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首页 > 解决方案 > 检查目标时出错：预期 dense_1 的形状为 (5749,) 但得到的数组的形状为 (1,)

python - 检查目标时出错：预期 dense_1 的形状为 (5749,) 但得到的数组的形状为 (1,)

问题描述

我是深度学习的初学者，正在构建一个程序，从图像中确定人。但我的神经网络显示错误，我不知道如何修复它 -

model.fit(imgs_array,Y,batch_size = 401, epochs = 2, validation_split = 0.2)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1527, in fit
    x, y, sample_weights = self._standardize_user_data(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 991, in _standardize_user_data
    x, y, sample_weights = self._standardize_weights(x, y, sample_weight,
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1149, in _standardize_weights
    y = training_utils.standardize_input_data(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/keras/engine/training_utils.py", line 329, in standardize_input_data
    raise ValueError(
ValueError: Error when checking target: expected dense_1 to have shape (5749,) but got array with shape (1,)

我的完整代码是

import os
import numpy as np
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from sklearn.model_selection import train_test_split
from tensorflow.python import keras
from tensorflow.keras.utils import to_categorical
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten, Conv2D, Dropout


folders = os.listdir('lfw/')
# print(len(folders))       #5749
folders_a = np.asarray(folders)

image_files = []
X = []
Y = []
for folder in folders:
    files = os.listdir('lfw/'+folder+'/')       # type of files is a list
    image_files.append(files)
    for file in files:
        X.append(file)
        Y.append(folder)

# print(len(Y))     #13233
img_paths = []
for i in range(0,13233):
    img_paths.append('lfw/'+Y[i]+'/'+X[i])      #img_paths is set now

print(len(img_paths))

imgs = []
for img_path in img_paths:
    img_1 = load_img(img_path, color_mode="grayscale")
    imgs.append(img_to_array(img_1))

    # print(imgs[0].shape)      #(250,250,1)



Y = np.array(Y)
# print(type(Y))
# print(Y.shape)        #(13233,)

#Building Neural Network
imgs_array = np.array(imgs)
imgs_array /= 255


model = Sequential()
model.add(Conv2D(20, kernel_size = 3, activation = 'relu', input_shape =(250,250,1)))
model.add(Conv2D(20, kernel_size =3, activation = 'relu'))
model.add(Flatten())
model.add(Dense(401, activation = 'relu'))
model.add(Dense(5749,activation = 'softmax'))
print("compiling initiated")
model.compile(loss = 'mean_squared_error',optimizer = 'sgd')
print("model compiled")
model.fit(imgs_array,Y,batch_size = 401, epochs = 2, validation_split = 0.2)

错误的原因是什么以及如何解决？上面代码中的print(Y.shape)行给了我输出(13233,)。为什么它显示(1, )在错误中？

注意 - 我已经看到下面给出的链接，但它们没有解决我的问题。

检查目标时出错：预期 dense_1 的形状为 (257, 257) 但得到的数组的形状为 (257, 1)

拟合 RNN LSTM 模型时出错

检查目标时出错：预期 dense_1 的形状为 (1,) 但得到的数组的形状为 (256,)

标签： pythonnumpytensorflowmachine-learningdeep-learning

解决方案

快速浏览一下，我认为您的问题如下：您的网络将形状张量 (batch_size, 250, 250, 1) 作为输入，并将形状张量 (batch_size, 5749) 作为输出

当您开始拟合时，keras 会批量输入数据和相应的标签，因此它会为您的网络输入张量提供形状 (batch_size,) + imgs_array.shape[1:] 和形状标签 (batch_size,) + Y.shape[1 :]

也就是说，对于批次中的每个图像，您的标签是一个标量（或者显然是一个一维向量，我没有仔细阅读）并且您与之比较的输出是一个大小为 5749 的张量，这不是标量。

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