tf.cast  |  TensorFlow v2.16.1 (original) (raw)

tf.cast

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Casts a tensor to a new type.

View aliases

Main aliases

tf.dtypes.cast

Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.cast, tf.compat.v1.dtypes.cast

tf.cast(
    x, dtype, name=None
)

Used in the notebooks

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

For example:

x = tf.constant([1.8, 2.2], dtype=tf.float32) tf.cast(x, tf.int32) <tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 2], dtype=int32)>

Notice tf.cast has an alias tf.dtypes.cast:

x = tf.constant([1.8, 2.2], dtype=tf.float32) tf.dtypes.cast(x, tf.int32) <tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 2], dtype=int32)>

The operation supports data types (for x and dtype) ofuint8, 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.

Note casting nan and inf values to integral types has undefined behavior.

Note this operation can lead to a loss of precision when converting native Python float and complex variables to tf.float64 or tf.complex128tensors, since the input is first converted to the float32 data type and then widened. It is recommended to use tf.convert_to_tensor instead oftf.cast for any non-tensor inputs.