TensorBoard Features Explained

TensorBoard is a tool used for visualizing and monitoring the training process of deep learning models, with its main features including:

1. Visualizing Model Structure: TensorBoard can display the structure of deep learning models, including the connections between layers and the number of parameters.
2. Display training process metrics: TensorBoard can show the changes in metrics such as loss functions, accuracy, etc. during the training process, helping users understand the training progress of the model.
3. Visualizing model images: TensorBoard can display image data in deep learning models, such as feature maps and convolution kernels, helping users understand how the model works.
4. Display a computation graph: TensorBoard is able to showcase the computation graph of deep learning models, aiding users in understanding the forward and backward propagation process of the model.
5. Visualization of embedded vectors: TensorBoard can display the embedded vectors learned in the model, such as word vectors in Word2Vec, helping users understand the model’s performance in learning tasks.
6. Visualization of training curves: TensorBoard can display changes in learning rate, gradient size, and other metrics during the training process, helping users optimize the training of their models.
7. The histogram of the model can be displayed: TensorBoard can show the histogram of the weights and biases in each layer of the model, helping users understand the distribution of model parameters.