Training.py

# Training the model for each combination of the hyperparameters.

x_train = X_train
x_test, y_test = X_val , y_val

#A unique number for each training session
session_num = 0

#Nested for loop training with all possible  combinathon of hyperparameters
for num_units in HP_NUM_UNITS.domain.values:
  for dropout_rate in tf.linspace(HP_DROPOUT.domain.min_value,HP_DROPOUT.domain.max_value,3):
    for optimizer in HP_OPTIMIZER.domain.values:
      hparams = {
          HP_NUM_UNITS: num_units,
          HP_DROPOUT: float("%.2f"%float(dropout_rate)), # float("%.2f"%float(dropout_rate)) limits the decimal palces to 2
          HP_OPTIMIZER: optimizer,
      }
      run_name = "run-%d" % session_num
      print('--- Starting trial: %s' % run_name)
      print({h.name: hparams[h] for h in hparams})
      run('logs/hparam_tuning/' + run_name, hparams)
      session_num += 1