Tensorflow Estimator - High evaluation values on training data

Question

I'm using Tensorflow 1.10 with a custom Estimator. To test my training/evaluation loop, I just feed the same image/label into the network every time, so I expected the network to converge fast, which it does.

I'm also using the same image for evaluation, but get a much bigger loss value than when training. After training 2000 steps the loss is:

INFO:tensorflow:Loss for final step: 0.01181452

but evaluates to:

Eval loss at step 2000: 0.41252694

This seems wrong to me. It looks like the same problem as in this thread. Is there something special to consider, when using the evaluate method of Estimator ?

---

Some more details about my code:

I've defined my model (FeatureNet) like here as an inheritance of tf.keras.Model with init and call method.

My model_fn looks like this:

def model_fn(features, labels, mode):

    resize_shape = (180, 320)
    num_dimensions = 16

    model = featurenet.FeatureNet(resize_shape, num_dimensions=num_dimensions)

    training = (mode == tf.estimator.ModeKeys.TRAIN)
    seg_pred = model(features, training)

    predictions = {
       # Generate predictions (for PREDICT mode)
       "seg_pred": seg_pred
    }
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    # Calculate Loss (for both TRAIN and EVAL modes)
    seg_loss = tf.reduce_mean(tf.keras.backend.binary_crossentropy(labels['seg_true'], seg_pred))
    loss = seg_loss

    # Configure the Training Op (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.MomentumOptimizer(learning_rate=1e-4, momentum=0.9)

        train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())

        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

    # Add evaluation metrics (for EVAL mode)
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss)

Then in the main-part I train and evaluate with an custom Estimator:

# Create the Estimator
estimator = tf.estimator.Estimator(
    model_fn=model_fn,
    model_dir="/tmp/discriminative_model"
    )

def input_fn():
    features, labels = create_synthetic_image()

    training_data = tf.data.Dataset.from_tensors((features, labels))
    training_data = training_data.repeat(None)
    training_data = training_data.batch(1)
    training_data = training_data.prefetch(1)
    return training_data

estimator.train(input_fn=lambda: input_fn(), steps=2000)
eval_results = estimator.evaluate(input_fn=lambda: input_fn(), steps=50)
print('Eval loss at step %d: %s' % (eval_results['global_step'], eval_results['loss']))

Where create_synthetic_image creates the same image/label every time.

Answer 1

I've found, that the handling of BatchNormalization can cause such errors, like described here .

The usage of the control_dependencies in the model-fn solved the issue for me ( see here ).

if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.MomentumOptimizer(learning_rate=1e-4, momentum=0.9)

    with tf.control_dependencies(model.get_updates_for(features)):
        train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())

    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)