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  • 机器学习进阶-案例实战-停车场车位识别-keras预测是否停车站有车

    import numpy
    import os
    
    from keras import applications
    from keras.preprocessing.image import ImageDataGenerator
    from keras import optimizers
    from keras.models import Sequential, Model
    from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
    from keras import backend as k
    from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping
    from keras.models import Sequential
    from keras.layers.normalization import BatchNormalization
    from keras.layers.convolutional import Conv2D
    from keras.layers.convolutional import MaxPooling2D
    from keras.initializers import TruncatedNormal
    from keras.layers.core import Activation
    from keras.layers.core import Flatten
    from keras.layers.core import Dropout
    from keras.layers.core import Dense
    
    
    files_train = 0
    files_validation = 0
    
    cwd = os.getcwd()
    folder = 'train_data/train'
    for sub_folder in os.listdir(folder):
        path, dirs, files = next(os.walk(os.path.join(folder,sub_folder)))
        files_train += len(files)
    
    
    folder = 'train_data/test'
    for sub_folder in os.listdir(folder):
        path, dirs, files = next(os.walk(os.path.join(folder,sub_folder)))
        files_validation += len(files)
    
    print(files_train,files_validation)
    
    img_width, img_height = 48, 48
    train_data_dir = "train_data/train"
    validation_data_dir = "train_data/test"
    nb_train_samples = files_train
    nb_validation_samples = files_validation
    batch_size = 32
    epochs = 15
    num_classes = 2
    
    model = applications.VGG16(weights='imagenet', include_top=False, input_shape = (img_width, img_height, 3))
    
    
    for layer in model.layers[:10]:
        layer.trainable = False
    
    
    x = model.output
    x = Flatten()(x)
    predictions = Dense(num_classes, activation="softmax")(x)
    
    
    model_final = Model(input = model.input, output = predictions)
    
    
    model_final.compile(loss = "categorical_crossentropy", 
                        optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), 
                        metrics=["accuracy"]) 
    
    
    train_datagen = ImageDataGenerator(
    rescale = 1./255,
    horizontal_flip = True,
    fill_mode = "nearest",
    zoom_range = 0.1,
    width_shift_range = 0.1,
    height_shift_range=0.1,
    rotation_range=5)
    
    test_datagen = ImageDataGenerator(
    rescale = 1./255,
    horizontal_flip = True,
    fill_mode = "nearest",
    zoom_range = 0.1,
    width_shift_range = 0.1,
    height_shift_range=0.1,
    rotation_range=5)
    
    train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size = (img_height, img_width),
    batch_size = batch_size,
    class_mode = "categorical")
    
    validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size = (img_height, img_width),
    class_mode = "categorical")
    
    checkpoint = ModelCheckpoint("car1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
    early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')
    
    
    
    
    history_object = model_final.fit_generator(
    train_generator,
    samples_per_epoch = nb_train_samples,
    epochs = epochs,
    validation_data = validation_generator,
    nb_val_samples = nb_validation_samples,
    callbacks = [checkpoint, early])
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  • 原文地址:https://www.cnblogs.com/my-love-is-python/p/10435794.html
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