tf.cast()的用法

最新推荐文章于 2022-10-14 09:22:02 发布

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定义方式：

def cast(x, dtype, name=None)

解释：

修改张量数据的数据类型

x：待修改数据类型的数据

dtype：目标数据类型

name：可选参数，即定义操作的名称

cast在tensorflow的代码位置：tensorflow/python/ops/math_ops.py

具体的定义如下所示：

def cast(x, dtype, name=None):
  """Casts a tensor to a new type.

  The operation casts `x` (in case of `Tensor`) or `x.values`
  (in case of `SparseTensor` or `IndexedSlices`) to `dtype`.

  For example:

  ```python
  x = tf.constant([1.8, 2.2], dtype=tf.float32)
  tf.cast(x, tf.int32)  # [1, 2], dtype=tf.int32
  ```

  The operation supports data types (for `x` and `dtype`) of
  `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `int32`, `int64`,
  `float16`, `float32`, `float64`, `complex64`, `complex128`, `bfloat16`.
  In case of casting from complex types (`complex64`, `complex128`) to real
  types, only the real part of `x` is returned. In case of casting from real
  types to complex types (`complex64`, `complex128`), the imaginary part of the
  returned value is set to `0`. The handling of complex types here matches the
  behavior of numpy.

  Args:
    x: A `Tensor` or `SparseTensor` or `IndexedSlices` of numeric type. It could
      be `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `int32`,
      `int64`, `float16`, `float32`, `float64`, `complex64`, `complex128`,
      `bfloat16`.
    dtype: The destination type. The list of supported dtypes is the same as
      `x`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` or `IndexedSlices` with same shape as `x` and
      same type as `dtype`.

  Raises:
    TypeError: If `x` cannot be cast to the `dtype`.
  """
  base_type = dtypes.as_dtype(dtype).base_dtype
  if isinstance(x,
                (ops.Tensor, _resource_variable_type)) and base_type == x.dtype:
    return x
  with ops.name_scope(name, "Cast", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      values_cast = cast(x.values, base_type, name=name)
      x = sparse_tensor.SparseTensor(x.indices, values_cast, x.dense_shape)
    elif isinstance(x, ops.IndexedSlices):
      values_cast = cast(x.values, base_type, name=name)
      x = ops.IndexedSlices(values_cast, x.indices, x.dense_shape)
    else:
      # TODO(josh11b): If x is not already a Tensor, we could return
      # ops.convert_to_tensor(x, dtype=dtype, ...)  here, but that
      # allows some conversions that cast() can't do, e.g. casting numbers to
      # strings.
      x = ops.convert_to_tensor(x, name="x")
      if x.dtype.base_dtype != base_type:
        x = gen_math_ops.cast(x, base_type, name=name)
    if x.dtype.is_complex and base_type.is_floating:
      logging.warn("Casting complex to real discards imaginary part.")
    return x

例子：

import tensorflow as tf


def tf_cast_1():
    t1 = tf.Variable([1, 2, 3, 4, 5, 6])
    t2 = tf.cast(t1, dtype=tf.float32)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        print('t1', t1, sess.run(t1))
        print('t2', t2, sess.run(t2))


tf_cast_1()

运行的结果如下所示：