learning_rates = [0.01, 0.02, 0.04, 0.08, 0.16]
model_state_by_batch_size_and_learning_rate_trial_4 = \
pickle.load(open('pickled_objects/model_state_by_batch_size_and_learning_rate_trial_4.pickle', 'rb'))
model_state_parallelized_by_batch_size =\
pickle.load(open('pickled_objects/model_state_parallelized_by_batch_size.pickle', 'rb'))
initial_model = ml_utils.build_model()
initial_weights = initial_model.get_weights()
models_by_batch_size_and_learning_rate = {}
for batch_size in batch_sizes:
models_by_batch_size_and_learning_rate[batch_size] = {}
for learning_rate in learning_rates:
filepath = 'pickled_objects/batch_size_{}_lr_{}_best_model_trial_4.h5'.format(batch_size, learning_rate)
models_by_batch_size_and_learning_rate[batch_size][learning_rate] = load_model(filepath)```
In the above line of code why are you loading the previous models for your experiment?
