Is there a way to apply a function to dimension 0 of a tensorflow array having the shape (None, 2)

I have a very technical question related to TensorFlow.

I have a TensorFlow matrix having a dimension of (None, 2). I need to apply a function, say some_function, only on Dimension 0 of the matrix i.e. over all rows. The issue is dimension 0 is a None type (it is dynamic as it depends on the input size being fed to the NN model), and it gives an error showing None is not an integer type. There are two tf functions: tf.map_fn and tf.scan to iterate over a Tensorflow array. But both won't work over a None dimension.

Maybe you could check it by defining a test TensorFlow array of shape (None, 2) and try applying any test function to the first dimension. Any help/input would be appreciated!

Since this is a keras model output, if I try to do the following,

res2 = tf.map_fn(lambda y: y*2, model.output)

You get,

> TypeError: 'Tensor' object cannot be interpreted as an integer

But, the following would work,

# Inital model that produces the output you want to map
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(2, input_shape=(2,)))

res = tf.keras.layers.Lambda(lambda x: tf.map_fn(lambda y: y*2, x))(model.output)

Then you define a new model, and use that to get the result of the tf.map_fn.

model2 = tf.keras.Model(inputs=model.inputs, outputs=res)
print(model2.predict(np.array([[1,2],[3,4]])))

PS: But this is nothing to do with the first dimension being None. tf.map_fn can deal with None dimension just fine. You can verify this by running tf.map_fn on a tf.placeholder([None,2]) in TF 1.x.

Because it is iteratively applying a function over that dimension and does not need to know the size to do so.