tensorflow--Basic Operations

import tensorflow as tf

# Basic constant operations
# The value returned by the constructor represents the output
# of the Constant op.
a = tf.constant(2)
b = tf.constant(3)

# Launch the default graph.
with tf.Session() as sess:
    print("a=2, b=3")
    print("Addition with constants: %i" % sess.run(a+b))
    print("Multiplication with constants: %i" % sess.run(a*b))

# Basic Operations with variable as graph input
# The value returned by the constructor represents the output
# of the Variable op. (define as input when running session)
# tf Graph input
a = tf.placeholder(tf.int16)
b = tf.placeholder(tf.int16)

# Define some operations
add = tf.add(a, b)
mul = tf.multiply(a, b)

# Launch the default graph.
with tf.Session() as sess:
    # Run every operation with variable input
    print("Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3}))
    print("Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3}))


# ----------------
# More in details:
# Matrix Multiplication from TensorFlow official tutorial

# Create a Constant op that produces a 1x2 matrix.  The op is
# added as a node to the default graph.
#
# The value returned by the constructor represents the output
# of the Constant op.
matrix1 = tf.constant([[3., 3.]])

# Create another Constant that produces a 2x1 matrix.
matrix2 = tf.constant([[2.],[2.]])

# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.
# The returned value, 'product', represents the result of the matrix
# multiplication.
product = tf.matmul(matrix1, matrix2)

# To run the matmul op we call the session 'run()' method, passing 'product'
# which represents the output of the matmul op.  This indicates to the call
# that we want to get the output of the matmul op back.
#
# All inputs needed by the op are run automatically by the session.  They
# typically are run in parallel.
#
# The call 'run(product)' thus causes the execution of threes ops in the
# graph: the two constants and matmul.
#
# The output of the op is returned in 'result' as a numpy `ndarray` object.
with tf.Session() as sess:
    result = sess.run(product)
    print(result)
    # ==> [[ 12.]]

tf.placeholder(
    dtype,
    shape=None,
    name=None
)# 可以理解为为定义一个张量并为其申请一块空间，并且，以此方式定义的张量必须通过函数参数feed_dict来赋值，Session.run(), Tensor.eval(), Operation.run()

placeholder定义的张量不能直接用于值化运算。

tf.add(
    x,
    y,
    name=None
)# 返回x+y的值，张量加法运算，x,y的shape必须一致

tf.multiply(
    x,
    y,
    name=None
)# 返回x*y的值，张量的乘法运算，shape必须一致

run(
    fetches,
    feed_dict=None, #数据字典，字典的key与fetches中的计算图元素一一对应，并将value赋值给这些张量
    options=None,
    run_metadata=None
)

sess.run(add, feed_dict={a: 2, b: 3}) #将2，3分别赋值给placeholder的a, b，然后执行add加法运算，得到结果

tf.matmul(
    a,
    b,
    transpose_a=False, # 矩阵转置
    transpose_b=False,
    adjoint_a=False, # 共轭矩阵
    adjoint_b=False,
    a_is_sparse=False, # 稀疏矩阵
    b_is_sparse=False,
    name=None
)# 矩阵乘法