tensorboard入门实例

import tensorflow as tf

# 创建节点时设置name，方便在图中识别
W = tf.Variable([0], dtype=tf.float32, name='graph_W')
b = tf.Variable([0], dtype=tf.float32, name='graph_b')

# 创建节点时设置name，方便在图中识别
x = tf.placeholder(tf.float32, name='graph_x')
y = tf.placeholder(tf.float32, name='graph_y')

# 线性模型
linear_model = W * x + b

# 损失模型隐藏到loss-model模块
with tf.name_scope("loss-model"):
    loss = tf.reduce_sum(tf.square(linear_model - y))
    # 给损失模型的输出添加scalar，用来观察loss的收敛曲线
    tf.summary.scalar("loss", loss)

optimizer = tf.train.GradientDescentOptimizer(0.001)

train = optimizer.minimize(loss)

x_train = [1, 2, 3, 6, 8]
y_train = [4.8, 8.5, 10.4, 21.0, 25.3]

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)

    # 调用 merge_all() 收集所有的操作数据
    merged = tf.summary.merge_all()

    # 模型运行产生的所有数据保存到 ./tensorflow 文件夹供 TensorBoard 使用
    writer = tf.summary.FileWriter('./tensorflow', sess.graph)

    # 训练10000次
    for i in range(10000):
        # 训练时传入merge
        summary, _, train_W, train_b, train_loss = sess.run([merged, train, W, b, loss], {x: x_train, y: y_train})
        print("training W: %s\tb: %s\tloss: %s" % (train_W, train_b, train_loss))
        # 收集每次训练产生的数据
        writer.add_summary(summary, i)

    print("train over: \tW: %s b: %s loss: %s" % (train_W, train_b, train_loss))

    curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})

    print("After train: \tW: %s b: %s loss: %s" % (curr_W, curr_b, curr_loss))

# 输出结果：
# train over: 	W: [2.982361] b: [2.0705438] loss: 2.1294134
# After train:  W: [2.982361] b: [2.0705438] loss: 2.1294134
import tensorflow as tf

# 创建节点时设置name，方便