在 keras 中，model.predict() 的結果是什么以及加載模型的預測問題

Question

（Python 3.7、tensorflow 2.3、keras 2.4.3）

我用 resnet 50 做了一個分類器（在 keras 中有功能 api）。 我訓練、保存並加載了 model。 我想用一張圖片查看預測的概率，所以我使用了 model.predict() 方法。

我以為 model.predict() 的結果是預測的概率，但結果是這樣的

[[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]

另一個問題是結果是錯誤的，即使我用另一張圖片進行測試，結果也總是一樣。

我想知道是否有任何其他方法可以獲得預測概率，為什么我的 model 總是得到相同的預測以及我的代碼有任何問題。

此代碼用於訓練和保存 model

import  keras
from keras import models, layers
from keras import Input
from keras.models import Model, load_model
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers, initializers, regularizers, metrics
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.layers import BatchNormalization, Conv2D, Activation, Dense, GlobalAveragePooling2D, MaxPooling2D, ZeroPadding2D, Add
from tensorflow.python.client import device_lib
 
import os
import matplotlib.pyplot as plt
import numpy as np
import math

train_datagen = ImageDataGenerator(rescale = 1./255)

train_dir = os.path.join(r'C:\Users\Admin\Desktop\deeplearning\아주대 고양이 판별기(resnet-18\data\trainset')
 
train_generator = train_datagen.flow_from_directory(train_dir, batch_size=16, target_size=(224, 224), color_mode='rgb')

# number of classes
K = 10

input_tensor = Input(shape=(224, 224, 3), dtype='float32', name='input')
 
 
def conv1_layer(x):    
    x = ZeroPadding2D(padding=(3, 3))(x)
    x = Conv2D(64, (7, 7), strides=(2, 2))(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = ZeroPadding2D(padding=(1,1))(x)
 
    return x   
 
def conv2_layer(x):         
    x = MaxPooling2D((3, 3), 2)(x)     
 
    shortcut = x
 
    for i in range(3):
        if (i == 0):
            x = Conv2D(64, (1, 1), strides=(1, 1), padding='valid')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
            
            x = Conv2D(64, (3, 3), strides=(1, 1), padding='same')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
 
            x = Conv2D(256, (1, 1), strides=(1, 1), padding='valid')(x)
            shortcut = Conv2D(256, (1, 1), strides=(1, 1), padding='valid')(shortcut)            
            x = BatchNormalization()(x)
            shortcut = BatchNormalization()(shortcut)
 
            x = Add()([x, shortcut])
            x = Activation('relu')(x)
            
            shortcut = x
 
        else:
            x = Conv2D(64, (1, 1), strides=(1, 1), padding='valid')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
            
            x = Conv2D(64, (3, 3), strides=(1, 1), padding='same')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
 
            x = Conv2D(256, (1, 1), strides=(1, 1), padding='valid')(x)
            x = BatchNormalization()(x)            
 
            x = Add()([x, shortcut])   
            x = Activation('relu')(x)  
 
            shortcut = x        
    
    return x
 
def conv3_layer(x):        
    shortcut = x    
    
    for i in range(4):     
        if(i == 0):            
            x = Conv2D(128, (1, 1), strides=(2, 2), padding='valid')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)        
            
            x = Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)  
 
            x = Conv2D(512, (1, 1), strides=(1, 1), padding='valid')(x)
            shortcut = Conv2D(512, (1, 1), strides=(2, 2), padding='valid')(shortcut)
            x = BatchNormalization()(x)
            shortcut = BatchNormalization()(shortcut)            
 
            x = Add()([x, shortcut])    
            x = Activation('relu')(x)    
 
            shortcut = x              
        
        else:
            x = Conv2D(128, (1, 1), strides=(1, 1), padding='valid')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
            
            x = Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
 
            x = Conv2D(512, (1, 1), strides=(1, 1), padding='valid')(x)
            x = BatchNormalization()(x)            
 
            x = Add()([x, shortcut])     
            x = Activation('relu')(x)
 
            shortcut = x      
            
    return x

def conv4_layer(x):
    shortcut = x        
  
    for i in range(6):     
        if(i == 0):            
            x = Conv2D(256, (1, 1), strides=(2, 2), padding='valid')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)        
            
