使用tflearn 构建多层感知机MLP

旧城等待， 2022-05-31 05:13 206阅读 0赞

# -*- coding:utf-8 -*-
    ''' Created on 2018-1-19 '''
    import numpy as np
    import tflearn
    import tflearn.datasets.mnist as mnist
    
    X, Y, testX, testY = mnist.load_data(one_hot=True)
    
    def do_DNN(X, Y, testX, testY):
        # Building deep neural network
    # 定义输入层
        input_layer = tflearn.input_data(shape=[None, 784])
    # 定义一个64神经元的全连接隐藏层，
        dense1 = tflearn.fully_connected(input_layer, 64, activation='tanh',regularizer='L2', weight_decay=0.001)
    # 对全连接使用一个dropout，随机停用部分神经元，防止过拟合
        dropout1 = tflearn.dropout(dense1, 0.8)
        dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh',regularizer='L2', weight_decay=0.001)
        dropout2 = tflearn.dropout(dense2, 0.8)
    # 定义输出层，使用softmax分类
        softmax = tflearn.fully_connected(dropout2, 10, activation='softmax')
    
        # Regression using SGD with learning rate decay and Top-3 accuracy
        # 反向传播使用SGD随机梯度下降法，学习率递减
        sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)
    # 定义，真实结果在预测结果前3中就算正确
        top_k = tflearn.metrics.Top_k(3)
    # 定义回归策略
        net = tflearn.regression(softmax, optimizer=sgd, metric=top_k,loss='categorical_crossentropy')
        # Training
        model = tflearn.DNN(net, tensorboard_verbose=0)
        model.fit(X, Y, n_epoch=20, validation_set=(testX, testY),
                  show_metric=True, run_id="dense_model")
    
    # 准确率98%
    do_DNN(X, Y, testX, testY)

MNIST数据集的下载  
MNIST是一些手写数字的图片，通过[http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz][http_www.iro.umontreal.ca_lisa_deep_data_mnist_mnist.pkl.gz]下载数据集。

[http_www.iro.umontreal.ca_lisa_deep_data_mnist_mnist.pkl.gz]: http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz