the dynamic has a result, but the static inference shape reports an error

[Isa-Fay]

In some cases, the dynamic has a result, but the static inference shape reports an error.
Here are some samples:

version: keras -- 3.5.0; Jax – 0.4.31

import os
import re
import jax
import numpy as np
os.environ['KERAS_BACKEND']='jax'
import keras

layer = keras.layers.AveragePooling2D(
    pool_size=[ 2, 3 ],
    strides=1,
    padding="valid",
    data_format="channels_first",
    trainable=True,
    autocast=True,
)

result_static = layer.compute_output_shape([1, 4, 4, 1])

result_dynamic = layer(
    inputs=np.random.rand(*[1, 4, 4, 1]),
)

This is what I got for the static result:

and the dynamic result:

version: keras -- 3.5.0; Jax – 0.4.31

import os
import re
import jax
import numpy as np
os.environ['KERAS_BACKEND']='jax'
import keras

layer = keras.layers.CategoryEncoding(
    num_tokens=3,
    output_mode="one_hot",
    trainable=True,
    dtype="int32",
    autocast=True,
)

result_static = layer.compute_output_shape([1, 1])

result_dynamic = layer(
    inputs=np.random.rand(*[1, 1]),
)

The problem also occurs when backend is Torch.
3.
version: keras-- 3.5.0; Torch -- 2.4.0

import os
import re
import torch
import numpy as np
os.environ['KERAS_BACKEND']='torch'
import keras

layer = keras.layers.CategoryEncoding(
    num_tokens=3,
    output_mode="one_hot",
    trainable=True,
    dtype="int32",
    autocast=True,
)

result_static = layer.compute_output_shape([1, 1])

result_dynamic = layer(
    inputs=np.random.rand(*[1, 1]),
)

[mehtamansi29]

Hi @Isa-Fay -

Thanks for reporting the issue. Both issues are reproduce in all backend(jax,tensorflow and torch).

Issue1: Getting the error ValueError: Computed output size would be negative. Received: inputs.shape=[1 4 4 1]andpool_size=[2 3]``
Here AveragePooling2D layer `data_format="channels_first".`
So for input= [1,4,4,1] here, batch_size=1, channel= 4, height= 4, width=1

For padding='valid' : (W−F+2P)/S+1:
(1-2+2 x 0)/1+1= -1
(1-3+2 x 0)/1+1= -2

So changing padding='same' in AveragePooling2D layer will resolve the error.

Issue2: Getting the error: TypeError: can only concatenate list (not "tuple") to list
change layer.compute_output_shape([1, 1]) to layer.compute_output_shape((1, 1)) will resolve the error.

Attached gist for the reference.

[Isa-Fay]

Thanks for you reply!
The input is indeed wrong, but that's not my point. I thought it is unreasonable that the dynamic has a result, but the static inference shape reports an error. If the results are the same( both report error or both have a result ), it would be more user friendly.