Basic introduction to TensorFlow's Eager API

簡單介紹一下TensorFlow的Eager API入門。

• 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

# Set Eager API
print("Setting Eager mode...")
tf.enable_eager_execution()
tfe = tf.contrib.eager
 

Setting Eager mode...

# 定義常數
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 

# 不需要呼叫 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

# 完全相容Numpy
print("Mixing operations with Tensors and Numpy Arrays")

# 定義常數張量
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.]]

#不需要呼叫 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)