文章目录
使用 TensorBoard 流程
- 1、建立文件夹存放 TensorBoard 的记录文件；
- 2、实例化记录器；
- 3、将参数（一般是标量）记录到指定的记录器中；
- 4、访问 TensorBoard 的可视界面。
具体流程
Step 1
在代码目录下建立一个文件夹（如 ./tensorboard ）。
Step 2
实例化记录器：
summary_writer = tf.summary.create_file_writer('./tensorboard') # 参数为记录文件所保存的目录
Step 3
将参数（一般是标量）记录到指定的记录器中：
summary_writer = tf.summary.create_file_writer('./tensorboard')
# 开始模型训练
for batch_index in range(num_batches):
# ...（训练代码，当前batch的损失值放入变量loss中）
with summary_writer.as_default(): # 希望使用的记录器
tf.summary.scalar("loss", loss, step=batch_index) # 还可以添加其他自定义的变量
每运行一次 tf.summary.scalar() ，记录器就会向记录文件中写入一条记录。
Step 4
当我们要对训练过程可视化时，在代码目录打开终端，运行：
tensorboard --logdir=E:\Pycharm\code\Jupyter\tensorflow2.0\My_net\Tensorboard\tensorboard --host=127.0.0.1
其中 ‘E:\Pycharm\code\Jupyter\tensorflow2.0\My_net\Tensorboard\tensorboard’ 是存放 TensorBoard 记录文件的文件夹路径。
然后使用浏览器访问命令行程序所输出的网址（一般是 http://127.0.0.1:6006/），即可访问 TensorBoard 的可视界面。
查看 Graph 和 Profile 信息
tf.summary.trace_on(graph=True, profiler=True) # 开启Trace，可以记录图结构和profile信息
# 进行训练
with summary_writer.as_default():
tf.summary.trace_export(name="model_trace", step=0, profiler_outdir=log_dir) # 保存Trace信息到文件
之后，我们就可以在 TensorBoard 中选择 “Profile”，以时间轴的方式查看各操作的耗时情况。如果使用了 tf.function 建立了计算图，也可以点击 “Graphs” 查看图结构。
实例
此处用 MNIST 的训练过程举例：
1、定义模型及训练过程
import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
mnist = keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
# Add a channels dimension
x_train = x_train[..., tf.newaxis].astype(np.float32)
x_test = x_test[..., tf.newaxis].astype(np.float32)
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(x_test.shape[0])
class MyModel(keras.Model):
# Set layers.
def __init__(self):
super(MyModel, self).__init__()
# Convolution Layer with 32 filters and a kernel size of 5.
self.conv1 = layers.Conv2D(32, kernel_size=5, activation=tf.nn.relu)
# Max Pooling (down-sampling) with kernel size of 2 and strides of 2.
self.maxpool1 = layers.MaxPool2D(2, strides=2)
# Convolution Layer with 64 filters and a kernel size of 3.
self.conv2 = layers.Conv2D(64, kernel_size=3, activation=tf.nn.relu)
# Max Pooling (down-sampling) with kernel size of 2 and strides of 2.
self.maxpool2 = layers.MaxPool2D(2, strides=2)
# Flatten the data to a 1-D vector for the fully connected layer.
self.flatten = layers.Flatten()
# Fully connected layer.
self.fc1 = layers.Dense(1024)
# Apply Dropout (if is_training is False, dropout is not applied).
self.dropout = layers.Dropout(rate=0.5)
# Output layer, class prediction.
self.out = layers.Dense(10)
# Set forward pass.
def call(self, x, is_training=False):
x = tf.reshape(x, [-1, 28, 28, 1])
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
x = self.maxpool2(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.dropout(x, training=is_training)
x = self.out(x)
if not is_training:
# tf cross entropy expect logits without softmax, so only
# apply softmax when not training.
x = tf.nn.softmax(x)
return x
model = MyModel()
loss_object = keras.losses.SparseCategoricalCrossentropy()
optimizer = keras.optimizers.Adam()
@tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
predictions = model(images)
loss = loss_object(labels, predictions)
loss = tf.reduce_mean(loss)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return loss
2、建立文件夹存放 TensorBoard 的记录文件
log_dir = 'tensorboard'
3、实例化记录器（开启Trace）
summary_writer = tf.summary.create_file_writer(log_dir) # 实例化记录器
tf.summary.trace_on(profiler=True) # 开启Trace（可选）
4、将参数记录到指定的记录器中
EPOCHS = 5
for epoch in range(EPOCHS):
for images, labels in train_ds.take(10):
loss = train_step(images, labels)
with summary_writer.as_default(): # 指定记录器
tf.summary.scalar("loss", loss, step=epoch) # 将当前损失函数的值写入记录器
with summary_writer.as_default():
tf.summary.trace_export(name="model_trace", step=0, profiler_outdir=log_dir) # 保存Trace信息到文件（可选）