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.
I've found, that the handling of
BatchNormalization
can cause such errors, like described here.The usage of the
control_dependencies
in themodel-fn
solved the issue for me (see here).