> For the complete documentation index, see [llms.txt](https://baozoulin.gitbook.io/tensorflow/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://baozoulin.gitbook.io/tensorflow/ke-shi-hua-haozhu-shou-tensorboard/tensorboardke-shi-hua-hao-bang-shou-1.md).

# Tensorboard可视化好帮手1

**with tf.name\_scope('inputs')**可以将**xs**，**ys**所有包括进来，形成一个大的图层

图层名字就是**with tf.name\_scope()**方法里的参数

```python
with tf.name_scope('inputs'):
    # define placeholder for inputs to network
    xs = tf.placeholder(tf.float32, [None, 1]，name='x_in')
    ys = tf.placeholder(tf.float32, [None, 1],name='y_in')
```

编辑**layer**:

当选择用tensorflow中的激励函数（激活函数）的时候，tensorflow会默认添加名称

```python
def add_layer(inputs, in_size, out_size, activation_function=None):
    # add one more layer and return the output of this layer
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            Weights = tf.Variable(
            tf.random_normal([in_size, out_size]), 
            name='W')
        with tf.name_scope('biases'):
            biases = tf.Variable(
            tf.zeros([1, out_size]) + 0.1, 
            name='b')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(
            tf.matmul(inputs, Weights), 
            biases)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b, )
        return outputs
```

**loss**部分:

```python
# the error between prediciton and real data
with tf.name_scope('loss'):
    loss = tf.reduce_mean(
    tf.reduce_sum(
    tf.square(ys - prediction),
    axis=[1]
    ))
```

**train\_step**:

```python
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
```

**tf.summary.FileWriter()**将'绘画'出的图保存到一个目录中

第二个参数需要使用**sess.graph**，需要把这句话放在获取**session**的后面

**graph**是将前面定义的框架信息收集起来，然后放在**logs/**目录下面。

```python
sess = tf.Session() # get session
# tf.train.SummaryWriter soon be deprecated, use following
writer = tf.summary.FileWriter("logs/", sess.graph)
```

**终端**:

```
tensorboard --logdir logs
```