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TensorBoard_1.py
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TensorBoard_1.py
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# coding=utf-8
# https://www.leiphone.com/news/201704/PgRxGpwtFpSgJoAZ.html
# TensorFlow自带可视化工具TensorBoard的打开方式
import tensorflow as tf
import numpy as np
def add_layer(inputs, in_size, out_size, n_layer, activation_function=None): # activation_function=None线性函数
layer_name = "layer%s" % n_layer
with tf.name_scope(layer_name):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size, out_size])) # Weight中都是随机变量
tf.histogram_summary(layer_name + "/weights", Weights) # 可视化观看变量
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1) # biases推荐初始值不为0
tf.histogram_summary(layer_name + "/biases", biases) # 可视化观看变量
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.matmul(inputs, Weights) + biases # inputs*Weight+biases
tf.histogram_summary(layer_name + "/Wx_plus_b", Wx_plus_b) # 可视化观看变量
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
tf.histogram_summary(layer_name + "/outputs", outputs) # 可视化观看变量
return outputs
# 创建数据x_data,y_data
x_data = np.linspace(-1, 1, 300)[:, np.newaxis] # [-1,1]区间,300个单位,np.newaxis增加维度
noise = np.random.normal(0, 0.05, x_data.shape) # 噪点
y_data = np.square(x_data) - 0.5 + noise
with tf.name_scope('inputs'): # 结构化
xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
ys = tf.placeholder(tf.float32, [None, 1], name='y_input')
# 三层神经,输入层(1个神经元),隐藏层(10神经元),输出层(1个神经元)
l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu) # 隐藏层
prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None) # 输出层
# predition值与y_data差别
with tf.name_scope('loss'):
loss = tf.reduce_mean(
tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1])) # square()平方,sum()求和,mean()平均值
tf.scalar_summary('loss', loss) # 可视化观看常量
with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 0.1学习效率,minimize(loss)减小loss误差
init = tf.initialize_all_variables()
sess = tf.Session()
# 合并到Summary中
merged = tf.merge_all_summaries()
# 选定可视化存储目录
writer = tf.train.SummaryWriter("/home/chuwei/PycharmProjects/Speech Separation/", sess.graph)
sess.run(init) # 先执行init
# 训练1k次
for i in range(1000):
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 50 == 0:
result = sess.run(merged, feed_dict={xs: x_data, ys: y_data}) # merged也是需要run的
writer.add_summary(result, i) # result是summary类型的,需要放入writer中,i步数(x轴)