tf.convert_to_tensor

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Converts the given value to a Tensor.

tf.convert_to_tensor(
    value, dtype=None, dtype_hint=None, name=None
)

This function converts Python objects of various types to Tensor objects. It accepts Tensor objects, numpy arrays, Python lists, and Python scalars. For example:

import numpy as np

def my_func(arg):
  arg = tf.convert_to_tensor(arg, dtype=tf.float32)
  return tf.matmul(arg, arg) + arg

# The following calls are equivalent.
value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))
value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])
value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32))

This function can be useful when composing a new operation in Python (such as my_func in the example above). All standard Python op constructors apply this function to each of their Tensor-valued inputs, which allows those ops to accept numpy arrays, Python lists, and scalars in addition to Tensor objects.

Note: This function diverges from default Numpy behavior for float and string types when None is present in a Python list or scalar. Rather than silently converting None values, an error will be thrown.

Args:

• value: An object whose type has a registered Tensor conversion function.
• dtype: Optional element type for the returned tensor. If missing, the type is inferred from the type of value.
• dtype_hint: Optional element type for the returned tensor, used when dtype is None. In some cases, a caller may not have a dtype in mind when converting to a tensor, so dtype_hint can be used as a soft preference. If the conversion to dtype_hint is not possible, this argument has no effect.
• name: Optional name to use if a new Tensor is created.

Returns:

A Tensor based on value.

Raises:

• TypeError: If no conversion function is registered for value to dtype.
• RuntimeError: If a registered conversion function returns an invalid value.
• ValueError: If the value is a tensor not of given dtype in graph mode.