keras.layers.convolutional import Conv2D 
from keras.models import Sequential, Model 
from keras.optimizers import SGD 

def makediag3d(a):
    a = np.asarray(a)
    depth, size = a.shape
    x = np.zeros((depth,size,size))
    for i in range(depth):
        x[i].flat[slice(0,None,1+size)] = a[i]
   return x

input = pd.read_excel( '3_height.xls')
new_input = (makediag3d(input))
output = pd.read_excel( '3_absor.xls')
new_output= np.array(output)

BUFFER_SIZE = 50000 BATCH_SIZE = 128 train_dataset =

tf.data.Dataset.from_tensor_slices（new_input）.shuffle（BUFFER_SIZE）.batch（BATCH_SIZE）

def make_generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(3*3*256, use_bias=False, input_shape=(50,)))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Reshape((3, 3, 256)))
    assert model.output_shape == (None, 7, 7, 256)
    model.add(layers.Conv2DTranspose(128, (5, 5), strides(1,1),padding='same',  use_bias=False)) 
    assert model.output_shape == (None, 3, 3, 128)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(64, (5, 5),strides(2,2),padding='same', 
    use_bias=False))
    assert model.output_shape == (None, 3, 3, 64)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2),padding='same',  

    use_bias=False, activation='tanh'))
    assert model.output_shape == (None, 3, 3, 1)

    return model
generator = make_generator_model()

def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
                                 input_shape=[3, 3, 1]))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.2))

    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.2))

    model.add(layers.Flatten())
    model.add(layers.Dense(1))

    return model
discriminator = make_discriminator_model()

cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss

checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
                             discriminator_optimizer=discriminator_optimizer,
                             generator=generator,
                             discriminator=discriminator)

我得到的错误：

NameError                                 Traceback (most recent call last)
<ipython-input-84-0106cda2248f> in <module>
      3 checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
      4                                  discriminator_optimizer=discriminator_optimizer,
----> 5                                  generator=generator,
      6                                  discriminator=discriminator)
      7 

NameError: name 'generator' is not defined

我还提供了完整代码的链接，以供您仔细阅读。

https://drive.google.com/file/d/1VJOARa9XvPWtcjQFbd0jATLsbkeEzJcl/view？ usp =分享

生成对抗网络收敛误差

0 个答案: