def enable_numpy_behavior(prefer_float32=False):
"""Enable NumPy behavior on Tensors.
Includes addition of methods, type promotion on operator overloads and
support for NumPy-style slicing.
Args:
prefer_float32: Whether to allow type inference to use float32, or use
float64 similar to NumPy.
"""
ops.enable_numpy_style_type_promotion()
ops.enable_numpy_style_slicing()
np_math_ops.enable_numpy_methods_on_tensor()
np_dtypes.set_prefer_float32(prefer_float32)
def enable_numpy_behavior(prefer_float32=False):
"""Enable NumPy behavior on Tensors.
Enabling NumPy behavior has three effects:
* It adds to `tf.Tensor` some common NumPy methods such as `T`,
`reshape` and `ravel`.
* It changes dtype promotion in `tf.Tensor` operators to be
compatible with NumPy. For example,
`tf.ones([], tf.int32) + tf.ones([], tf.float32)` used to throw a
"dtype incompatible" error, but after this it will return a
float64 tensor (obeying NumPy's promotion rules).
* It enhances `tf.Tensor`'s indexing capability to be on par with
[NumPy's](https://numpy.org/doc/stable/reference/arrays.indexing.html).
Args:
prefer_float32: Controls whether dtype inference will use float32
for Python floats, or float64 (the default and the
NumPy-compatible behavior).
"""
ops.enable_numpy_style_type_promotion()
ops.enable_numpy_style_slicing()
np_math_ops.enable_numpy_methods_on_tensor()
np_dtypes.set_prefer_float32(prefer_float32)
return y, z
with self.assertRaises(TypeError):
f(np.asarray([3, 4]))
def testIndex(self):
@tf.function
def f(x):
return [0, 1][x]
with self.assertRaises(TypeError):
f(np.asarray([1]))
class VariableTest(InteropTest):
def test(self):
tf_var = tf.Variable(2.0)
value = np.square(tf_var)
self.assertIsInstance(value, np.ndarray)
self.assertAllClose(4.0, value)
with tf.control_dependencies([tf_var.assign_add(value)]):
tf_var_value = tf_var.read_value()
self.assertAllClose(6.0, tf_var_value)
if __name__ == '__main__':
ops.enable_numpy_style_type_promotion()
np_math_ops.enable_numpy_methods_on_tensor()
tf.compat.v1.enable_eager_execution()
tf.test.main()
