👀 Views: 25 💬 Answers: 1 📅 Created: 2025-06-10

tensorflow machine-learning data-augmentation Python

I'm working on a personal project and This might be a silly question, but I'm currently working on a TensorFlow 2.9 project where I'm trying to build a model for image classification... I am using the `tf.data` API to load and augment my images, but I'm encountering an `InvalidArgumentError` that states 'Input 0 of layer conv2d is incompatible with the layer: expected axis -1 of input shape to have value 3 but received input with shape (None, 32, 32, 1)'. Here's the relevant code snippet where I create the dataset and apply the augmentation: ```python import tensorflow as tf # Load dataset (using MNIST for simplicity) (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() # Normalize and expand dimensions for grayscale images x_train = x_train.astype('float32') / 255.0 x_train = tf.expand_dims(x_train, axis=-1) # shape becomes (60000, 28, 28, 1) # Create a tf.data dataset train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_dataset = train_dataset.shuffle(10000) # Data augmentation def augment(image, label): image = tf.image.resize(image, [32, 32]) # Resize image to 32x32 image = tf.image.random_flip_left_right(image) return image, label train_dataset = train_dataset.map(augment).batch(32) ``` I tried several approaches to resolve this, including modifying the way I expand dimensions and ensuring that my model's input shape matches the augmented output. However, the error persists when I attempt to fit the model: ```python model = tf.keras.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_dataset, epochs=5) ``` The model expects three channels (RGB) but my images are still in single channel format after augmentation. I've confirmed that the resizing is working correctly and that the images are converted to the right format, but I'm still not getting the channel count right. What am I missing here? How can I ensure that my input images have 3 channels after augmentation without modifying the original dataset? For context: I'm using Python on macOS. What's the best practice here? I'm working in a Linux environment. Any ideas how to fix this?