👀 Views: 2 💬 Answers: 1 📅 Created: 2025-06-10

tensorflow image-classification mnist Python

I'm integrating two systems and I'm working on a project and hit a roadblock... I'm currently working on an image classification project using TensorFlow 2.8.0 and I'm working with a persistent behavior when trying to reshape my input tensor. My dataset consists of 28x28 grayscale images, and I'm using the following code to load and preprocess the data: ```python import tensorflow as tf from tensorflow.keras.datasets import mnist # Load the dataset (train_images, train_labels), (test_images, test_labels) = mnist.load_data() # Normalize the images to the range [0, 1] train_images = train_images.astype('float32') / 255.0 test_images = test_images.astype('float32') / 255.0 # Reshape images to add a channel dimension tain_images = train_images.reshape((-1, 28, 28, 1)) test_images = test_images.reshape((-1, 28, 28, 1)) ``` Upon running the model training with the following code: ```python model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, train_labels, epochs=5) ``` I receive the following behavior: ``` ValueError: Input to reshape is a tensor with 60000 values, but the requested shape has 0 ``` I have double-checked the shape of `train_images` before the reshape operation, and it appears to be correct `(60000, 28, 28)`. I also ensured that the data type is consistent. Additionally, I tried using `np.expand_dims()` to add the channel dimension, but it resulted in the same behavior. Has anyone encountered this scenario before, or could there be something I'm missing in the preprocessing steps? Any help would be greatly appreciated! Am I missing something obvious? I'm developing on Debian with Python. Thanks for taking the time to read this!