In TensorFlow, Variables and Placeholders are fundamental concepts for building models. Here's a breakdown:
🔹 What are Variables?
- Definition: Variables are used to store and update parameters during training.
- Key Features:
- Persist values across sessions
- Automatically track gradients for optimization
- Represent model weights or biases
- 📷 Tensorflow Variables
🔹 What are Placeholders?
- Definition: Placeholders act as entry points for feeding external data into the computation graph.
- Key Features:
- Define data shapes and types
- Allow dynamic input during execution
- Often used for training data and labels
- 📷 Tensorflow Placeholders
🔄 Key Differences
| Aspect | Variables | Placeholders |
|---|---|---|
| Data Flow | Store model parameters | Feed external data |
| Session Behavior | Retain values between runs | Reset values each session |
| Typical Use | Weights, biases | Input data, labels |
🧪 Example Usage
# Variables
weights = tf.Variable(initial_value=0.5, name="weights")
# Placeholders
inputs = tf.placeholder(tf.float32, shape=[None, 784], name="inputs")
labels = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
For deeper exploration, check our TensorFlow Tutorial to see how these concepts integrate with neural networks. 🚀