python - 构建 CNN 模型时出错，目标大小与样本大小不同

问题描述

我正在尝试将现有的 CNN 模型教程与我自己的数据集一起使用，因为我是深度学习的新手，我想了解这个概念。

我没有得到的是标签 y 值。如何训练模型为每个样本 x 赋予标签 y？除了使用 for lop 之外，有没有更好的方法将带有标签的数据加载到 cnn？

我有 5 只动物的数据集，由 1725 只（整体）组成。例如，狗 = 3 张图片。猫 = 54 张图片。

每次我尝试运行我的模型时，我都会收到此错误：

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)

在我运行它之后

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 ]]

在重塑 img_data 之后

#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：发现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'])

标签： pythontensorflowkeras

python - 构建 CNN 模型时出错，目标大小与样本大小不同

问题描述

解决方案

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