text: # TensorBoard in PyTorch
  
  In this tutorial, we implement a MNIST classifier using a simple neural network and visualize the training process using [TensorBoard](https://www.tensorflow.org/get_started/summaries_and_tensorboard). In training phase, we plot the loss and accuracy functions through `scalar_summary` and visualize the training images through `image_summary`. In addition, we visualize the weight and gradient values of the parameters of the neural network using `histogram_summary`. PyTorch code for handling these summary functions can be found [here](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard/main.py#L81-L97).
  
  ![alt text](gif/tensorboard.gif)
  
  <br>
  
  ## Usage 
  
  #### 1. Install the dependencies
  ```bash
  $ pip install -r requirements.txt
  ```
  
  #### 2. Train the model
  ```bash
  $ python main.py
  ```
  
  #### 3. Open the TensorBoard
  To run the TensorBoard, open a new terminal and run the command below. Then, open http://localhost:6006/ on your web browser.
  ```bash
  $ tensorboard --logdir='./logs' --port=6006
  ```
  