TensorFlow-多层感知机(MLP)

TensorFlow训练神经网络的4个步骤：

1、定义算法公式，即训练神经网络的forward时的计算

2、定义损失函数和选择优化器来优化loss

3、训练步骤

4、对模型进行准确率评测

附Multi-Layer Perceptron代码：

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist=input_data.read_data_sets("MNiST_data/",one_hot=True)
sess=tf.InteractiveSession()

in_units=784
h1_units=300
w1=tf.Variaable(tf.truncated_normal([in_units,h1_units],stddev=0.1))
b1=tf.Variable(tf.zeros([h1_units]))
w2=tf.Variable(tf.zeros([h1_units,10]))
b2=tf.Variable(tf.zeros([10]))

x=tf.placeholder(tf.float32,[None,in_units])
keep_prob=tf.placeholder(tf.float32)

hidden1=tf.nn.relu(tf.matmul(x,w1)+b1)
hidden1_drop=tf.nn.dropout(hidden1,keep_prob)
y=tf.nn.softmax(tf.matmul(hidden1_drop,w2)+b2)

y_=tf.placeholder(tf.float32,[None,10])
cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))
train_step=tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)

tf.initialize_all_variables().run()
for i in range(3000):
    batch_xs,batch_ys=mnist.train.next_batch(100)
    train_step.run({x:batch_xs,y_:batch_ys,keep_prob:0.75})

correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))