Tensorflow相关基础概念

1. tf.placeholder
A placeholder is simply a variable that we will assign data to at a later date. It allows us to create our operations and build our computation graph, without needing the data. In TensorFlow terminology, we then feed data into the graph through these placeholders.

import tensorflow as tf x = tf.placeholder("float", None) y = x * 2 with tf.Session() as session: result = session.run(y, feed_dict={x: [1, 2, 3]}) print(result)

2. tf.matmul
矩阵相乘

3. tf.Variable

import tensorflow as tf x = tf.constant(35, name='x') y = tf.Variable(x + 5, name='y') print(y)  # y.value 0 model = tf.global_variables_initializer() with tf.Session() as session:     session.run(model)     print(session.run(y))  # 40