How does one initialize a variable with tf.get_variable and a numpy value in TensorFlow?

Question

I wanted to initialize some of the variable on my network with numpy values. For the sake of the example consider:

init=np.random.rand(1,2) tf.get_variable('var_name',initializer=init) 

when I do that I get an error:

ValueError: Shape of a new variable (var_name) must be fully defined, but instead was <unknown>. 

why is it that I am getting that error?

To try to fix it I tried doing:

tf.get_variable('var_name',initializer=init, shape=[1,2]) 

which yielded a even weirder error:

TypeError: 'numpy.ndarray' object is not callable 

I tried reading the docs and examples but it didn't really help.

Is it not possible to initialize variables with numpy arrays with the get_variable method in TensorFlow?

Answer 1

The following works, if you convert the constant NumPy array into a constant Tensor:

init = tf.constant(np.random.rand(1, 2)) tf.get_variable('var_name', initializer=init) 

The documentation for get_variable is a little lacking indeed. Just for your reference, the initializer argument has to be either a TensorFlow Tensor object (which can be constructed by calling tf.constant on a numpy value in your case), or a 'callable' that takes two arguments, shape and dtype, the shape and data type of the value that it's supposed to return. Again, in your case, you can write the following in case you wanted to use the 'callable' mechanism:

init = lambda shape, dtype: np.random.rand(*shape) tf.get_variable('var_name', initializer=init, shape=[1, 2])