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runRNNtraining.py
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runRNNtraining.py
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from src.data import *
from src.RNN import *
from src.training import *
def main():
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
CodePath = os.path.dirname(os.path.abspath("__file__"))
# #============TRAINING 1===================
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000
# attention_mechanism="normal"
# RNN_ID = "RNN_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_fulldataset_"+str(dataset_size)+"trials"
# attention_mechanism="cosine"
# RNN_ID = RNN_ID+"_"+attention_mechanism
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #Load dataset
# BATCH_SIZE = 4
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size)
# #train RNN
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, attention_mechanism=attention_mechanism)
# #=================TRAINING 2 (max FCCDdiff=0.5)===================
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000
# maxFCCDdiff = 0.5
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #Load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, restrict_dataset=True, restrict_dict = restrict_dict, size=dataset_size)
# #train RNN
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID)
# #=================TRAINING 3 (max FCCDdiff=0.25)===================
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000
# maxFCCDdiff = 0.25
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #Load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, restrict_dataset=True, restrict_dict = restrict_dict, size=dataset_size)
# #train RNN
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID)
# #=================TRAINING 4 (max FCCDdiff=0.25 and maxDLF=0.25)===================
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000
# maxFCCDdiff = 0.25
# maxDLFdiff = 0.25
# RNN_ID ="RNN_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_maxDLFdiff"+str(maxDLFdiff)+"_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #Load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, restrict_dataset=True, restrict_dict = restrict_dict, size=dataset_size)
# #train RNN
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID)
# #=================TRAINING 5 (max FCCDdiff=0.1)===================
# NUM_EPOCHS = 30 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000
# maxFCCDdiff = 0.1
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #Load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, restrict_dataset=True, restrict_dict = restrict_dict, size=dataset_size)
# #train RNN
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID)
# #=================TRAINING 6 (max FCCDdiff=0.05)===================
# NUM_EPOCHS = 40 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000
# maxFCCDdiff = 0.05
# maxDLFdiff = 1.0 #i.e. no restriction
# attention_mechanism="normal"
# RNN_ID ="RNN_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# attention_mechanism="cosine"
# RNN_ID = RNN_ID+"_"+attention_mechanism
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #Load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, restrict_dataset=True, restrict_dict = restrict_dict, size=dataset_size)
# #train RNN
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, attention_mechanism=attention_mechanism)
# #=================TRAINING 7 (max FCCDdiff=0.01)===================
# NUM_EPOCHS = 80 #can try increasing
# LEARNING_RATE = 0.01 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000
# maxFCCDdiff = 0.01
# maxDLFdiff = 1.0 #i.e. no restriction
# attention_mechanism="normal"
# RNN_ID ="RNN_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# # attention_mechanism="cosine"
# # RNN_ID = RNN_ID+"_"+attention_mechanism
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #Load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, restrict_dataset=True, restrict_dict = restrict_dict, size=dataset_size)
# #train RNN
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, attention_mechanism=attention_mechanism)
# #=================TRAINING 8 (max FCCDdiff=0.005)===================
# NUM_EPOCHS = 40 #can try increasing
# LEARNING_RATE = 0.01 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000
# maxFCCDdiff = 0.005
# maxDLFdiff = 1.0 #i.e. no restriction
# attention_mechanism="normal"
# RNN_ID ="RNN_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# # attention_mechanism="cosine"
# # RNN_ID = RNN_ID+"_"+attention_mechanism
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #Load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, restrict_dataset=True, restrict_dict = restrict_dict, size=dataset_size)
# #train RNN
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, attention_mechanism=attention_mechanism)
# #=================TRAINING 9 FCCD ONLY (FCCD ONLY max FCCDdiff=0.5)===================
# CodePath = os.path.dirname(os.path.abspath("__file__"))
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_FCCDonly/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000
# maxFCCDdiff = 0.05
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_FCCDonly_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load restricted dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, restrict_dataset=True, restrict_dict = restrict_dict, size=dataset_size, path = MC_PATH)
# #run training
# FCCD_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, FCCDonly = True)
# #=================TRAINING 10 MC2 DLF=1, Quantile Regression, no restriction===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# RNN_ID = "RNN_MC2DLF1_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_fulldataset_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 11 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.5===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.5
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 12 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.25===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.25
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 13 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.1===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.1
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 14 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.05===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.05
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 15 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.01===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.01
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# # All norm1_ratioInput
# #=================TRAINING 16 MC2 DLF=1, Quantile Regression, no restriction, ===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 30 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# RNN_ID = "RNN_MC2DLF1_norm1_ratioInput_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_fulldataset_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, normaliseSpectraUnity = True, ratioSpectraRNNInput = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 17 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.5===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 30 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.5
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_ratioInput_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, ratioSpectraRNNInput = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 18 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.25===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 30 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.25
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_ratioInput_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, ratioSpectraRNNInput = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 19 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.1===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 30 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.1
