小编Kar*_*kan的帖子

我正在尝试使用:train = optimizer.minimize(loss)但标准优化器无法使用tf.float64.因此,我想我的截断loss从tf.float64只tf.float32.

Traceback (most recent call last):
  File "q4.py", line 85, in <module>
    train = optimizer.minimize(loss)
  File "/Library/Python/2.7/site-packages/tensorflow/python/training/optimizer.py", line 190, in minimize
    colocate_gradients_with_ops=colocate_gradients_with_ops)
  File "/Library/Python/2.7/site-packages/tensorflow/python/training/optimizer.py", line 229, in compute_gradients
    self._assert_valid_dtypes([loss])
  File "/Library/Python/2.7/site-packages/tensorflow/python/training/optimizer.py", line 354, in _assert_valid_dtypes
    dtype, t.name, [v for v in valid_dtypes]))
ValueError: Invalid type tf.float64 for Add_1:0, expected: [tf.float32].

如何找到MXNet符号中保存的实际数值.

假设我有,

x = mx.sym.Variable('x')
y = mx.sym.Variable('y')
z = x + y, 

如果x = [100,200]且y = [300,400],我想打印:

z = [400,600],

有点像tensorflow的eval()方法

我在MXNet中有2个符号,并希望将它们连接起来.我怎样才能做到这一点:

例如:a = [100,200],,我b = [300,400]想得到

c = [100,200,300,400]