Question

我正在尝试使用以下代码构建自动编码器

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()

我使用的训练数据：

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（ train_ds， validation_data = val_ds，历元= 50 ）我收到ValueError：logits和标签的形状必须相同（（None，328，328，3）vs（None，1））

有人知道如何解决这个问题吗？

Answer 1

对于自动编码器，您的目标必须是您的输入。您要最小化的错误是重建损失，因此输出必须是原始输入。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_mode='input'。请注意，我似乎有一个不同的版本，所以我不得不用image_size替换target_size才能为我运行。

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'
)

这是一个完整的工作示例，其中包含生成的数据，该示例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)

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

ValueError：自动编码器的logit和标签必须具有相同的形状（（None，328，328，3）vs（None，1））

1 个答案: