Tensorflow实战07-迁移学习

阳光穿透心脏的1/2处 2023-03-02 05:11 14阅读 0赞

> ##### 注：学习完深度学习的基本知识后，本练习可帮助大家逐步从理论转向实战，如果对深度学习还不太了解，前先学习深度学习的基本知识以及原理 #####

#### 在下方有空的地方填写代码并检验： ####

# Import all the necessary files!
    import os
    import tensorflow as tf
    from tensorflow.keras import layers
    from tensorflow.keras import Model

# Download the inception v3 weights
    !wget --no-check-certificate \
        https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \
        -O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
    
    # Import the inception model  
    from tensorflow.keras.applications.inception_v3 import InceptionV3
    
    # Create an instance of the inception model from the local pre-trained weights
    local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
    
    pre_trained_model = # Your Code Here
    
    pre_trained_model.load_weights(local_weights_file)
    
    # Make all the layers in the pre-trained model non-trainable
    for layer in pre_trained_model.layers:
      # Your Code Here
      
    # Print the model summary
    pre_trained_model.summary()
    
    # Expected Output is extremely large, but should end with:
    
    #batch_normalization_v1_281 (Bat (None, 3, 3, 192)    576         conv2d_281[0][0]                 
    #__________________________________________________________________________________________________
    #activation_273 (Activation)     (None, 3, 3, 320)    0           batch_normalization_v1_273[0][0] 
    #__________________________________________________________________________________________________
    #mixed9_1 (Concatenate)          (None, 3, 3, 768)    0           activation_275[0][0]             
    #                                                                 activation_276[0][0]             
    #__________________________________________________________________________________________________
    #concatenate_5 (Concatenate)     (None, 3, 3, 768)    0           activation_279[0][0]             
    #                                                                 activation_280[0][0]             
    #__________________________________________________________________________________________________
    #activation_281 (Activation)     (None, 3, 3, 192)    0           batch_normalization_v1_281[0][0] 
    #__________________________________________________________________________________________________
    #mixed10 (Concatenate)           (None, 3, 3, 2048)   0           activation_273[0][0]             
    #                                                                 mixed9_1[0][0]                   
    #                                                                 concatenate_5[0][0]              
    #                                                                 activation_281[0][0]             
    #==================================================================================================
    #Total params: 21,802,784
    #Trainable params: 0
    #Non-trainable params: 21,802,784

last_layer = pre_trained_model.get_layer(# Your Code Here)
    print('last layer output shape: ', last_layer.output_shape)
    last_output = # Your Code Here
    
    # Expected Output:
    # ('last layer output shape: ', (None, 7, 7, 768))

# Define a Callback class that stops training once accuracy reaches 99.9%
    class myCallback(tf.keras.callbacks.Callback):
      def on_epoch_end(self, epoch, logs={}):
        if(logs.get('acc')>0.999):
          print("\nReached 99.9% accuracy so cancelling training!")
          self.model.stop_training = True

from tensorflow.keras.optimizers import RMSprop
    
    # Flatten the output layer to 1 dimension
    x = layers.Flatten()(last_output)
    # Add a fully connected layer with 1,024 hidden units and ReLU activation
    x = layers.Dense(# Your Code Here)(x)
    # Add a dropout rate of 0.2
    x = layers.Dropout(# Your Code Here)(x)                  
    # Add a final sigmoid layer for classification
    x = layers.Dense  (# Your Code Here)(x)           
    
    model = Model( # Your Code Here, x) 
    
    model.compile(optimizer = RMSprop(lr=0.0001), 
                  loss = # Your Code Here, 
                  metrics = # Your Code Here)
    
    model.summary()
    
    # Expected output will be large. Last few lines should be:
    
    # mixed7 (Concatenate)            (None, 7, 7, 768)    0           activation_248[0][0]             
    #                                                                  activation_251[0][0]             
    #                                                                  activation_256[0][0]             
    #                                                                  activation_257[0][0]             
    # __________________________________________________________________________________________________
    # flatten_4 (Flatten)             (None, 37632)        0           mixed7[0][0]                     
    # __________________________________________________________________________________________________
    # dense_8 (Dense)                 (None, 1024)         38536192    flatten_4[0][0]                  
    # __________________________________________________________________________________________________
    # dropout_4 (Dropout)             (None, 1024)         0           dense_8[0][0]                    
    # __________________________________________________________________________________________________
    # dense_9 (Dense)                 (None, 1)            1025        dropout_4[0][0]                  
    # ==================================================================================================
    # Total params: 47,512,481
    # Trainable params: 38,537,217
    # Non-trainable params: 8,975,264

# Get the Horse or Human dataset
    !wget --no-check-certificate https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip -O /tmp/horse-or-human.zip
    
    # Get the Horse or Human Validation dataset
    !wget --no-check-certificate https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip -O /tmp/validation-horse-or-human.zip 
      
    from tensorflow.keras.preprocessing.image import ImageDataGenerator
    
    import os
    import zipfile
    
    local_zip = '//tmp/horse-or-human.zip'
    zip_ref = zipfile.ZipFile(local_zip, 'r')
    zip_ref.extractall('/tmp/training')
    zip_ref.close()
    
    local_zip = '//tmp/validation-horse-or-human.zip'
    zip_ref = zipfile.ZipFile(local_zip, 'r')
    zip_ref.extractall('/tmp/validation')
    zip_ref.close()

train_horses_dir = # Your Code Here
    train_humans_dir = # Your Code Here
    validation_horses_dir = # Your Code Here
    validation_humans_dir = # Your Code Here
    
    train_horses_fnames = # Your Code Here
    train_humans_fnames = # Your Code Here
    validation_horses_fnames = # Your Code Here
    validation_humans_fnames = # Your Code Here
    
    print(# Your Code Here)
    print(# Your Code Here)
    print(# Your Code Here)
    print(# Your Code Here)
    
    # Expected Output:
    # 500
    # 527
    # 128
    # 128

# Define our example directories and files
    train_dir = '/tmp/training'
    validation_dir = '/tmp/validation'
    
    # Add our data-augmentation parameters to ImageDataGenerator
    train_datagen = ImageDataGenerator(# Your Code Here)
    
    # Note that the validation data should not be augmented!
    test_datagen = ImageDataGenerator(# Your Code Here )
    
    # Flow training images in batches of 20 using train_datagen generator
    train_generator = train_datagen.flow_from_directory(# Your Code Here)     
    
    # Flow validation images in batches of 20 using test_datagen generator
    validation_generator =  test_datagen.flow_from_directory( # Your Code Here)
    
    # Expected Output:
    # Found 1027 images belonging to 2 classes.
    # Found 256 images belonging to 2 classes.

# Run this and see how many epochs it should take before the callback
    # fires, and stops training at 99.9% accuracy
    # (It should take less than 100 epochs)
    
    callbacks = # Your Code Here
    history = model.fit_generator(# Your Code Here)

import matplotlib.pyplot as plt
    acc = history.history['acc']
    val_acc = history.history['val_acc']
    loss = history.history['loss']
    val_loss = history.history['val_loss']
    
    epochs = range(len(acc))
    
    plt.plot(epochs, acc, 'r', label='Training accuracy')
    plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
    plt.title('Training and validation accuracy')
    plt.legend(loc=0)
    plt.figure()
    
    
    plt.show()

#### 参考答案： ####

# Import all the necessary files!
    import os
    import tensorflow as tf
    from tensorflow.keras import layers
    from tensorflow.keras import Model

# Download the inception v3 weights
    !wget --no-check-certificate \
        https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \
        -O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
    
    # Import the inception model  
    from tensorflow.keras.applications.inception_v3 import InceptionV3
    
    # Create an instance of the inception model from the local pre-trained weights
    local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
    
    pre_trained_model = InceptionV3(input_shape = (150, 150, 3), 
                                    include_top = False, 
                                    weights = None)
    
    pre_trained_model.load_weights(local_weights_file)
    
    # Make all the layers in the pre-trained model non-trainable
    for layer in pre_trained_model.layers:
      layer.trainable = False
      
    # Print the model summary
    pre_trained_model.summary()
    
    # Expected Output is extremely large, but should end with:
    
    #batch_normalization_v1_281 (Bat (None, 3, 3, 192)    576         conv2d_281[0][0]                 
    #__________________________________________________________________________________________________
    #activation_273 (Activation)     (None, 3, 3, 320)    0           batch_normalization_v1_273[0][0] 
    #__________________________________________________________________________________________________
    #mixed9_1 (Concatenate)          (None, 3, 3, 768)    0           activation_275[0][0]             
    #                                                                 activation_276[0][0]             
    #__________________________________________________________________________________________________
    #concatenate_5 (Concatenate)     (None, 3, 3, 768)    0           activation_279[0][0]             
    #                                                                 activation_280[0][0]             
    #__________________________________________________________________________________________________
    #activation_281 (Activation)     (None, 3, 3, 192)    0           batch_normalization_v1_281[0][0] 
    #__________________________________________________________________________________________________
    #mixed10 (Concatenate)           (None, 3, 3, 2048)   0           activation_273[0][0]             
    #                                                                 mixed9_1[0][0]                   
    #                                                                 concatenate_5[0][0]              
    #                                                                 activation_281[0][0]             
    #==================================================================================================
    #Total params: 21,802,784
    #Trainable params: 0
    #Non-trainable params: 21,802,784

last_layer = pre_trained_model.get_layer('mixed7')
    print('last layer output shape: ', last_layer.output_shape)
    last_output = last_layer.output
    
