How to properly implement training mode in Keras with TensorFlow backend?

Backend
1

I’m working on a deep learning project with a ResNet architecture using Keras and TensorFlow. I’m having trouble with the training phase parameter when feeding data to my model.

Here’s my current setup for the feed dictionary during training:

batch_feed = {input_x: train_data[batch_indices[start_idx:end_idx]], 
              target_y: train_labels[batch_indices[start_idx:end_idx]], 
              keras.backend.learning_phase(): 1}

My network includes dropout and batch normalization layers, so I thought I needed to specify the training phase explicitly. However, when I include keras.backend.learning_phase(): 1 in the feed dictionary, my model’s accuracy stays around 10% and barely improves.

If I remove the learning phase from the feed dictionary like this:

batch_feed = {input_x: train_data[batch_indices[start_idx:end_idx]], 
              target_y: train_labels[batch_indices[start_idx:end_idx]]}

The model trains normally and accuracy increases as expected through the epochs.

I’m confused about when exactly I should use the learning phase parameter. Is it required for networks with dropout and batch norm layers, or am I doing something wrong in my implementation?

2

hmm, that’s interesting - are you mixing up keras high-level API with low-level tensorflow ops? what method are you using to train - model.fit() or custom training loops? also curious about your tensorflow version since learning_phase handling changed a lot between tf1 and tf2.

3

you’re mixing keras api with raw tensorflow sessions - that’s what’s causing the problem. setting learning_phase manually messes with keras’ internal state management. just use model.compile() and model.fit() instead of the feed_dict approach. keras will automatically handle the dropout/batchnorm switching without you needing to control the phase manually.

4

Keras handles the learning phase automatically when you use model.fit() or model.train_on_batch(). Sounds like you’re using TensorFlow 1.x with manual feed dictionaries - that’s deprecated now. TensorFlow 2.x manages the learning phase internally during training calls. If you’re stuck with TF1.x, make sure your model’s compiled correctly and use model.train_on_batch() instead of manual session runs. Since removing the learning phase fixes your training, the framework’s already handling it properly. I’d suggest migrating to tf.keras.Model.fit() or tf.GradientTape for custom training loops - they automatically manage training state for dropout and batch normalization without any manual work.