Can you explain the difference and usage of variables and placeholders in TensorFlow?

Variables and placeholders in TensorFlow are both used to store data, but they have different characteristics and purposes.

1. Variables:
   Variables are parameters that can be trained (optimized) during the model training process, including learnable parameters such as weights and biases. The values of variables are updated in each training iteration, allowing the model to gradually converge to the optimal solution. In TensorFlow, variables are created and initialized using the tf.Variable() function.
2. Placeholder:
   A placeholder is a parameter used to pass in external data, it needs to be assigned a value before model training, and will not change during the training process. Placeholders are typically used to represent input data and labels, allowing for different data to be dynamically passed in during model training. In TensorFlow, placeholders are created using the tf.placeholder() function.

Summary:

1. Variables are used to store model parameters and will be constantly updated during the training process.
2. Placeholders are used to pass in external data and are not updated during the training process.

In model training, variables and placeholders are often used together to construct the input data and parameters for the model, as well as for training.