应用 CNN 方法进行人脸识别但得到值错误

Posted 2023-03-12

【中文标题】应用 CNN 方法进行人脸识别但得到值错误

【英文标题】：Applying CNN method for face recognition but get the value error

【发布时间】：2021-04-08 10:27:08

【问题描述】：

所提出的方法可以在算法确定的条件下自动检测高光谱图像的特征，并达到正确快速的识别结果。

在这里，我尝试使用 CNN 方法运行人脸识别，但随后收到如下错误消息 ---

**

File "<ipython-input-6-fdb29ac830ce>", line 1, in <module>
    runfile('C:/Users/MDIC/Desktop/Face Recognition With CNN.py', wdir='C:/Users/MDIC/Desktop')
  File "C:\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 786, in runfile
    execfile(filename, namespace)
  File "C:\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)
  File "C:/Users/MDIC/Desktop/Face Recognition With CNN.py", line 221, in <module>
    plt.plot(epochs, val_acc)
  File "C:\Anaconda3\lib\site-packages\matplotlib\pyplot.py", line 2811, in plot
    is not None else ), **kwargs)
  File "C:\Anaconda3\lib\site-packages\matplotlib\__init__.py", line 1810, in inner
    return func(ax, *args, **kwargs)
  File "C:\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py", line 1611, in plot
    for line in self._get_lines(*args, **kwargs):
  File "C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py", line 393, in _grab_next_args
    yield from self._plot_args(this, kwargs)
  File "C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py", line 370, in _plot_args
    x, y = self._xy_from_xy(x, y)
  File "C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py", line 231, in _xy_from_xy
    "have shapes  and ".format(x.shape, y.shape))
ValueError: x and y must have same first dimension, but have shapes (2,) and (1,)

**

这是我的编码---

# Importing libraries
from matplotlib import pyplot as plt
from tensorflow.keras.preprocessing.image import array_to_img, img_to_array, load_img
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.image as mpimg
import numpy as np
import os

# Preparing dataset
# Setting names of the directies for both sets
base_dir = 'data'
seta ='Man_One'
setb ='Man_Two'

# Each of the sets has three sub directories train, validation and test
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')

def prepare_data(base_dir, seta, setb):
# Take the directory names for the base directory and both the sets 
# Returns the paths for train, validation for each of the sets
    seta_train_dir = os.path.join(train_dir, seta)
    setb_train_dir = os.path.join(train_dir, setb)

    seta_valid_dir = os.path.join(validation_dir, seta)
    setb_valid_dir = os.path.join(validation_dir, setb)

    seta_train_fnames = os.listdir(seta_train_dir)
    setb_train_fnames = os.listdir(setb_train_dir)

    return seta_train_dir, setb_train_dir, seta_valid_dir, setb_valid_dir, seta_train_fnames, setb_train_fnames

seta_train_dir, setb_train_dir, seta_valid_dir, setb_valid_dir, seta_train_fnames, setb_train_fnames = prepare_data(base_dir, seta, setb)

seta_test_dir = os.path.join(test_dir, seta)
setb_test_dir = os.path.join(test_dir, setb)
test_fnames_seta = os.listdir(seta_test_dir)
test_fnames_setb = os.listdir(setb_test_dir)

datagen = ImageDataGenerator( 
          height_shift_range = 0.2,
          width_shift_range = 0.2,
          rotation_range = 40,
          shear_range = 0.2,
          zoom_range = 0.2,
          horizontal_flip = True,
          fill_mode = 'nearest')

img_path = os.path.join(seta_train_dir, seta_train_fnames[3])
img = load_img(img_path, target_size = (150, 150))
x = img_to_array(img)
x = x.reshape((1,) + x.shape)

i = 0
for batch in datagen.flow(x, batch_size = 1):
    plt.figure(i)
    imgplot = plt.imshow(array_to_img(batch[0]))
    i += 1
    if i % 5 == 0:
        break
        
# Convolutional Neural Network model
# Import TensorFlow libraries
from tensorflow.keras import layers
from tensorflow.keras import Model

img_input = layers.Input(shape = (150, 150, 3))        

# 2D Convolution layer with 64 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(64, 3, activation = 'relu')(img_input)
# 2D max pooling layer
x = layers.MaxPooling2D(2)(x)

# 2D Convolution layer with 128 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(128, 3, activation = 'relu')(x)
# 2D Max pooling layer
x = layers.MaxPooling2D(2)(x)

