TensorFlow利用dropout解决过拟合问题

        在TensorFlow训练样本的数据中，有时会出现过拟合（overfiting）的问题，可以采取dropout的方法来解决，即随机丢弃部分样本。

        下面是示例代码，通过tensorboard对结果进行了可视化：

        

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):
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            W = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
        with tf.name_scope('bias'):
            b = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.matmul(inputs, W) + b
        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)
        tf.summary.histogram(layer_name + '/outputs', outputs)
        return outputs


# define placeholder for inputs to network
keep_prob = tf.placeholder(tf.float32)
xs = tf.placeholder(tf.float32, [None, 64])  # 8x84
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]))
tf.summary.scalar('loss', cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.6).minimize(cross_entropy)

sess = tf.Session()
merged = tf.summary.merge_all()
# summary writer goes in there
train_writer = tf.summary.FileWriter('logs2/train', sess.graph)
test_writer = tf.summary.FileWriter('log2/test', sess.graph)

sess.run(tf.initialize_all_variables())


for i in range(500):
    sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})
    # record los
    if i % 50 == 0:
        train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob: 1})
        test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test, keep_prob: 1})
        train_writer.add_summary(train_result, i)
        test_writer.add_summary(test_result, i)