            x = Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)  
 
            x = Conv2D(1024, (1, 1), strides=(1, 1), padding='valid')(x)
            shortcut = Conv2D(1024, (1, 1), strides=(2, 2), padding='valid')(shortcut)
            x = BatchNormalization()(x)
            shortcut = BatchNormalization()(shortcut)
 
            x = Add()([x, shortcut]) 
            x = Activation('relu')(x)
 
            shortcut = x               
        
        else:
            x = Conv2D(256, (1, 1), strides=(1, 1), padding='valid')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
            
            x = Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
 
            x = Conv2D(1024, (1, 1), strides=(1, 1), padding='valid')(x)
            x = BatchNormalization()(x)            
 
            x = Add()([x, shortcut])    
            x = Activation('relu')(x)
 
            shortcut = x      
 
    return x

def conv5_layer(x):
    shortcut = x    
  
    for i in range(3):     
        if(i == 0):            
            x = Conv2D(512, (1, 1), strides=(2, 2), padding='valid')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)        
            
            x = Conv2D(512, (3, 3), strides=(1, 1), padding='same')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)  
 
            x = Conv2D(2048, (1, 1), strides=(1, 1), padding='valid')(x)
            shortcut = Conv2D(2048, (1, 1), strides=(2, 2), padding='valid')(shortcut)
            x = BatchNormalization()(x)
            shortcut = BatchNormalization()(shortcut)            
 
            x = Add()([x, shortcut])  
            x = Activation('relu')(x)      
 
            shortcut = x               
        
        else:
            x = Conv2D(512, (1, 1), strides=(1, 1), padding='valid')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
            
            x = Conv2D(512, (3, 3), strides=(1, 1), padding='same')(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
 
            x = Conv2D(2048, (1, 1), strides=(1, 1), padding='valid')(x)
            x = BatchNormalization()(x)           
            
            x = Add()([x, shortcut]) 
            x = Activation('relu')(x)       
 
            shortcut = x                  
 
    return x
 
x = conv1_layer(input_tensor)
x = conv2_layer(x)
x = conv3_layer(x)
x = conv4_layer(x)
x = conv5_layer(x)
x = GlobalAveragePooling2D()(x)
output_tensor = Dense(K, activation='softmax')(x)
 
resnet = Model(input_tensor, output_tensor)

resnet.compile(loss = 'categorical_crossentropy', optimizer = 'rmsprop', metrics = ['accuracy'])
    
resnet.fit(train_generator, steps_per_epoch = 11, epochs = 50)



resnet_json = resnet.to_json()
with open("resnet.json", "w") as json_file :
    json_file.write(resnet_json)
print("saved model architecture")
    
resnet.save_weights('weights.h5')

print("saved weights")

這是用於加載 model 並預測

from keras.models import model_from_json, load_model
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras import Input
import matplotlib.pyplot as plt
import os
import numpy as np
import PIL.Image as pilimg


json_file = open("resnet.json", "r")
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("weights.h5")


"""
loaded_model = load_model('resnet-50.h5')
"""


print("loaded model and weights")

x = pilimg.open(r'C:\Users\Admin\Desktop\deeplearning\아주대 고양이 판별기(resnet-18\test\test.jpg')
x = x.resize((224,224))
plt.imshow(x)
plt.show()
x = np.array(x)
x = np.reshape(x, (1, 224, 224, 3))


loaded_model.compile(loss = 'categorical_crossentropy', optimizer = 'rmsprop', metrics = ['accuracy'])

result = loaded_model.predict(x)
print(result)
#print(loaded_model.predict_proba(x))
#print(loaded_model.predict_classes(x))

Answer 1

這是一個預測的概率。 在這種情況下，它恰好對class == np.argmax(result)的預測接近 100%，並且對其他類非常接近 0。

事實上，您可以推斷這些是浮點數（概率）而不是整數，因為它們后面有一個點，例如0. 1.

現在，如果對於所有預測，您得到相同的結果，這就是訓練方面的問題（即，您的 model 可能已經過擬合並且它只預測一個 class 或者您沒有像您應用的那樣對測試圖像應用相同的處理在訓練圖像上，你會得到不正確的結果）。