# 2D Convolution layer with 256 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(256, 3, activation = 'relu')(x)
# 2D Max pooling layer
x = layers.MaxPooling2D(2)(x)

# 2D Convolution layer with 512 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(512, 3, activation = 'relu')(x)
# 2D Max pooling layer
x = layers.MaxPooling2D(2)(x)

# 2D Convolution layer with 512 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(512, 3, activation = 'relu')(x)
# Flatten layer
x = layers.Flatten()(x)

# Fully connected layers and ReLU activation algorithm
x = layers.Dense(1024, activation = 'relu')(x)
x = layers.Dense(1024, activation = 'relu')(x)
x = layers.Dense(1000, activation = 'relu')(x)

# Dropout layers for optimisation
x = layers.Dropout(0.5)(x)

# Fully connected layers and sigmoid activation algorithm
output = layers.Dense(1, activation = 'sigmoid')(x)

model = Model(img_input, output)

model.summary()

import tensorflow as tf
# Using binary_crossentropy as the loss function and
# Adam optimizer as the optimizing function when training
model.compile(loss = 'binary_crossentropy',
              optimizer = tf.optimizers.Adam(learning_rate = 0.0005),
              metrics = ['acc'])
from tensorflow.keras.preprocessing.image import ImageDataGenerator            

# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen = ImageDataGenerator(rescale = 1./255)

# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
                  train_dir,
                  target_size = (150, 150),
                  batch_size = 20,
                  class_mode = 'binary')

validation_generator = test_datagen.flow_from_directory(
                       validation_dir,
                       target_size = (150, 150),
                       batch_size = 20,
                       class_mode = 'binary')

# 4x4 grid
ncols = 5
nrows = 5

pic_index = 0

# Set up matpotlib fig and size it to fit 5x5 pics
fig = plt.gcf()
fig.set_size_inches(ncols * 5, nrows * 5)

pic_index += 10
next_seta_pix = [os.path.join(seta_train_dir, fname)
                 for fname in seta_train_fnames[pic_index - 10:pic_index]]
next_setb_pix = [os.path.join(setb_train_dir, fname)
                 for fname in setb_train_fnames[pic_index - 10:pic_index]]

for i, img_path in enumerate(next_seta_pix + next_setb_pix):
# Set up subplot; subplot indices start at 1
    sp = plt.subplot(nrows, ncols, i + 1)
    sp.axis('Off')

    img = mpimg.imread(img_path)
    plt.imshow(img)
    
plt.show()

# Train the model
mymodel = model.fit_generator(
          train_generator,
          steps_per_epoch = 10,
          epochs = 80,
          validation_data = validation_generator,
          validation_steps = 7,
          verbose = 2)

import random
from tensorflow.keras.preprocessing.image import img_to_array, load_img

successive_outputs = [layer.output for layer in model.layers[1:]]
visualization_model = Model(img_input, successive_outputs)

a_img_files = [os.path.join(seta_train_dir, f) for f in seta_train_fnames]
b_img_files = [os.path.join(setb_train_dir, f) for f in setb_train_fnames]
img_path = random.choice(a_img_files + b_img_files)

img = load_img(img_path, target_size = (150, 150))
x = img_to_array(img)
x = x.reshape((1,) + x.shape)

x /= 255

successive_feature_maps = visualization_model.predict(x)

layer_names = [layer.name for layer in model.layers]

for layer_name, feature_map in zip(layer_names, successive_feature_maps):
    if len(feature_map.shape) == 4:
# Just do this for the conv/maxpool layers
        n_features = feature_map.shape[-1]
# The feature map has shape(1, size, size, n_features)
        size = feature_map.shape[1]
# Will tile images in this matrix
        display_grid = np.zeros((size, size * n_features))
        for i in range(n_features):
# Postprocess the feature           
            x = feature_map[0, :, :, i]
            x -= x.mean()
            x *= 64
            x += 128
            x = np.clip(x, 0, 255).astype('float32')
# Will tile each filter into this big horizontal grid
            display_grid[:, i * size : (i + 1) * size] = x 
    
# Accuracy results for each training and validation epoch
acc = mymodel.history['acc']
val_acc = mymodel.history['val_acc']

# Loss results for each training and validation epoch
loss = mymodel.history['loss']
val_loss = mymodel.history['val_loss']

epochs = range(len(acc))

# Plot accuracy for each training and validation epoch
plt.plot(epochs, acc)
plt.plot(epochs, val_acc)
plt.title('Training and validation accuracy')
plt.legend(['train', 'val'], loc='center')
plt.figure()

