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  • Keras + LSTM 做回归demo 2

    接上回, 这次做了一个多元回归

    这里贴一下代码

    import numpy as np
    np.random.seed(1337)
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt
    import keras
    from keras.models import Sequential
    from keras.layers import Activation
    from keras.layers import LSTM
    from keras.layers import Dropout
    from keras.layers import Dense
    import pandas as pd
    

      

    datan = 1000
    # 真实参数
    ori_weights = [5, -4, 3, -2, 1]
    colsn = len(ori_weights)
    bias = -1
    ori = np.zeros((1, colsn))
    ori[0] = np.asarray(ori_weights)
    
    # 列信息
    cols_name = [chr(65+i) for i in range(colsn)]
    X = np.zeros((colsn, datan))
    
    for i in range(colsn):
        X[i] = np.random.normal(1, 0.1, datan)
    # 真实Y
    Y = np.matmul(ori, X) + bias + np.random.normal(-0.1, 0.1, (datan, ))
    
    # 数据预览
    df = pd.DataFrame(X.T, columns=cols_name)
    df['Y'] = df.apply(lambda row: np.matmul(ori, [row[k] for k in df.columns] )[0]+bias, axis=1)
    df['target'] = Y[0]
    df.head()
    

      

    X_train, X_test, Y_train, Y_test = train_test_split(X.T, Y.T, test_size=0.33, random_state=42)
    

      

    neurons = 128          
    activation_function = 'tanh'  
    loss = 'mse'                  
    optimizer="adam"              
    dropout = 0.01 
    batch_size = 12          
    epochs = 200
    

      

    model = Sequential()
    
    model.add(LSTM(neurons, return_sequences=True, input_shape=(1, colsn), activation=activation_function))
    model.add(Dropout(dropout))
    model.add(LSTM(neurons, return_sequences=True, activation=activation_function))
    model.add(Dropout(dropout))
    model.add(LSTM(neurons, activation=activation_function))
    model.add(Dropout(dropout))
    model.add(Dense(output_dim=1, input_dim=1))
    

      

    model.compile(loss=loss, optimizer=optimizer)
    

      

    epochs = 2001
    for step in range(epochs):
        cost = model.train_on_batch(X_train[:, np.newaxis], Y_train)
        if step % 30 == 0:
            print(f'{step} train cost: ', cost)
    

      

    # test
    print('Testing ------------')
    cost = model.evaluate(X_test[:, np.newaxis], Y_test, batch_size=40)
    print('test cost:', cost)
    

      

    # plotting the prediction
    Y_pred = model.predict(X_test[:, np.newaxis])
    #
    sdf = pd.DataFrame({'test':list(Y_test.T[0]), 'pred':list(Y_pred.T[0])})
    sdf.sort_values(by='test', inplace=True)
    #
    plt.scatter(range(len(Y_test)), list(sdf.test))
    plt.plot(range(len(Y_test)), list(sdf.pred), 'r--')
    plt.show()
    

      

      

      

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  • 原文地址:https://www.cnblogs.com/fadedlemon/p/10530244.html
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