Inception v3的转移学习返回相同的预测

问题描述

我尝试自定义初始分类。我用猫，狗，人和其他人对猫，狗，人（家庭照片集）和其他人（主要是自然场景）进行分类。我有大约2000只狗，1000只猫，7000个人和3000张图像，其中80:20训练和验证。模型的实质如下。当我训练时，训练精度接近97％，验证精度约为90％。

    import os

    from tensorflow.keras import layers
    from tensorflow.keras import Model
    from tensorflow.keras.optimizers import RMSprop
      
    from tensorflow.keras.applications.inception_v3 import InceptionV3
    
    local_weights_file = 'C:/users/sethr/education/Healthcare/imagedetect/modelimageadvance /inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
    
    pre_trained_model = InceptionV3(input_shape = (150,150,3),include_top = False,weights =  `enter code here`local_weights_file)
    for layer in pre_trained_model.layers:
        layer.trainable = False 
    # pre_trained_model.summary()
    last_layer = pre_trained_model.get_layer('mixed7')
    #print('last layer output shape: ',last_layer.output_shape)
    last_output = last_layer.output
    # Flatten the output layer to 1 dimension
    x = tf.keras.layers.Flatten()(last_output)
    # Add a fully connected layer with 1,024 hidden units and ReLU activation
    x = tf.keras.layers.Dense(512,activation='relu')(x)
    x = tf.keras.layers.Dense(256,activation='relu')(x)
    # Add a dropout rate of 0.2
    x = tf.keras.layers.Dropout(0.2)(x)                  
    # Add a final sigmoid layer for classification
    x = tf.keras.layers.Dense(4,activation='softmax')(x)           
    
    model = Model(pre_trained_model.input,x)
    
    model.compile(optimizer = RMSprop(lr=0.0001),loss = 'categorical_crossentropy',metrics = ['accuracy'])
    
    history = model.fit(train_generator,validation_data = validation_generator,steps_per_epoch = 20,epochs = 20,validation_steps = 25,verbose = 2)
    model.save("dcho.rp5")
    ___________________
    
    import numpy as np
    import cv2
    import glob
    from keras.preprocessing import image
    import matplotlib.image as mpimg
    import matplotlib.pyplot as plt
    
    labels= ["cat","dog","human","other"]
    path='C:/Users/sethr/education/Healthcare/imagedetect/images/*.jpg'
    for fim in glob.glob(path):
    
      # predicting images
      img=image.load_img(fim,target_size=(150,150))
      
      x=image.img_to_array(img)
      x=np.expand_dims(x,axis=0)
      images = np.vstack([x])
      plt.figure()
      plt.imshow(img)
      plt.show()
      classes = model.predict(images,batch_size=10)

________________________________________________________

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# All images will be rescaled by 1./255.
train_datagen = ImageDataGenerator( rescale = 1.0/255. )
test_datagen  = ImageDataGenerator( rescale = 1.0/255. )

# --------------------
# Flow training images in batches of 20 using train_datagen generator
# -------------------- 
train_generator = train_datagen.flow_from_directory(train_dir,batch_size=20,shuffle='True',class_mode='categorical',150)) 
# --------------------

    # Flow validation images in batches of 20 using test_datagen generator
    # --------------------
    validation_generator =  test_datagen.flow_from_directory(validation_dir,target_size = (150,150),)

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问题在于它总是返回人类作为预测。.我和亚当一起玩耍，调整了学习速度，但预测仍然保持不变。任何见识。

解决方法

问题是您在预测图像时没有完成重新缩放部分。

您只是为了训练和验证而进行重新缩放

train_datagen = ImageDataGenerator( rescale = 1.0/255. )
test_datagen  = ImageDataGenerator( rescale = 1.0/255. )
# Note - this test_datagen used for validation

为了获得正确的预测，我们应该执行与训练和验证相同的预处理步骤。所以你需要重新缩放图像。

您可以使用此代码重新缩放图像

x = x* 1.0/255.0

所以正确的预测图像部分如下所示。

    for fim in glob.glob(path):
    
      # predicting images
      img=image.load_img(fim,target_size=(150,150))
      
      x=image.img_to_array(img)
      x=np.expand_dims(x,axis=0)

      # Add this line
      x = x* 1.0/255.0

      images = np.vstack([x])
      plt.figure()
      plt.imshow(img)
      plt.show()
      classes = model.predict(images,batch_size=10)