# Plot loss for each training and validation epoch
plt.plot(epochs, loss)
plt.plot(epochs, val_loss)
plt.title('Training and validation loss')
plt.legend(['train', 'val'], loc='center')
plt.figure()

# Testing model on a random train image from set a

train_img = random.choice(seta_train_fnames)
train_image_path = os.path.join(seta_train_dir, train_img)
train_img = load_img(train_image_path, target_size = (150, 150))
plt.figure()
plt.imshow(train_img)
train_img = (np.expand_dims(train_img, 0))
train_img = tf.cast(train_img, tf.float32)
print(train_img.shape)

model.predict(train_img)

# Testing model on a random train image from set b

train_img = random.choice(setb_train_fnames)
train_image_path = os.path.join(setb_train_dir, train_img)
train_img = load_img(train_image_path, target_size = (150, 150))
plt.figure()
plt.imshow(train_img)
train_img = (np.expand_dims(train_img, 0))
train_img = tf.cast(train_img, tf.float32)
print(train_img.shape)

model.predict(train_img)

# Testing a random image from the test set a 

cal_mo = 0
cal_mt = 0
cal_unconclusive = 0
alist = []
for fname in test_fnames_seta:
    if fname.startswith('.'):
        continue
    file_path = os.path.join(seta_test_dir, fname)
    load_file = load_img(file_path, target_size = (150, 150))
    load_file = (np.expand_dims(load_file, 0))
    load_file = tf.cast(load_file, tf.float32)
    pred_img = model.predict(load_file)
    if(pred_img[0]<0.5):
        cal_mo+=1
    elif(pred_img[0]>0.5):
        cal_mt+=1
    else:
        print(pred_img[0], "\n")
        cal_unconclusive+=1
        alist.append(file_path)
print(alist)

print("Identified as: \n")
print("Man_One:", cal_mo)
print("Man_Two:", cal_mt)
print( "Inconclusive:", cal_unconclusive)
print( "Percentage:", (cal_mo/(cal_mo + cal_mt + cal_unconclusive)) * 100)
a =  (cal_mo/(cal_mo + cal_mt + cal_unconclusive)) * 100
   
# Testing a random image from the test set b

cal_mo = 0
cal_mt = 0
cal_unconclusive = 0
alist = []
for fname in test_fnames_setb:
    if fname.startswith('.'):
        continue
    file_path = os.path.join(setb_test_dir, fname)
    load_file = load_img(file_path, target_size = (150, 150))
    load_file = (np.expand_dims(load_file, 0))
    load_file = tf.cast(load_file, tf.float32)
    pred_img = model.predict(load_file)
    if(pred_img[0]<0.5):
        cal_mo+=1
    elif(pred_img[0]>0.5):
        cal_mt+=1
    else:
        print(pred_img[0], "\n")
        cal_unconclusive+=1
        alist.append(file_path)
print(alist)

print("Identified as: \n")
print("Man_One:", cal_mo)
print("Man_Two:", cal_mt)
print( "Inconclusive:", cal_unconclusive)
print( "Percentage:", (cal_mt/(cal_mo + cal_mt + cal_unconclusive)) * 100)
b =  (cal_mt/(cal_mo + cal_mt + cal_unconclusive)) * 100             

avg = (a+b)/2
print("Average Percentage:", avg)

上面的程序有点长，请仔细看

请尽快帮助我

非常感谢

【参考方案1】：

您的验证生成数据可能在达到 80 个训练阶段之前就终止了。检查您是否至少有7*80 验证图像。

然后检查您的：mymodel.history['val_acc'] 中的元素数量。如果您使用 epochs = range(len(acc)) 作为图表的 x 值，则训练和验证必须相同。问题是你的acc 和val_acc 有不同数量的元素。

【讨论】：

我的数据如下 --- [Man_One 的训练样本 = 100，Man_Two 的训练样本 = 100] [Man_One 的验证样本 = 20，Man_Two 的验证样本 = 20] [测试样本 = Man_One 和测试样本 30 = Man_Two 30] 不确定哪个部分不正确 -------------

一年前的编程还行，没问题，还写进了我的硕士论文---今天突然报这个错误

如果我想将训练样本 = 100 并将验证样本 = 30 那么如何为上述编写正确的编程代码---请帮助---

如果您只有 200 个用于训练的样本和 40 个用于验证的样本，并且您的 batch_size 为 20，那么您应该将 steps_per_epoch 参数设置为值

能否请您根据您的想法写出代码或语句，以便于理解