tensorflow sonnet 模块 多层感知机 MLP 实

'''
Created on 2019年2月25日

@author: xiezhipu
'''

import sonnet as snt
import numpy as np
import tensorflow as tf

HiddenSize=[10,10,8,8]

config = tf.ConfigProto()
 
# 配置GPU内存分配方式，按需增长，很关键
config.gpu_options.allow_growth = True
 
# 配置可使用的显存比例
config.gpu_options.per_process_gpu_memory_fraction = 0.1

X=np.random.rand(500,2)*200

Y=np.matmul(X,np.array([[0.5],[0.3]]))+np.random.rand(500,1)*5

x=tf.placeholder(tf.float32, [None,2])

yTrue=tf.placeholder(tf.float32,[None,1])

layers=[]

for i in HiddenSize:
    hidden_i=snt.Linear(output_size=i)
    layers.append(hidden_i)
    layers.append(tf.nn.relu)

hiddent_to_out=snt.Linear(output_size=1)

layers.append(hiddent_to_out)

mlp=snt.Sequential(layers)

train_predictions = mlp(x)

lossfun=tf.reduce_mean(tf.abs(tf.subtract(train_predictions/yTrue,1)))

train_step=tf.train.AdamOptimizer(learning_rate=0.15).minimize(lossfun)


with tf.Session(config = config) as sess:
    init = tf.global_variables_initializer()
    sess.run(init)
    for i in range(int(1e3)):
        _,loss = sess.run([train_step,lossfun],feed_dict={x:X,yTrue:Y})
        print(loss)