TensorFlow Eager Execution

紫色蒲公英
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什么是 TensorFlow 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


Eager Execution API 是一种命令式的、调用即执行的操作接口。之前的tensorflow是声明式编程,需要先声明

图的计算再调用tf.Session去执行,虽然这种方式有很多的优点,但同时也有不利于debug等不便之处。


启动Eager Execution

from __future__ import absolute_import, division, print_function

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

# 设置为立即执行模式
print("Eager mode...")
tfe.enable_eager_execution()


示例

# 定义张量常量 可以直接print()
print("Define constant tensors")
a = tf.constant(4)
print("a = %i" % a)
b = tf.constant(3)
print("b = %i" % b)

# 结果
# a = 4
# b = 3

# 不需要调用tf.Session即可执行
print("Running operations, without tf.Session")
c = a + b
print("a + b = %i" % c)
d = a * b
print("a * b = %i" % d)

# 结果
# a + b = 7
# a * b = 12

# 与Numpy完全兼容

# 定义张量常量
a = tf.constant([[2., 1.],
                 [1., 0.]], dtype=tf.float32)
print("Tensor: a = %s" % a)
b = np.array([[3., 0.],
              [5., 1.]], dtype=np.float32)
print("NumpyArray: b = %s" % b)

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

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

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

# 结果
# Tensor:
#  a = tf.Tensor(
# [[2. 1.]
#  [1. 0.]], shape=(2, 2), dtype=float32)
# NumpyArray:
#  b = [[3. 0.]
#  [5. 1.]]
# 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)
# 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)


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