Tensorflow之dropout
训练神经网络模型时,如果训练样本较少,为了防止模型过拟合,可以使用Dropout, Dropout是指在模型训练时随机让网络某些隐含层节点的权重不工作,不工作的那些节点可以暂时认为不是网络结构的一部分,但是它的权重得保留下来(只是暂时不更新而已),因为下次样本输入时它可能又得工作了(有点抽象,具体实现看后面的实验部分)。
#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ tensorflow dropout dropout一般用在全连接的部分,卷积部分不会用到dropout,输出曾也不会使用dropout,适用范围[输入,输出) 只用在训练集,不用在测试集 """ import tensorflow as tf from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer #加载数据 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, keep_prob=1.0): 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 # 这里做 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, ) tf.summary.histogram(layer_name + '/outputs', outputs) return outputs xs = tf.placeholder(tf.float32, [None, 64]) # 8x8 ys = tf.placeholder(tf.float32, [None, 10]) #添加输出层 l1 = add_layer(xs, 64 ,50, 'l1', activation_function=tf.nn.tanh) prediction = add_layer(l1, 50, 10, 'l2', activation_function=tf.nn.softmax) cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1])) # loss #scalar_summary记录存数值,用于画图 tf.summary.scalar('loss', cross_entropy) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) #定义每次做连接的神经元个数 keep_prob = tf.placeholder(tf.float32) sess = tf.Session() # #在TensorFlow中,所有的操作只有当你执行,或者另一个操作依赖于它的输出时才会运行。 #我们刚才创建的这些节点(summary nodes)都围绕着你的图像:没有任何操作依赖于它们的结果。 #因此,为了生成汇总信息,我们需要运行所有这些节点。这样的手动工作是很乏味的, #因此可以使用tf.merge_all_summaries来将他们合并为一个操作。 #http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/summaries_and_tensorboard.html merged = tf.summary.merge_all() #summary train_writer = tf.train.summary.FileWriter("logs/train", sess.graph) test_writer = tf.train..summary.FileWriter("logs/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}) if i % 50 == 0: # record loss # record loss 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)