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  • 函数式API简介

    函数式API简介

    导入相关库以及数据加载

    相关库导入:

    import tensorflow as tf
    from tensorflow import keras
    import matplotlib.pyplot as plt
    %matplotlib inline
    

    数据加载:

    fashion_mnist = keras.datasets.fashion_mnist
    (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
    

    数据归一化:

    train_images = train_images / 255.0
    test_images = test_images / 255.0
    

    函数式定义模型

    输入:

    input = keras.Input(shape = (28, 28))
    

    这里的意思就是可以传任意28*28的数据

    模型定义:

    x = keras.layers.Flatten()(input)
    x = keras.layers.Dense(32, activation = 'relu')(x)
    x = keras.layers.Dropout(0.5)(x)
    x = keras.layers.Dense(64, activation = 'relu')(x)
    

    输出:

    output = keras.layers.Dense(10, activation = 'softmax')(x)
    

    构建模型:

    model = keras.Model(inputs = input, outputs = output)
    model.summary()
    

    模型编译

    model.compile(
        optimizer = 'adam',
        loss      = 'sparse_categorical_crossentropy',
        metrics   = ['acc']
    )
    

    模型训练

    history = model.fit(
        train_images,
        train_labels,
        epochs = 30,
        validation_data = (test_images, test_labels)
    )
    
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  • 原文地址:https://www.cnblogs.com/miraclepbc/p/14312152.html
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