What Are the Main Differences Between TensorFlow 1.x and 2.x

The evolution from TensorFlow 1.x to 2.x brought significant changes. Here are the main differences:

1. Execution Mode

TensorFlow 1.x: Static Computational Graph

• Uses declarative programming style
• Requires building the computational graph first, then executing through Session
• More efficient for graph optimization and deployment

python
import tensorflow as tf

# Build computational graph
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
c = a + b

# Execute computational graph
with tf.Session() as sess:
    result = sess.run(c, feed_dict={a: 5.0, b: 3.0})
    print(result)

TensorFlow 2.x: Eager Execution

• Eager execution enabled by default, operations return results immediately
• Uses imperative programming style, more aligned with Python conventions
• More intuitive debugging, can use Python debugging tools

python
import tensorflow as tf

# Eager execution
a = tf.constant(5.0)
b = tf.constant(3.0)
c = a + b
print(c)  # Direct output

2. API Simplification

Keras Integration

• TensorFlow 2.x deeply integrates Keras as a high-level API
• Recommends using tf.keras for model building
• API is more concise and consistent

Removed APIs

• tf.app, tf.flags, tf.logging have been removed
• tf.contrib module completely removed
• tf.Session and tf.placeholder no longer recommended

3. Automatic Control Flow

TensorFlow 1.x

• Requires special control flow operations: tf.cond, tf.while_loop
• Complex syntax, not intuitive

TensorFlow 2.x

• Directly uses Python control flow statements
• More natural and readable

python
# Direct Python control flow in TensorFlow 2.x
if x > 0:
    y = x
else:
    y = -x

4. Variable Management

TensorFlow 1.x

• Requires explicit variable initialization
• Uses tf.global_variables_initializer()
• Complex variable scope management

TensorFlow 2.x

• Variables are automatically initialized
• Uses Python objects to manage variables
• More aligned with object-oriented programming paradigm

5. Gradient Computation

TensorFlow 1.x

python
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    sess.run(train_op)

TensorFlow 2.x

python
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)

with tf.GradientTape() as tape:
    predictions = model(inputs)
    loss = compute_loss(predictions, targets)

gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))

6. Distributed Strategy

TensorFlow 2.x Improvements

• Unified distributed strategy API: tf.distribute.Strategy

• Supports multiple distributed strategies:

  • MirroredStrategy: Single machine, multiple GPUs
  • MultiWorkerMirroredStrategy: Multi-machine, multiple GPUs
  • TPUStrategy: TPU training
  • ParameterServerStrategy: Parameter server

7. Performance Optimization

TensorFlow 2.x Additions

• tf.function decorator: Converts Python functions to computational graphs
• Combines the convenience of eager execution with computational graph performance
• Automatic optimization and parallelization

python
@tf.function
def train_step(inputs, targets):
    with tf.GradientTape() as tape:
        predictions = model(inputs)
        loss = compute_loss(predictions, targets)
    
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

8. Compatibility

Backward Compatibility

• TensorFlow 2.x provides tf.compat.v1 module
• Can run most TensorFlow 1.x code
• Provides migration tools to help upgrade

Summary

Feature	TensorFlow 1.x	TensorFlow 2.x
Execution Mode	Static computational graph	Eager execution
Programming Style	Declarative	Imperative
API Complexity	Complex	Simplified
Debugging Difficulty	Higher	Lower
Performance	High performance after optimization	tf.function provides high performance
Learning Curve	Steep	Gentle

TensorFlow 2.x significantly lowers the barrier to entry while maintaining high performance, enabling developers to build and train deep learning models more quickly.