Question

由于我是深度学习的新手，而且我想了解这个概念，因此我尝试将现有的CNN模型教程与我自己的数据集一起使用。

我不明白的是标签y值。如何训练模型为每个样本x赋予标签y？除了使用lop之外，还有没有更好的方法来将带有标签的数据加载到cnn？

我有5个动物的数据集，包括1725个（整体）。例如，狗= 3张图片。猫= 54张图片。

每次尝试运行模型时，都会出现此错误：

ValueError: Input arrays should have the same number of samples as target arrays. Found 5172 input samples and 1725 target samples.

这是输入

import numpy as np
import matplotlib.pyplot as plt
import os 
import cv2
import tensorflow as tf
import tensorflow.keras as keras
from keras.utils import to_categorical


IMG_SIZE = 64

PATH = os.getcwd()
data_path = PATH + '\image'
data_dir_list = os.listdir(data_path)

num_classes = 5
img_data_list = []
num_channel=1



for dataset in data_dir_list:
    img_list=os.listdir(data_path+'/'+ dataset)
    print ('Loaded the images of dataset-'+'{}\n'.format(dataset))
    for img in img_list:
        input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img )
        input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)
        input_img_resize=cv2.resize(input_img,(64,64))
        img_data_list.append(input_img_resize)

img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data /= 255
print(img_data.size)
print (img_data.shape)

运行后

Loaded the images of dataset-0

Loaded the images of dataset-1

Loaded the images of dataset-2

Loaded the images of dataset-3

Loaded the images of dataset-4

7065600
(1725, 64, 64)

如果我检查img_data列表

print(len(img_data))
print(img_data)
1725
[[[0.8784314  0.32941177 0.22745098 ... 0.13333334 0.06666667 0.05490196]
  [0.03137255 0.16862746 0.14901961 ... 0.18431373 0.20784314 0.16470589]
  [0.1764706  0.42745098 0.26666668 ... 0.42352942 0.05882353 0.00784314]
  ...
  [0.42352942 0.4        0.2901961  ... 0.3647059  0.4392157  0.4392157 ]
  [0.38431373 0.4        0.4392157  ... 0.4392157  0.3019608  0.32941177]
  [0.20784314 0.41568628 0.40392157 ... 0.42745098 0.21176471 0.3372549 ]]

重塑img_data之后在这里

#num_of_samples = 1725 

from sklearn.model_selection import train_test_split
import random 
from sklearn.utils import shuffle


num_of_samples = img_data.shape[0]

# convert class labels to on-hot encoding
#Y = np_utils.to_categorical(labels, 5)
#train_labels = keras.utils.to_categorical(labels, num_classes)
nb_train_samples = 1725
train_labels = np.array([0] * (195) + [1] * (120) + [2] * (380) + [3] * (144) + [4] * (886))
train_labels = keras.utils.to_categorical(train_labels, num_classes = 5)

#Shuffle the dataset
x = [] # for images 
y = [] # for labels 
print(train_labels.shape)


x = shuffle(img_data)
print(x.shape)

y = shuffle(train_labels)
print(y.shape)
# Split the dataset
y_train = y.reshape((1725,5))


X_train,X_test, y_train, y_test = train_test_split(X, y)

X_train = np.array(X_train).reshape(-1, 32,32, 1)
print (X_train.size)

X_test = np.array(X_test).reshape(-1, 32,32, 1)


print (y_train.size)


print (train_labels)
print (train_labels.size)

输出

(1725, 5)
(1725, 64, 64)
(1725, 5)

ValueError：找到输入样本数量不一致的输入变量：[5175，1725]

#Set Model Parameters

batch_size = 15
epochs = 50
num_classes = 5

input_shape=img_data[0].shape


#Build Model 

Model = Sequential()
Model.add(Conv2D(32, kernel_size=(3,3), input_shape=(32,32,1)))
Model.add(Activation('relu'))
Model.add(MaxPooling2D(pool_size=(2, 2)))
Model.add(Conv2D(64, (3, 3)))
Model.add(Activation('relu'))
Model.add(Flatten())
Model.add(Dense(1024))
Model.add(Activation('relu'))
Model.add(Dropout(0.4))
Model.add(Dense(num_classes))
Model.add(Activation('softmax'))



# Model Compiling 

Model.compile(loss = "categorical_crossentropy", optimizer = "Adam", metrics=['accuracy'])

Answer 1

关于5175，1725 * 3 = 5175，所以一些numpy整形操作是将RGB颜色通道（3）和样本计数（1725）组合在一起吗？不过，由于图像已转换为灰度，因此上述代码段中的情况并非如此。

是的，有一种更简单的方法可以将图像数据加载到Keras中。只需将每个类的图像放在其自己的子文件夹（如dog，cat等）下，然后使用ImageDataGenerator的flow_from_directory方法。另一个优点是它还支持数据扩充，规范化等。

https://keras.io/preprocessing/image/

建立CNN模型时出错，目标大小与样本大小不同

1 个答案: