System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows, Ubuntu 18.04
• TensorFlow installed from (source or binary): binary
• TensorFlow version (use command below): 2.4.0, nightly
• Python version: 3.7
• CUDA/cuDNN version: 11.0, 8.0.4
• GPU model and memory: 1060, 6GB

Describe the current behavior
When loading weights with skip_mismatch=True, and if the stored weights don't match with the current layer weights,
the warning seems to show misleading shapes for the stored weights.

Describe the expected behavior
I have attached a minimal code to produce the warning. The warning seems confusing. The expected shapes of weights shown in
the warning should be ((3, 4, 7, 32) vs (3, 4, 7, 16)) rather than ((3, 4, 7, 32) vs (16, 7, 3, 4)). I suspect the transpose here is causing the change of shape.

Standalone code to reproduce the issue

import tensorflow as tf

model = tf.keras.Sequential(
    tf.keras.layers.Conv2D(16, [3, 4], use_bias=False, input_shape=[10, 10, 7]))
model.save_weights('test.h5')

tf.keras.backend.clear_session()

new_model = tf.keras.Sequential(
    tf.keras.layers.Conv2D(32, [3, 4], use_bias=False, input_shape=[10, 10, 7]))
new_model.load_weights('test.h5', skip_mismatch=True, by_name=True)

WARNING:tensorflow:Skipping loading of weights for layer conv2d due to mismatch in shape ((3, 4, 7, 32) vs (16, 7, 3, 4)).