    # Expected Output:
    # ('last layer output shape: ', (None, 7, 7, 768))

from tensorflow.keras.optimizers import RMSprop
    
    # Flatten the output layer to 1 dimension
    x = layers.Flatten()(last_output)
    # Add a fully connected layer with 1,024 hidden units and ReLU activation
    x = layers.Dense(1024, activation='relu')(x)
    # Add a dropout rate of 0.2
    x = layers.Dropout(0.2)(x)                  
    # Add a final sigmoid layer for classification
    x = layers.Dense  (1, activation='sigmoid')(x)           
    
    model = Model( pre_trained_model.input, x) 
    
    model.compile(optimizer = RMSprop(lr=0.0001), 
                  loss = 'binary_crossentropy', 
                  metrics = ['acc'])
    
    model.summary()
    
    # Expected output will be large. Last few lines should be:
    
    # mixed7 (Concatenate)            (None, 7, 7, 768)    0           activation_248[0][0]             
    #                                                                  activation_251[0][0]             
    #                                                                  activation_256[0][0]             
    #                                                                  activation_257[0][0]             
    # __________________________________________________________________________________________________
    # flatten_4 (Flatten)             (None, 37632)        0           mixed7[0][0]                     
    # __________________________________________________________________________________________________
    # dense_8 (Dense)                 (None, 1024)         38536192    flatten_4[0][0]                  
    # __________________________________________________________________________________________________
    # dropout_4 (Dropout)             (None, 1024)         0           dense_8[0][0]                    
    # __________________________________________________________________________________________________
    # dense_9 (Dense)                 (None, 1)            1025        dropout_4[0][0]                  
    # ==================================================================================================
    # Total params: 47,512,481
    # Trainable params: 38,537,217
    # Non-trainable params: 8,975,264

print(len(os.listdir('/tmp/cats-v-dogs/training/cats/')))
    print(len(os.listdir('/tmp/cats-v-dogs/training/dogs/')))
    print(len(os.listdir('/tmp/cats-v-dogs/testing/cats/')))
    print(len(os.listdir('/tmp/cats-v-dogs/testing/dogs/')))
    
    # Expected output:
    # 11250
    # 11250
    # 1250
    # 1250

train_horses_dir = os.path.join(train_dir, 'horses') # Directory with our training horse pictures
    train_humans_dir = os.path.join(train_dir, 'humans') # Directory with our training humans pictures
    validation_horses_dir = os.path.join(validation_dir, 'horses') # Directory with our validation horse pictures
    validation_humans_dir = os.path.join(validation_dir, 'humans')# Directory with our validation humanas pictures
    
    train_horses_fnames = os.listdir(train_horses_dir)
    train_humans_fnames = os.listdir(train_humans_dir)
    validation_horses_fnames = os.listdir(validation_horses_dir)
    validation_humans_fnames = os.listdir(validation_humans_dir)
    
    print(len(train_horses_fnames))
    print(len(train_humans_fnames))
    print(len(validation_horses_fnames))
    print(len(validation_humans_fnames))
    
    # Expected Output:
    # 500
    # 527
    # 128
    # 128

# Define our example directories and files
    train_dir = '/tmp/training'
    validation_dir = '/tmp/validation'
    
    # Add our data-augmentation parameters to ImageDataGenerator
    train_datagen = ImageDataGenerator(rescale = 1./255.,
                                       rotation_range = 40,
                                       width_shift_range = 0.2,
                                       height_shift_range = 0.2,
                                       shear_range = 0.2,
                                       zoom_range = 0.2,
                                       horizontal_flip = True)
    
    # Note that the validation data should not be augmented!
    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,
                                                        class_mode = 'binary', 
                                                        target_size = (150, 150))     
    
    # Flow validation images in batches of 20 using test_datagen generator
    validation_generator =  test_datagen.flow_from_directory( validation_dir,
                                                              batch_size  = 20,
                                                              class_mode  = 'binary', 
                                                              target_size = (150, 150))
    
    # Expected Output:
    # Found 1027 images belonging to 2 classes.
    # Found 256 images belonging to 2 classes.

# Run this and see how many epochs it should take before the callback
    # fires, and stops training at 99.9% accuracy
    # (It should take less than 100 epochs)
    callbacks = myCallback()
    history = model.fit_generator(
                train_generator,
                validation_data = validation_generator,
                steps_per_epoch = 100,
                epochs = 100,
                validation_steps = 50,
                verbose = 2,
                callbacks=[callbacks])