Keras CNN用于多类别分类交叉熵损失函数

Question

这是我输入的用于分类由鸟类，狗和猫组成的类的代码。它与二进制分类的代码相同，但是当我添加另一个类并将编译方法的损失函数更改为使用categorical_Crossentropy时，它给了我以下错误（=在代码结尾处）。任何人都可以解释这里有什么问题或我犯的错误吗？

# Importing Keras and Tensorflow modules 

import tensorflow as tf 
from keras.models import Sequential 
from keras.layers import Conv2D 
from keras.layers import MaxPooling2D 
from keras.layers import Flatten 
from keras.layers import Dense 
from keras.preprocessing.image import ImageDataGenerator 
from keras.models import load_model 
from keras.utils.np_utils import to_categorical 
import os.path 

# Initilize the CNN 

classifier = Sequential() 
# Step 1 - Convolution 

classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu')) 

# Step 2 - Pooling 

classifier.add(MaxPooling2D(pool_size = (2, 2))) 

# Step 2(b) - Add 2nd Convolution Layer making it Deep followed by a Pooling Layer 

classifier.add(Conv2D(32, (3, 3), activation = 'relu')) 
classifier.add(MaxPooling2D(pool_size = (2, 2))) 

# Step 3 - Flattening 

classifier.add(Flatten()) 

# Step 4 - Fully Connected Neural Network 

# Hidden Layer - Activation Function RELU 
classifier.add(Dense(units = 128, activation = 'relu')) 
# Output Layer - Activation Function Softmax(to clasify multiple classes) 
classifier.add(Dense(units = 1, activation = 'softmax')) 

# Compile the CNN 

# Categorical Crossentropy - to classify between multiple classes of images 
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', 
metrics = ['accuracy']) 

# Image Augmentation and Training Section 

# Image Augmentation to prevent Overfitting (Applying random transformation on 
images to train set.ie. 
# scalling, rotating and streching) 

train_datagen = ImageDataGenerator(
     rescale=1./255, 
     shear_range=0.2, 
     zoom_range=0.2, 
     horizontal_flip=True) 

test_datagen = ImageDataGenerator(rescale=1./255) 

training_set = train_datagen.flow_from_directory(
     'dataset/training_set', 
     target_size=(64, 64), 
     batch_size=8, 
     class_mode='categorical') 

test_set = test_datagen.flow_from_directory(
     'dataset/test_set', 
     target_size=(64, 64), 
     batch_size=8, 
     class_mode='categorical') 

#Fit the clasifier on the CNN data 
if(os.path.isfile('my_model.h5') == False): 
    classifier.fit_generator(
      training_set, 
      steps_per_epoch=8000, 
      epochs=2, 
      validation_data=test_set, 
      validation_steps=2000 
           ) 
# Save the generated model to my_model.h5 
classifier.save('my_model.h5') 
else: 
    classifier = load_model('my_model.h5') 

Answer 1

你的数据集似乎有3个班，所以你需要在模型定义改变最后一行：

classifier.add(Dense(units = 3, activation = 'softmax')) 

Answer 2

您必须每班一个神经元最后一次（密集）层上。

classifier.add(Dense(3)) 

现在你只有一个神经元，并且由此你的网络仍然只设置了两个类。