调用该API可以不通过 tensorflow.Session.run()调用 定义的张量constant tensor,可以直接print
# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function import numpy as np import tensorflow as tf import tensorflow.contrib.eager as tfe # 设置 eager API tfe.enable_eager_execution() a = tf.constant(2) b = tf.constant(3) print('a = %i' % a) print('b = %i' % b) # run op no tf.Session.run() print("can run op without tf.Session.run") c = a + b c1 = a * b print("no Session... c=%i" % c) print("no Session... c1=%i" % c1) # eagerAPI完全兼容numpy # 定义张量 define constant tensors a = tf.constant([[2., 1.],[1., 0]], dtype=tf.float32) # tensor b = tf.constant([[3., 0.],[5., 1.]], dtype=tf.float32) c2 = tf.matmul(a, b) # 矩阵相乘matmul print("tensor:\n a=%s" % a) print("tensor:\n b=%s" % b) print("tensor multply :\n c2=%s" % c2) print(a.shape[0]) # 多少组维度信息 print(a.shape[1]) # 维度 # tensor对象能够迭代? range ????? for i in range(a.shape[0]): for u in range(a.shape[1]): print(a[i][u])