Proper way to input a scalar into a Tensorflow 2 model

user_1538049 0
Viewed 311

In my Tensorflow 2 model, I want my batch size to be parametric, such that I can build tensors which have appropriate batch size dynamically. I have the following code:

batch_size_param = 128

tf_batch_size = tf.keras.Input(shape=(), name="tf_batch_size", dtype=tf.int32)
batch_indices = tf.range(0, tf_batch_size, 1)

md = tf.keras.Model(inputs={"tf_batch_size": tf_batch_size}, outputs=[batch_indices])
res = md(inputs={"tf_batch_size": batch_size_param})

The code throws an error in tf.range:

ValueError: Shape must be rank 0 but is rank 1
 for 'limit' for '{{node Range}} = Range[Tidx=DT_INT32](Range/start, tf_batch_size, Range/delta)' with input shapes: [], [?], []

I think the problem is with the fact that tf.keras.Input automatically tries to expand the input array at the first dimension, since it expects the partial shape of the input without the batch size and will attach the batch size according to the shape of the input array, which in my case a scalar. I can just feed the scalar value as a constant integer into tf.range but this time, I won't be able to change it after the model graph has been compiled.

Interestingly, I failed to find a proper way to input only a scalar into a TF-2 model even though I checked the documentation, too. So, what would be the best way to handle such a case?

tensorflow deep-learning python
2 Answers

Don't use tf.keras.Input and just define the model by subclassing.

import tensorflow as tf


class ScalarModel(tf.keras.Model):
    def __init__(self):
        super().__init__()

    def call(self, x):
        return tf.range(0, x, 1)


print(ScalarModel()(10))
# tf.Tensor([0 1 2 3 4 5 6 7 8 9], shape=(10,), dtype=int32)
user_32227970

I'm not sure if this is actually a good idea, but you could use tf.squeeze like

inp = keras.Input(shape=(), dtype=tf.int32)
batch_indices = tf.range(tf.squeeze(inp))
model = keras.Model(inputs=inp, outputs=batch_indices)

so that

model(6)

gives

<tf.Tensor: shape=(6,), dtype=int32, numpy=array([0, 1, 2, 3, 4, 5])>

Edit: Depending on what you want to achieve, it might also be worth looking into ragged tensors:

inp = keras.Input(shape=(), dtype=tf.int32)
batch_indices = tf.ragged.range(inp)
model = keras.Model(inputs=inp, outputs=batch_indices)

would make

model(np.array([6,7]))

return

<tf.RaggedTensor [[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5, 6]]>
user_7743310
Related