文章目錄
使用 TensorBoard 流程
- 1、建立文件夾存放 TensorBoard 的記錄文件；
- 2、實例化記錄器；
- 3、將參數（一般是標量）記錄到指定的記錄器中；
- 4、訪問 TensorBoard 的可視界面。
具體流程
Step 1
在代碼目錄下建立一個文件夾（如 ./tensorboard ）。
Step 2
實例化記錄器：
summary_writer = tf.summary.create_file_writer('./tensorboard') # 參數爲記錄文件所保存的目錄
Step 3
將參數（一般是標量）記錄到指定的記錄器中：
summary_writer = tf.summary.create_file_writer('./tensorboard')
# 開始模型訓練
for batch_index in range(num_batches):
# ...（訓練代碼，當前batch的損失值放入變量loss中）
with summary_writer.as_default(): # 希望使用的記錄器
tf.summary.scalar("loss", loss, step=batch_index) # 還可以添加其他自定義的變量
每運行一次 tf.summary.scalar() ，記錄器就會向記錄文件中寫入一條記錄。
Step 4
當我們要對訓練過程可視化時，在代碼目錄打開終端，運行：
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 記錄文件的文件夾路徑。
然後使用瀏覽器訪問命令行程序所輸出的網址（一般是 http://127.0.0.1:6006/），即可訪問 TensorBoard 的可視界面。
查看 Graph 和 Profile 信息
tf.summary.trace_on(graph=True, profiler=True) # 開啓Trace，可以記錄圖結構和profile信息
# 進行訓練
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
之後，我們就可以在 TensorBoard 中選擇 “Profile”，以時間軸的方式查看各操作的耗時情況。如果使用了 tf.function 建立了計算圖，也可以點擊 “Graphs” 查看圖結構。
實例
此處用 MNIST 的訓練過程舉例：
1、定義模型及訓練過程
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、建立文件夾存放 TensorBoard 的記錄文件
log_dir = 'tensorboard'
3、實例化記錄器（開啓Trace）
summary_writer = tf.summary.create_file_writer(log_dir) # 實例化記錄器
tf.summary.trace_on(profiler=True) # 開啓Trace（可選）
4、將參數記錄到指定的記錄器中
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(): # 指定記錄器
tf.summary.scalar("loss", loss, step=epoch) # 將當前損失函數的值寫入記錄器
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（可選）