TensorFlow 2.12: Odd Behavior in Model's Performance Metrics During Validation Phase
I'm training a TensorFlow model using the Keras API, and I've noticed some odd behavior with the performance metrics during the validation phase. While the training loss decreases as expected, the validation accuracy fluctuates wildly instead of showing a smooth improvement. It seems to spike at random epochs, even when the validation loss is consistently decreasing. I've tried various approaches to solve this by adjusting the batch size and learning rate, but the issue persists. Here’s a snippet of my model training code: ```python import tensorflow as tf from tensorflow import keras # Sample data dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(32) validation_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(32) model = keras.Sequential([ keras.layers.Dense(128, activation='relu', input_shape=(input_shape,)), keras.layers.Dense(num_classes, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) history = model.fit(dataset, epochs=10, validation_data=validation_dataset) ``` I also tried using different optimizers like RMSprop and adjusting the learning rate with the LearningRateScheduler callback, but I still see that erratic behavior in validation metrics. My data is normalized, and I've even shuffled it before training. I'm receiving validation accuracy values like this: ``` Epoch 1: val_accuracy: 0.60 Epoch 2: val_accuracy: 0.65 Epoch 3: val_accuracy: 0.58 Epoch 4: val_accuracy: 0.70 Epoch 5: val_accuracy: 0.62 ``` These fluctuations are making it hard to gauge whether the model is actually learning anything useful. Is there a common reason for validation metrics to behave this way, or best practices to ensure more stable validation accuracy? Any insights would be greatly appreciated.