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_ratioInput_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, ratioSpectraRNNInput = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 20 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.05===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 30 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.05
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_ratioInput_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, ratioSpectraRNNInput = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 21 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.01===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 30 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.01
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_ratioInput_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, ratioSpectraRNNInput = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# # All norm1, separate scalers
# # #=================TRAINING 22 MC2 DLF=1, Quantile Regression, no restriction, ===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# RNN_ID = "RNN_MC2DLF1_norm1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_fulldataset_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, normaliseSpectraUnity = True, separate_scalers=True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 23 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.5===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.5
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 24 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.25===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.25
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 25 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.1===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.1
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 26 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.05===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.05
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 27 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.01===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.01
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_norm1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, normaliseSpectraUnity = True, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# # all separate scalers
# # #=================TRAINING 28 MC2 DLF=1, Quantile Regression, no restriction, ===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# RNN_ID = "RNN_MC2DLF1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_fulldataset_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, separate_scalers=True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 29 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.5===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.5
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 30 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.25===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.25
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 31 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.1===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.1
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 32 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.05===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.05
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 33 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.01===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.01
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_2scalers_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, separate_scalers = True)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
#maxE=425
# #=================TRAINING 34 MC2 DLF=1, Quantile Regression, no restriction===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# RNN_ID = "RNN_MC2DLF1_maxE425_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_fulldataset_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, maxE=425)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 35 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.5===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.5
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_maxE425_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, maxE=425)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 36 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.25===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.25
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_maxE425_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, maxE=425)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 37 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.1===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.1
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_maxE425_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, maxE=425)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 38 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.05===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.05
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_maxE425_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, maxE=425)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 39 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.01===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.01
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_maxE425_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, maxE=425)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
#maxE=400
# #=================TRAINING 40 MC2 DLF=1, Quantile Regression, no restriction===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# RNN_ID = "RNN_MC2DLF1_maxE400_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_fulldataset_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, maxE=400)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 41 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.5===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.5
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_maxE400_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, maxE=400)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 42 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.25===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000
# maxFCCDdiff = 0.25
# maxDLFdiff = 1.0 #i.e. no restriction
# RNN_ID ="RNN_MC2DLF1_maxE400_"+str(NUM_EPOCHS)+"epochs_LR"+str(LEARNING_RATE)+"_maxFCCDdiff"+str(maxFCCDdiff)+"mm_"+str(dataset_size)+"trials"
# #initialise directories to save
# if not os.path.exists(CodePath+"/saved_models/"+RNN_ID+"/plots/"):
# os.makedirs(CodePath+"/saved_models/"+RNN_ID+"/plots/")
# #load dataset
# BATCH_SIZE = 4
# restrict_dict = {"maxFCCDdiff": maxFCCDdiff, "maxDLFdiff": maxDLFdiff}
# train_loader, test_loader, dataset = load_data(BATCH_SIZE, size=dataset_size, path_MC2 = MC_PATH_FCCDonly, restrict_dataset=True, restrict_dict = restrict_dict, maxE=400)
# #run training
# FCCD_accuracy_values, DLF_accuracy_values, loss_values = train_RNN(dataset, train_loader, test_loader, NUM_EPOCHS, LEARNING_RATE, saveRNN=True, plot_training_results = True, RNN_ID = RNN_ID, quantileRegressionDLF=True)
# #=================TRAINING 43 MC2 DLF=1, Quantile Regression, restriction max FCCD 0.1===================
# MC_PATH = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_randomDLFs/"
# MC_PATH_FCCDonly = CodePath+"/data/V05268A_data/training_data_V05268A_5000randomFCCDs_DLF1/"
# NUM_EPOCHS = 20 #can try increasing
# LEARNING_RATE = 0.005 #0.01 #try modifying learning rate #0.001 too low for 30 epochs, 0.01 may be too high
# dataset_size = 10000 #10000