请在上面的代码中添加tensorboard来查看结果
时间: 2024-04-30 19:23:25 浏览: 8
以下是添加tensorboard的代码示例:
```python
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
# Load dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# Normalize data
x_train = x_train / 255.0
x_test = x_test / 255.0
# Define model
model = Sequential([
Dense(64, input_shape=(784,), activation='relu'),
Dense(32, activation='relu'),
Dense(10, activation='softmax')
])
# Compile model
optimizer = Adam(lr=0.001)
model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
# Define callbacks
callbacks = [tf.keras.callbacks.TensorBoard(log_dir='logs')]
# Train model
model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test), callbacks=callbacks)
```
在上述代码中,我们创建了一个TensorBoard回调并将其添加到模型的拟合方法中。这个回调将在每个epoch结束时记录一些指标,并将这些指标写入TensorBoard日志目录中。我们可以使用以下命令来启动TensorBoard服务器:
```
tensorboard --logdir logs
```
然后在浏览器中访问http://localhost:6006,就可以查看我们的模型指标和可视化图表了。