名称作用域

当处理更复杂的模型（如神经网络）时，该图可以很容易地与数千个节点混淆。 为了避免这种情况，您可以创建名称作用域来对相关节点进行分组。 例如，我们修改以前的代码来定义名为loss的名称作用域内的错误和mse操作：

with tf.name_scope("loss") as scope:
    error = y_pred - y
    mse = tf.reduce_mean(tf.square(error), name="mse")

在作用域内定义的每个op的名称现在以loss/为前缀：

>>> print(error.op.name)
loss/sub
>>> print(mse.op.name)
loss/mse

在 TensorBoard 中，mse和error节点现在出现在loss命名空间中，默认情况下会出现崩溃（图 9-5）。

完整代码

import numpy as np
from sklearn.datasets import fetch_california_housing
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
housing = fetch_california_housing()
m, n = housing.data.shape
print("数据集:{}行,{}列".format(m,n))
housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]
scaler = StandardScaler()
scaled_housing_data = scaler.fit_transform(housing.data)
scaled_housing_data_plus_bias = np.c_[np.ones((m, 1)), scaled_housing_data]
from datetime import datetime
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
root_logdir = r"D://tf_logs"
logdir = "{}/run-{}/".format(root_logdir, now)
n_epochs = 1000
learning_rate = 0.01
X = tf.placeholder(tf.float32, shape=(None, n + 1), name="X")
y = tf.placeholder(tf.float32, shape=(None, 1), name="y")
theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name="theta")
y_pred = tf.matmul(X, theta, name="predictions")
def fetch_batch(epoch, batch_index, batch_size):
    np.random.seed(epoch * n_batches + batch_index)  #  not shown in the book
    indices = np.random.randint(m, size=batch_size)  #  not shown
    X_batch = scaled_housing_data_plus_bias[indices] #  not shown
    y_batch = housing.target.reshape(-1, 1)[indices] #  not shown
    return X_batch, y_batch
with tf.name_scope("loss") as scope:
    error = y_pred - y
    mse = tf.reduce_mean(tf.square(error), name="mse")
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(mse)
init = tf.global_variables_initializer()
mse_summary = tf.summary.scalar('MSE', mse)
file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
n_epochs = 10
batch_size = 100
n_batches = int(np.ceil(m / batch_size))
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(n_epochs):
        for batch_index in range(n_batches):
            X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)
            if batch_index % 10 == 0:
                summary_str = mse_summary.eval(feed_dict={X: X_batch, y: y_batch})
                step = epoch * n_batches + batch_index
                file_writer.add_summary(summary_str, step)
            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
    best_theta = theta.eval()
file_writer.flush()
file_writer.close()
print("Best theta:")
print(best_theta)