TensorFlow-Dropout，降低网络过拟合的风险
硬件：NVIDIA-GTX1080
软件：Windows7、python3.6.5、tensorflow-gpu-1.4.0
一、基础知识
1、Dropout-使得网络中神经元按概率工作或不工作（让某些无关紧要的Weights按概率消失，实现惩罚机制）
2、Dropout-keep_prob：概率
3、sklearn_version >= 0.18
二、代码展示
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 layerWeights = 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 dropoutWx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob)if activation_function is None:outputs = Wx_plus_belse: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]) # 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
tf.summary.scalar('loss', cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)sess = tf.Session()
merged = tf.summary.merge_all()
# summary writer goes in here
train_writer = tf.summary.FileWriter("logs/train", sess.graph)
test_writer = tf.summary.FileWriter("logs/test", sess.graph)init = tf.global_variables_initializer()
sess.run(init)
for i in range(500):# here to determine the keeping probabilitysess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})if i % 50 == 0:# record losstrain_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)
sess.close()
三、执行tensorboard
参考TensorFlow-tensorboard结果可视化
四、结果展示
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