在张量流中创建float64变量

Question

我正在尝试进行逻辑回归,我的训练数据集来自一个numpy float64数组.我的代码看起来像,

import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
    examples =tf.constant(mat6)  # mat6 is a numpy float64 array
    t_labels = tf.constant(labels) # labels is an a numpy float64 array
    W = tf.Variable(tf.truncated_normal([115713, 2]))
    b = tf.Variable(tf.zeros([2]))
    logits = tf.matmul(examples, W)+b

这引发了一个例外

TypeError: Input 'b' of 'MatMul' Op has type float32 that does not match type float64 of argument 'a'.

这可能是因为W和b是float32而不是float64.有没有办法转换W和b或创建它作为float64

Answer 1

要使其工作,您应该使用初始值定义W和b变量tf.float64.的tf.truncated_normal()和tf.zeros()OPS每一个可选dtype的是可以被设置为参数tf.float64,如下所示:

    W = tf.Variable(tf.truncated_normal([115713, 2], dtype=tf.float64))
    b = tf.Variable(tf.zeros([2], dtype=tf.float64))