TensorFlow:将float64张量转换为float32 [英] TensorFlow: cast a float64 tensor to float32

问题描述

我正在尝试使用:train = optimizer.minimize(loss)，但是标准优化器不适用于tf.float64.因此，我想将loss从tf.float64截断为仅tf.float32.

I am trying to use: train = optimizer.minimize(loss) but the standard optimizers do not work with tf.float64. Therefore I want to truncate my loss from tf.float64 to only 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].

推荐答案

简短的答案是，您可以使用tf.float64转换为tf.float32. org/versions/r0.7/api_docs/python/array_ops.html#cast"rel =" noreferrer> tf.cast() op:

The short answer is that you can convert a tensor from tf.float64 to tf.float32 using the tf.cast() op:

loss = tf.cast(loss, tf.float32)

更长的答案是，这不能解决优化器的所有问题. (缺少对tf.float64的支持是已知问题.)优化器要求您要优化的所有 tf.Variable 对象也必须具有tf.float32类型.

The longer answer is that this will not solve all of your problems with the optimizers. (The lack of support for tf.float64 is a known issue.) The optimizers require that all of the tf.Variable objects that you are trying to optimize must also have type tf.float32.

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