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Enable NumPy behavior on Tensors.

Used in the notebooks

Used in the guide

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.

prefer_float32 Controls whether dtype inference will use float32 for Python floats, or float64 (the default and the NumPy-compatible behavior).