Basic introduction to TensorFlow's Eager API

A simple introduction to get started with TensorFlow's Eager API.

• Author: Aymeric Damien
• Project: https://github.com/aymericdamien/TensorFlow-Examples/

What is TensorFlow's Eager API ?

Eager execution is an imperative, define-by-run interface where operations are executed immediately as they are called from Python. This makes it easier to get started with TensorFlow, and can make research and development more intuitive. A vast majority of the TensorFlow API remains the same whether eager execution is enabled or not. As a result, the exact same code that constructs TensorFlow graphs (e.g. using the layers API) can be executed imperatively by using eager execution. Conversely, most models written with Eager enabled can be converted to a graph that can be further optimized and/or extracted for deployment in production without changing code. - Rajat Monga

More info: https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html

from __future__ import absolute_import, division, print_function

import numpy as np
import tensorflow as tf
import tensorflow.contrib.eager as tfe

# Set Eager API
print("Setting Eager mode...")
tfe.enable_eager_execution()

Setting Eager mode...

# Define constant tensors
print("Define constant tensors")
a = tf.constant(2)
print("a = %i" % a)
b = tf.constant(3)
print("b = %i" % b)

Define constant tensors
a = 2
b = 3

# Run the operation without the need for tf.Session
print("Running operations, without tf.Session")
c = a + b
print("a + b = %i" % c)
d = a * b
print("a * b = %i" % d)

Running operations, without tf.Session
a + b = 5
a * b = 6

# Full compatibility with Numpy
print("Mixing operations with Tensors and Numpy Arrays")

# Define constant tensors
a = tf.constant([[2., 1.],
                 [1., 0.]], dtype=tf.float32)
print("Tensor:\n a = %s" % a)
b = np.array([[3., 0.],
              [5., 1.]], dtype=np.float32)
print("NumpyArray:\n b = %s" % b)

Mixing operations with Tensors and Numpy Arrays
Tensor:
 a = tf.Tensor(
[[2. 1.]
 [1. 0.]], shape=(2, 2), dtype=float32)
NumpyArray:
 b = [[3. 0.]
 [5. 1.]]

# Run the operation without the need for tf.Session
print("Running operations, without tf.Session")

c = a + b
print("a + b = %s" % c)

d = tf.matmul(a, b)
print("a * b = %s" % d)

Running operations, without tf.Session
a + b = tf.Tensor(
[[5. 1.]
 [6. 1.]], shape=(2, 2), dtype=float32)
a * b = tf.Tensor(
[[11.  1.]
 [ 3.  0.]], shape=(2, 2), dtype=float32)

print("Iterate through Tensor 'a':")
for i in range(a.shape[0]):
    for j in range(a.shape[1]):
        print(a[i][j])

Iterate through Tensor 'a':
tf.Tensor(2.0, shape=(), dtype=float32)
tf.Tensor(1.0, shape=(), dtype=float32)
tf.Tensor(1.0, shape=(), dtype=float32)
tf.Tensor(0.0, shape=(), dtype=float32)