Question

我正在尝试在Wild Dataset的面孔上实现卷积自动编码器，它由形状为62x47x3的图像组成。

但是，mnist数据集上的Keras卷积自动编码器示例不适用于我正在训练的这个新数据集。

它抛出此错误

Error when checking target: expected conv2d_102 to have shape (60, 44, 3) but got array with shape (62, 47, 3)

关于某个包含错误形状输入的图层，即使包含

padding='same'

应该使输入和输出形状相等的命令。

我只尝试在网络中使用灰度图像，但这没有什么区别。

这是我正在使用的主要代码


import tensorflow
import keras
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Model, Sequential
from keras.layers import Dense, Conv2D, Dropout, BatchNormalization, Input, Reshape, Flatten, Deconvolution2D, Conv2DTranspose, MaxPooling2D, UpSampling2D, LeakyReLU
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import adam

from sklearn.datasets import fetch_lfw_people
from sklearn.model_selection import train_test_split

#importing the dataset in color cause that's dope
lfw_data = fetch_lfw_people(color=True)

#putting the data of images into a variable
x = lfw_data.images

#making a train and validation set
(x_train,x_test) = train_test_split(x, test_size=0.25)

#normalizing the pixel values
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

print(x_train.shape)

x_train = x_train.reshape(len(x_train), 62,47,3)
x_test = x_test.reshape(len(x_test), 62,47,3)

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K

input_img = Input(shape=(62, 47, 3))  # adapt this if using `channels_first` image data format

x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

# at this point the representation is (4, 4, 8) i.e. 128-dimensional

x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

autoencoder.summary()

，模型摘要输出为

Model: "model_14"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_18 (InputLayer)        (None, 62, 47, 3)         0         
_________________________________________________________________
conv2d_103 (Conv2D)          (None, 62, 47, 16)        448       
_________________________________________________________________
max_pooling2d_45 (MaxPooling (None, 31, 24, 16)        0         
_________________________________________________________________
conv2d_104 (Conv2D)          (None, 31, 24, 8)         1160      
_________________________________________________________________
max_pooling2d_46 (MaxPooling (None, 16, 12, 8)         0         
_________________________________________________________________
conv2d_105 (Conv2D)          (None, 16, 12, 8)         584       
_________________________________________________________________
max_pooling2d_47 (MaxPooling (None, 8, 6, 8)           0         
_________________________________________________________________
conv2d_106 (Conv2D)          (None, 8, 6, 8)           584       
_________________________________________________________________
up_sampling2d_42 (UpSampling (None, 16, 12, 8)         0         
_________________________________________________________________
conv2d_107 (Conv2D)          (None, 16, 12, 8)         584       
_________________________________________________________________
up_sampling2d_43 (UpSampling (None, 32, 24, 8)         0         
_________________________________________________________________
conv2d_108 (Conv2D)          (None, 30, 22, 16)        1168      
_________________________________________________________________
up_sampling2d_44 (UpSampling (None, 60, 44, 16)        0         
_________________________________________________________________
conv2d_109 (Conv2D)          (None, 60, 44, 1)         145       
=================================================================
Total params: 4,673
Trainable params: 4,673
Non-trainable params: 0
____________________________

当我尝试训练时

#train for 100 epochs
history = autoencoder.fit(x_train, x_train,epochs=100,batch_size=256, shuffle=True, validation_data=(x_test, x_test))

我收到此错误消息

Error when checking target: expected conv2d_102 to have shape (60, 44, 3) but got array with shape (62, 47, 3)

任何引发该错误原因的帮助或解释都很棒！

Answer 1

这是因为池和填充不匹配。您的数据的形状为(62,47)，但是模型输出为(60,44)。您需要适当调整模型或数据。

基于池的工作方式（除以2），并考虑到您有3个池，则图像大小仅在其2倍3 = 8的倍数时才正确匹配池。由于大小64和48非常接近图像的大小，似乎最简单的解决方案是在图像上添加填充。

因此，使数据的大小为(64,48)。 -这将允许多达4个池，而无需在模型中自定义填充。

x_train = np.pad(x_train, ((0,0), (1,1), (0,1), (0,0)), mode='constant')
x_test = np.pad(x_test, ((0,0), (1,1), (0,1), (0,0)), mode='constant')

不要忘记将padding='same'设置为所有图层。有一个卷积错过了它（一个在最后一个之前）

也许here列出的某些模式可能比其他模式表现更好。（例如，我会尝试使用mode='edge'。）

卷积自动编码器未针对（62,47,1）数据集“预期形状错误”进行训练

1 个答案: