• tensorflow1.0 dropout层


    """
    Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
    """
    import tensorflow as tf
    from sklearn.datasets import load_digits
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import LabelBinarizer
    
    # load data
    digits = load_digits()
    X = digits.data
    y = digits.target
    y = LabelBinarizer().fit_transform(y)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)
    
    
    def add_layer(inputs, in_size, out_size, layer_name, activation_function=None, ):
        # add one more layer and return the output of this layer
        Weights = tf.Variable(tf.random_normal([in_size, out_size]))
        biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, )
        Wx_plus_b = tf.matmul(inputs, Weights) + biases
        # here to dropout
        Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b, )
        return outputs
    
    def compute_accuracy(v_xs,v_ys,v_keep_prob):
        global prediction
        y_pre = sess.run(prediction,feed_dict={xs:v_xs,keep_prob:v_keep_prob})
        correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
        result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:v_keep_prob})
        return result
    
    # define placeholder for inputs to network
    keep_prob = tf.placeholder(tf.float32)
    xs = tf.placeholder(tf.float32, [None, 64])  # 8x8
    ys = tf.placeholder(tf.float32, [None, 10])
    
    # add output layer
    l1 = add_layer(xs, 64, 50, 'l1', activation_function=tf.nn.tanh)
    prediction = add_layer(l1, 50, 10, 'l2', activation_function=tf.nn.softmax)
    
    # the loss between prediction and real data
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                                  reduction_indices=[1]))  # loss
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
    
    sess = tf.Session()
    sess.run(tf.initialize_all_variables())
    
    for i in range(500):
        # here to determine the keeping probability
        sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})
        if i % 50 == 0:
            print(compute_accuracy(X_train, y_train,1),compute_accuracy(X_test, y_test,1))
    

      

    多思考也是一种努力,做出正确的分析和选择,因为我们的时间和精力都有限,所以把时间花在更有价值的地方。
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  • 原文地址:https://www.cnblogs.com/LiuXinyu12378/p/12495403.html
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