I’m working on a ResNet model using Keras with TensorFlow backend and running into some confusion with the learning_phase parameter.
When I include the learning phase in my feed dictionary like this:
batch_data = {input_x: train_X[batch_indices[i:j]], target_y: train_Y[batch_indices[i:j]], keras.backend.learning_phase(): 1}
My model’s accuracy gets stuck around 10% and barely improves during training. But when I remove the learning_phase from the feed dictionary:
batch_data = {input_x: train_X[batch_indices[i:j]], target_y: train_Y[batch_indices[i:j]]}
The accuracy increases normally as expected through the training epochs.
I’m using keras.backend.set_session(sess) since my ResNet is built with Keras layers. The network has dropout and batch normalization layers, so I thought the learning_phase was required to differentiate between training and inference modes.
Can someone explain why setting learning_phase to 1 causes this accuracy problem? Is the learning_phase actually needed for models with dropout and batch norm, or am I doing something wrong in my implementation?
This happens because you’re mixing Keras high-level training with manual TensorFlow session operations. When you set learning_phase to 1 in your feed dictionary, you force all dropout and batch normalization layers into training mode - but this conflicts with how Keras manages these layers during its own training loop.
I hit the same issue when using custom training loops with Keras models. The fix? Let Keras handle the learning phase automatically by using model.fit() or model.train_on_batch() instead of manual session.run() calls. These methods properly sync the learning phase with layer behaviors.
If you need manual training loops, try tf.keras.utils.get_custom_objects() to access the learning phase state, or switch to model.call(inputs, training=True/False) - that’s the modern TensorFlow 2.x approach for controlling training behavior.
sounds like u might be messin up with learning_phase in ur code. when u leave it out, keras defaults it right. double-check if ur setting it to 0 during training or if there r other set_learning_phase() calls that conflict.
that’s weird! are you calling keras.backend.set_learning_phase(0) somewhere else? sometimes manual phase setting conflicts with the feed dict. what versions of keras/tensorflow are you using?