Use tf.function for Performance Optimization
- Leverage the
tf.function decorator to compile a Python function into a TensorFlow graph. This enhances performance by enabling graph optimizations and reduces the Python overhead.
- Wrap computationally heavy Python functions that involve TensorFlow operations to accelerate execution and allow for more efficient execution planning.
import tensorflow as tf
@tf.function
def compute(x):
return x ** 2 + 2 * x - 5
result = compute(tf.constant(2.0))
Separate Build and Execution Phases
- Design the TensorFlow graph by separating the model architecture definition (build phase) from execution (run phase) to enhance readability and reusability.
- Build the model layers first, then use the model for training or inference later. This method also eases debugging and future modifications.
# Build phase
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)),
tf.keras.layers.Dense(1, activation='sigmoid')
])
# Execution phase
model.compile(optimizer='adam', loss='binary_crossentropy')
model.fit(train_data, train_labels, epochs=10)
Manage Graph Resources Efficiently
- Ensure resource cleanup by using context managers like
tf.device to manage computation devices efficiently and avoid unnecessary device memory allocation.
- Use
tf.Graph objects explicitly when multiple graphs are needed, rather than relying on implicit default graphs. This prevents cross-interference between graphs.
g1 = tf.Graph()
with g1.as_default():
# Define operations within this graph
a = tf.constant([1.0, 2.0])
g2 = tf.Graph()
with g2.as_default():
# Define operations within this graph
b = tf.constant([3.0, 4.0])
Use Placeholders for Input Handling (TF 1.x)
- In TensorFlow 1.x, use placeholders to insert input data into a graph. This enables feeding different data inputs without rebuilding the graph.
- Ensure proper tensor shape and data type to avoid errors during session runtime.
x = tf.placeholder(tf.float32, shape=[None, 3])
y = x ** 2
with tf.Session() as sess:
result = sess.run(y, feed_dict={x: [[1, 2, 3], [4, 5, 6]]})
Embrace Eager Execution for Improved Debugging and Interactivity
- Utilize TensorFlow's eager execution mode for interactive debugging and ease of use similar to standard Python functions.
- Switch to eager execution for tasks that benefit from Python’s control flow and require immediate execution results for debugging.
import tensorflow as tf
tf.config.run_functions_eagerly(True)
# Operations execute immediately, outputs are returned directly
result = tf.add(3, 5)
print(result)
