droputout使用

from __future__ import print_function
import tensorflow as tf 
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer


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):
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


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


keep_prob = tf.placeholder(tf.float32)
xs = tf.placeholder(tf.float32,[None, 64])
ys = tf.placeholder(tf.float32,[None, 10])


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]))


tf.summary.scalar('loss',cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)


sess = tf.Session()
merged = tf.summary.merge_all()


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):
sess.run(train_step, feed_dict={xs: x_train, ys: y_train, keep_prob: 0.5})
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)