👀 Views: 80 💬 Answers: 1 📅 Created: 2025-06-05

tensorflow keras machine-learning Python

I've been struggling with this for a few days now and could really use some help. I need some guidance on I'm trying to configure I'm writing unit tests and I've been struggling with this for a few days now and could really use some help..... I'm working with a 'ValueError' when trying to make predictions with my Keras model in TensorFlow 2.10. The model was trained on input data shaped (None, 28, 28, 1) but when I try to predict with a new dataset of shape (10, 28, 28), I'm getting the behavior: ``` ValueError: Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 28, 28, 1), found shape=(10, 28, 28) ``` I’ve reshaped my input data using `numpy`: ```python import numpy as np new_data = np.random.rand(10, 28, 28) # Shape (10, 28, 28) new_data = new_data.reshape(10, 28, 28, 1) # Trying to change shape ``` But I’m still running into the same behavior. I suspect it might be related to how I’m handling the data preprocessing. I also checked my model summary and the input shape is indeed (None, 28, 28, 1). Here’s how I’m loading and compiling the model: ```python from tensorflow.keras.models import load_model model = load_model('my_model.h5') model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) ``` Could there be something I’m missing in the input pipeline or the shape transformations? Also, if it’s relevant, this model was trained with a dataset that included grayscale images. Any insights or suggestions on how to resolve this behavior would be greatly appreciated! Has anyone else encountered this? My development environment is CentOS. Am I missing something obvious? This is happening in both development and production on CentOS. How would you solve this? I've been using Python for about a year now. Thanks, I really appreciate it! I've been using Python for about a year now.