tensorflow实现多层感知机

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
#隐含层节点数
h1_units = 300

"""
初始化参数
"""
# 随机生成正太分布
#tf.truncated_normal(shape, mean, stddev) :shape表示生成张量的维度，mean是均值，stddev是标准差。
W1 = tf.Variable(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)

#隐含层计算
hidden1 = tf.nn.relu(tf.add(tf.matmul(x,W1),b1))
#随即将一部分节点设为0
hidden_drop = tf.nn.dropout(hidden1,keep_prob)
#输出层计算
y = tf.nn.softmax(tf.matmul(hidden_drop,W2)+b2)

y_ = tf.placeholder(tf.float32,[None,10])
#计算损失函数
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)
#初始化所有变量
tf.global_variables_initializer().run()

"""
训练集进行训练
"""

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})

# 测试集进行测试
#tf.argmax(input, axis=None, name=None, dimension=None)
#此函数是对矩阵按行或列计算最大值
correct_prediction = tf.equal(tf.arg_max(y,1),tf.arg_max(y_,1))
#cast(x, dtype, name=None)  将x的数据格式转化成dtype.
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}))