# # Import the data
train_loader, test_loader = data_reader.read_data(flags)
# Reset the boundary if normalized
if flags.normalize_input:
flags.geoboundary_norm = [-1, 1, -1, 1]
print("Geometry boundary is set to:", flags.geoboundary)
# Make Network
print("Making network now")
ntwk = Network(Lorentz, flags, train_loader, test_loader)
# Training process
print("Start training now...")
#ntwk.pretrain()
#ntwk.load_pretrain()
ntwk.train()
# Do the house keeping, write the parameters and put into folder, also use pickle to save the flags object
write_flags_and_BVE(flags, ntwk.best_validation_loss, ntwk.ckpt_dir)
# put_param_into_folder(ntwk.ckpt_dir)
if __name__ == '__main__':
# Read the parameters to be set
flags = flagreader.read_flag()
# Call the train from flag function
training_from_flag(flags)
"""
This .py file is to run train.py for hyper-parameter swipping in a linear fashion.
"""
import train
#os.environ["CUDA_VISIBLE_DEVICE"] = "-1" #Uncomment this line if you want to use CPU only
import numpy as np
import flagreader
if __name__ == '__main__':
#linear_unit_list = [ 50, 100, 200, 500]
#linear_unit_list = [1000, 500]
#linear_unit_list = [1000]
# reg_scale_list = [1e-5, 5e-5, 1e-4, 5e-4, 1e-3, 5e-3]
#reg_scale_list = [5e-4]
for num_lor in range(4, 8):
flags = flagreader.read_flag() #setting the base case
flags.num_lor = num_lor
flags.spectra_save_dir = '/work/sr365/Christian_data_fit/num_lor' + str(
num_lor)
train.training_from_flag(flags)
#for num_gaussian in range(3,15):
# for linear_unit in linear_unit_list:
#for kernel_first in conv_kernel_size_first_list:
# for kernel_second in conv_kernel_size_second_list:
# Setting the loop for setting the parameter
#for i in range(5, 8):
# flags = flag_reader.read_flag() #setting the base case
# flags.data_set = 'sine_wave'
# flags.ckpt_dir = '/work/sr365/MDN_results/' + flags.data_set
# flags.num_gaussian = num_gaussian # Setting the number of gaussians
# linear = [linear_unit for j in range(i)] #Set the linear units
# linear[0] = 1 # The start of linear
test_loader,
inference_mode=True,
saved_model=flags.eval_model)
# Evaluation process
print("Start eval now:")
pred_file, truth_file = ntwk.evaluate()
# Plot the MSE distribution
plotMSELossDistrib_eval(pred_file, truth_file, flags)
print("Evaluation finished")
def evaluate_all(models_dir="models"):
"""
This function evaluate all the models in the models/. directory
:return: None
"""
for file in os.listdir(models_dir):
if os.path.isfile(os.path.join(models_dir, file, 'flags.obj')):
evaluate_from_model(os.path.join(models_dir, file))
return None
if __name__ == '__main__':
# Read the flag, however only the flags.eval_model is used and others are not used
useless_flags = flagreader.read_flag()
print(useless_flags.eval_model)
# Call the evaluate function from model
evaluate_from_model(useless_flags.eval_model)