Comparing different versions of a significant library like TensorFlow is essential for developers, particularly those involved in machine learning and deep learning. TensorFlow has become a standard within the community for constructing and training models. Over the years, TensorFlow has evolved, with the major change being the transition from TensorFlow 1.x to 2.x. Understanding these changes ensures developers can write efficient, optimized code.
Key Differences
In TensorFlow 2.x, several substantial changes were introduced, focusing on simplification and easier model development. Here we break down some of the core differences:
Eager Execution
In TensorFlow 1.x, a graph execution model was standard, requiring separate definition of computation graphs before running sessions. TensorFlow 2.x, however, adopts eager execution as default, making operations execute immediately and returning concrete values.
# TensorFlow 1.x style
import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
c = tf.constant(4.0)
assert c.graph is graph
# Define a session and run the graph
with tf.Session(graph=graph) as sess:
result = sess.run(c)
print(result) # Outputs: 4.0
# TensorFlow 2.x style
import tensorflow as tf
# No need to define a graph or session
result = tf.constant(4.0)
print(result) # Outputs:
Control Flow & Functions
TensorFlow 2.x integrates new mechanisms for control flow, allowing Python control flow statements like if, while, etc., to be converted into graph operations through the tf.function decorator. This was cumbersome in TensorFlow 1.x.
# TensorFlow 1.x style
import tensorflow as tf
x = tf.constant(0)
y = tf.while_loop(lambda x: x < 3, lambda x: x + 1, [x])
# TensorFlow 2.x style
def f(x):
for i in range(3):
x += 1
return x
x = tf.constant(0)
result = f(x)
print(result) # Outputs: 3
Keras Integration
Another significant change in TensorFlow 2.x is the tight integration with the high-level Keras API. In TensorFlow 1.x, Keras was a separate library that could be used with TensorFlow, but in 2.x, Keras is the recommended way to build and train models, with modules such as tf.keras.Model and tf.keras.layers.
# TensorFlow 1.x style
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))
# TensorFlow 2.x style
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
model = Sequential([
Dense(64, activation='relu', input_shape=(100,)),
Dense(10, activation='softmax')
])
model.summary()
Model Saving and Loading
TensorFlow 2.x standardizes saving and loading models using the tf.keras.models.save_model and tf.keras.models.load_model methods. This replaces the often complex and scattered methods available in TensorFlow 1.x.
# TensorFlow 2.x method
model.save("my_model")
# Loads the model
reconstructed_model = tf.keras.models.load_model("my_model")
Migration and Compatibility
Migrating from TensorFlow 1.x to 2.x introduces several challenges, given the structural changes. TensorFlow provides utilities like the tf_upgrade_v2 script to help automate this process. However, some manual code refactoring will likely still be needed.
Migrating is often recommended because of TensorFlow 2.x optimizations, richer features, and a more refined API. Projects not migrated risk becoming stale with newer models and upcoming operations potentially unsupported.
Conclusion
Understanding the significant updates from TensorFlow 1.x to 2.x is crucial. With TensorFlow 2.x, the development experience has become more straightforward, intuitive, and pythonic, which has allowed it to maintain its popularity with machine learning practitioners. Staying current with such tools not only benefits individual projects but also supports community standards, embracing technological progress.
