👀 Views: 57 💬 Answers: 1 📅 Created: 2025-06-19

tensorflow keras model-evaluation Python

Could someone explain I've been banging my head against this for hours. I'm using TensorFlow 2.12 to implement a custom training loop for a simple image classification model. After training the model, I want to evaluate its performance on a validation dataset. However, I'm experiencing unexpected results in the evaluation metrics compared to what I observe during the training phase. Specifically, the accuracy reported by `model.evaluate()` seems significantly lower than the accuracy I see when using `model.predict()` on the same validation dataset. Here’s a snippet of my training loop: ```python import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers # Create a simple model model = keras.Sequential([ layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)), layers.MaxPooling2D(), layers.Flatten(), layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Custom training loop for epoch in range(10): for step, (x_batch, y_batch) in enumerate(train_dataset): with tf.GradientTape() as tape: predictions = model(x_batch, training=True) loss = model.compiled_loss(y_batch, predictions) gradients = tape.gradient(loss, model.trainable_variables) model.optimizer.apply_gradients(zip(gradients, model.trainable_variables)) if step % 10 == 0: print(f"Epoch: {epoch}, Step: {step}, Loss: {loss.numpy()}") # After training, evaluating the model results = model.evaluate(validation_dataset) print(f"Evaluation results: {results}") ``` I have ensured that both the training and validation datasets have the same preprocessing pipeline applied, including normalization and resizing. However, here’s the result I am getting: ``` Evaluation results: [loss: 0.5, accuracy: 0.60] ``` When I run predictions on the validation dataset: ```python predictions = model.predict(validation_dataset) predicted_classes = tf.argmax(predictions, axis=1) accuracy = tf.reduce_mean(tf.cast(predicted_classes == true_labels, tf.float32)).numpy() print(f"Prediction accuracy: {accuracy}") ``` The prediction accuracy comes out to be around 0.85. I’m puzzled why the evaluation metrics differ so much. Is there something I might be doing incorrectly during the evaluation phase, or could there be an issue with how I’m feeding the validation dataset to `evaluate()`? Any insights would be greatly appreciated! This issue appeared after updating to Python LTS. Thanks for your help in advance! I'm working with Python in a Docker container on Ubuntu 20.04. What's the correct way to implement this?