Python ValueError:logits和标签必须与autoencoder具有相同的形状（（无，328，328，3）和（无，1））

我正在尝试用以下代码构建一个自动编码器

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sys
import os
import PIL
import PIL.Image
import tensorflow as tf
import tensorflow_datasets as tfds

import keras
from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D, MaxPool2D, Flatten, BatchNormalization
from keras.layers import Conv1D, MaxPool1D, Reshape
from keras.layers import Input, Dense, Dropout, Activation, Add, Concatenate
from keras import regularizers
from keras.models import Model, Sequential
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.optimizers import SGD, Adam, RMSprop, Adadelta
import keras.backend as K
from keras.objectives import mean_squared_error
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils

def create_block(input, chs): ## Convolution block of 2 layers
    x = input
    for i in range(2):
        x = Conv2D(chs, 3, padding="same")(x)
        x = Activation("relu")(x)
        x = BatchNormalization()(x)
    return x
input_img = Input(shape=(328, 328, 3))

block1 = create_block(input_img, 32)
x = MaxPool2D(2)(block1)
block2 = create_block(x, 64)

#Middle
x = MaxPool2D(2)(block2)
middle = create_block(x, 128)

# Decoder
block3 = create_block(middle, 64)
up1 = UpSampling2D((2,2))(block3)
block4 = create_block(up1, 32)
up2 = UpSampling2D((2,2))(block4)

# output
x = Conv2D(3, 1)(up2)
output = Activation("sigmoid")(x)


autoencoder = Model(input_img, output)
autoencoder.compile(SGD(1e-3, 0.9), loss='binary_crossentropy')
autoencoder.summary()

对于我的培训数据，我使用：

img_height = 328
img_width = 328

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

当我尝试使用autoencoder.fit运行它时( 列车,， 验证数据=val\U ds， 纪元=50 )我得到了ValueError：logits和标签必须具有相同的形状（（None，328，328，3）vs（None，1））

---

有人知道如何解决这个问题吗？

对于自动编码器，您的目标需要是您的输入。您试图最小化的错误是重建损失，因此输出需要是原始输入。默认情况下，Keras目录迭代器将返回目标（例如0和1），而不是输入

generator = tf.keras.preprocessing.image.ImageDataGenerator(
    validation_split=0.2
)

指定

class\u mode='input'

。请注意，我似乎有一个不同的版本，所以我必须用

target\u size

替换

image\u size

，才能运行它

train_ds = generator.flow_from_directory(
  data_dir,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size,
  class_mode='input'
)

下面是一个生成数据的完整工作示例，它使用您提供的自动编码器体系结构：

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Conv2D, UpSampling2D, MaxPool2D
from tensorflow.keras.layers import Activation, Dense, Input, BatchNormalization
from tensorflow.keras import Model, Sequential

def create_block(input, chs):
    x = input
    for i in range(2):
        x = Conv2D(chs, 3, padding="same")(x)
        x = Activation("relu")(x)
        x = BatchNormalization()(x)
    return x
input_img = Input(shape=(328, 328, 3))

block1 = create_block(input_img, 32)
x = MaxPool2D(2)(block1)
block2 = create_block(x, 64)

#Middle
x = MaxPool2D(2)(block2)
middle = create_block(x, 128)

# Decoder
block3 = create_block(middle, 64)
up1 = UpSampling2D((2,2))(block3)
block4 = create_block(up1, 32)
up2 = UpSampling2D((2,2))(block4)

# output
x = Conv2D(3, 1)(up2)
output = Activation("sigmoid")(x)

X = np.random.rand(8, 328, 328, 3).astype(np.float32)

autoencoder = Model(input_img, output)
autoencoder.compile('adam', loss='binary_crossentropy')
autoencoder.summary()

generator = tf.keras.preprocessing.image.ImageDataGenerator()

train_ds = generator.flow(
  x=X,
  y=X
)

history = autoencoder.fit(train_ds)

---

谢谢！现在可以了。由于我是Keras的新手，我想知道使用这些数据生成器是否正常？因为在所有使用mnist、cifar10等的示例中，生成器都使用普通numpy数组。与玩具数据集（例如mnist、CIFAR-10）相比，这些生成器的优点是不将整个数据集加载到内存中。如果处理文件夹中的图像，一定要使用目录迭代器以提高效率。教程使用MNIST/CIFAR-10实现再现性–您只需复制和粘贴代码即可运行。编辑：在我的例子中，它不会改变任何东西，因为数组已经在内存中了，但是你明白了。谢谢你对我的答案的评论！我混淆了自动编码器和编码器-解码器网络，因为这两个术语的用法经常被人们误解。我现在都明白了，谢谢你！再次感谢您指出，我将删除我的答案，因为它与问题不匹配。没问题！！

Train for 1 steps
1/1 [==============================] - 9s 9s/step - loss: 0.8827