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Using bfloat16 with TensorFlow models in Python

Last Updated : 26 Apr, 2025

In this article, we will discuss bfloat16 (Brain Floating Point 16) in Python. It is a numerical format that occupies 16 bits in memory and is used to represent floating-point numbers. It is similar to the more commonly used 32-bit single-precision float (float32) and 64-bit double-precision float (float64), but with a smaller range of values and lower precision.

Let’s understand this by some examples as discussed below:

Example 1

In Tensorflow, you can use bfloat16 data types in your models by casting your tensors to the bfloat16 dtype.

Python3

import tensorflow as tf 
  
# Create a float32 tensor with values [1, 2, 3, 4] 
tensor = tf.constant([1, 2, 3, 4], dtype=tf.float32) 
print(tensor) 
# Cast the tensor to bfloat16 
tensor = tf.cast(tensor, dtype=tf.bfloat16) 
print(tensor)

Output:

Example 2

You can also specify the dtype when creating tensors using functions such as tf.zeros and tf.ones.

Python3

import tensorflow as tf 
  
# Create a bfloat16 tensor with values [1, 2, 3, 4] 
tensor = tf.constant([1, 2, 3, 4], dtype=tf.bfloat16) 
print(tensor) 
# Create a tensor of ones with shape (2, 2) and dtype bfloat16 
ones = tf.ones((2, 2), dtype=tf.bfloat16) 
print(ones)

Output:

Note that when using bfloat16, you may see a reduction in model accuracy due to the lower precision compared to float32. You should carefully consider the trade-off between model accuracy and memory usage when deciding whether to use bfloat16 in your model.

Example 3

In this example, we use the functional API to define a model with a single hidden layer. Both the input layer and hidden layer have a bfloat16 dtype. We then compile and fit the model using the fit method.

Python3

import tensorflow as tf 
  
# Define a simple model with a single bfloat16-typed layer 
inputs = tf.keras.Input(shape=(10,), 
                        dtype=tf.bfloat16) 
x = tf.keras.layers.Dense(10,  
                  dtype=tf.bfloat16)(inputs) 
outputs = tf.keras.layers.Dense(1,  
                activation='sigmoid',  
                dtype=tf.bfloat16)(x) 
model = tf.keras.Model(inputs, outputs) 
print(outputs) 
print(model) 

Output:

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