001-Keras的Sequential 序贯模型

分手后的思念是犯贱 2023-07-24 08:49 11阅读 0赞

## Sequential 序贯模型 ##

序贯模型是多个网络层的线性堆叠，也就是“一条路走到黑”。  
序贯模型是函数式模型的简略版，为最简单的线性、从头到尾的结构顺序，不分叉，是多个网络层的线性堆叠。

from keras.models import Sequential
    from keras.layers import Dense, Activation
    import numpy as np
    np.random.seed(1337)
    
    import matplotlib.pyplot as plt
    
    # # create some data
    X = np.linspace(-1, 1, 200)
    # print(X)
    np.random.shuffle(X)    # randomize the data
    Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, ))
    # plot data
    plt.scatter(X, Y)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzY3MzE1Ng_size_16_color_FFFFFF_t_70]

X_train, Y_train = X[:160], Y[:160]     # first 160 data points
    X_test, Y_test = X[160:], Y[160:]       # last 40 data points
    
    # # build a neural network from the 1st layer to the last layer
    model = Sequential()
    
    model.add(Dense(units=1, input_dim=1)) 
    
    # # choose loss function and optimizing method
    model.compile(loss='mse', optimizer='sgd')
    
    # # training
    print('Training -----------')
    for step in range(301):
        cost = model.train_on_batch(X_train, Y_train)
        if step % 100 == 0:
            print('train cost: ', cost)
    
    # # test
    print('\nTesting ------------')
    cost = model.evaluate(X_test, Y_test, batch_size=40)
    print('test cost:', cost)
    W, b = model.layers[0].get_weights()
    print('Weights=', W, '\nbiases=', b)
    
    # plotting the prediction
    Y_pred = model.predict(X_test)
    plt.scatter(X_test, Y_test)
    plt.plot(X_test, Y_pred)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzY3MzE1Ng_size_16_color_FFFFFF_t_70 1]

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzY3MzE1Ng_size_16_color_FFFFFF_t_70 1]: /images/20230528/9ef5e0edc8334ed6bfa3fa45c4dbb232.png