tensorflow学习之使用tensorboard 展示神经网络的graph/histogram/scalar

Posted 2021-01-04 harriett-lin

# 创建神经网络， 使用tensorboard 展示graph/histogram/scalar
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt  # 若没有 pip install matplotlib

# 定义一个神经层
def add_layer(inputs, in_size, out_size,n_layer, activation_function=None):
    #add one more layer and return the output of this layer
    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]),name=‘W‘)
            tf.summary.histogram(layer_name+‘/weights‘,Weights)
        with tf.name_scope(‘biases‘):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,name=‘b‘)
            tf.summary.histogram(layer_name + ‘/biases‘, biases)
        with tf.name_scope(‘Wx_plus_b‘):
            Wx_plus_b = tf.matmul(inputs, Weights) + biases
        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

#make up some real data
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]  # x_data值为-1到1之间，有300个单位（例子），再加一个维度newaxis，即300行*newaxis列
noise = np.random.normal(0, 0.05, x_data.shape)  # 均值为0.方差为0.05，格式和x_data一样
y_data = np.square(x_data) - 0.5 + noise

#define placeholder for inputs to network
with tf.name_scope(‘inputs‘):
    xs = tf.placeholder(tf.float32, [None, 1],name=‘x_input1‘)  # none表示无论给多少个例子都行
    ys = tf.placeholder(tf.float32, [None, 1],name=‘y_input1‘)

# add hidden layer
l1 = add_layer(xs, 1, 10, n_layer=1,activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1,n_layer=2, activation_function=None)

#the error between prediction and real data
with tf.name_scope(‘loss‘):
    loss = tf.reduce_mean(
        tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))  # 对每个例子进行求和并取平均值 reduction_indices=[1]指按行求和
    tf.summary.scalar(‘loss‘,loss)
with tf.name_scope(‘train‘):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)  # 以0.1的学习效率对误差进行更正和提升

#两种初始化的方式
#init = tf.initialize_all_variables()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

#把所有的summary合并在一起
merged = tf.summary.merge_all()

#把整个框架加载到一个文件中去，再从文件中加载出来放到浏览器中查看
#writer=tf.train.SummaryWriter("logs/",sess.graph)
#首先找到tensorboard.exe的路径并进入c:AnacondaScripts,执行tensorboard.exe --logdir=代码生成的图像的路径（不能带中文）
writer=tf.summary.FileWriter("../../logs/",sess.graph)

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()   #show()是一次性的展示，为了使连续的展示，加入plt.ion()

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})
        writer.add_summary(result,i)

实验结果图：