小编Amp*_*Amp的帖子

我正在学习卷积自动编码器，并且正在使用 keras 构建图像降噪器。以下代码适用于构建模型：

denoiser.add(Conv2D(32, (3,3), input_shape=(28,28,1), padding='same')) 
denoiser.add(Activation('relu'))
denoiser.add(MaxPooling2D(pool_size=(2,2)))

denoiser.add(Conv2D(16, (3,3), padding='same'))
denoiser.add(Activation('relu'))
denoiser.add(MaxPooling2D(pool_size=(2,2)))

denoiser.add(Conv2D(8, (3,3), padding='same'))
denoiser.add(Activation('relu'))

################## HEY WHAT NO MAXPOOLING?

denoiser.add(Conv2D(8, (3,3), padding='same'))
denoiser.add(Activation('relu'))
denoiser.add(UpSampling2D((2,2)))

denoiser.add(Conv2D(16, (3,3), padding='same'))
denoiser.add(Activation('relu'))
denoiser.add(UpSampling2D((2,2)))

denoiser.add(Conv2D(1, (3,3), padding='same'))

denoiser.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
denoiser.summary()

并给出以下总结：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_155 (Conv2D)          (None, 28, 28, 32)        320       
_________________________________________________________________
activation_162 (Activation)  (None, 28, 28, 32)        0         
_________________________________________________________________
max_pooling2d_99 (MaxPooling (None, 14, 14, 32)        0         
_________________________________________________________________
conv2d_156 (Conv2D)          (None, 14, 14, 16)        4624 …

我正在尝试编写区分猫和狗的CNN。我将标签设置为dog：0和cat：1，因此我希望CNN如果是狗则输出0，如果是猫则输出1。但是，它反而相反（给它的猫是0，给狗1则是0）。请查看我的代码，看看我哪里出错了。谢谢

我目前正在使用jupyter笔记本使用python 3.6.8（内部的所有代码都是我从jupyter笔记本中复制粘贴代码的不同部分）

import os
import cv2
from random import shuffle
import numpy as np
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
%matplotlib inline

train_dir = r'C:\Users\tohho\Desktop\Python pypipapp\Machine Learning\data\PetImages\train'
test_dir = r'C:\Users\tohho\Desktop\Python pypipapp\Machine Learning\data\PetImages\test1'
IMG_WIDTH = 100
IMG_HEIGHT = 100
batch_size = 32



######## THIS IS WHERE I LABELLED 0 …