Update for 04-17-22
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@ -1,4 +1,6 @@
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############### Configuration file for Bayesian ###############
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import os
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layer_type = 'lrt' # 'bbb' or 'lrt'
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activation_type = 'softplus' # 'softplus' or 'relu'
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priors={
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@ -16,4 +18,25 @@ batch_size = 256
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train_ens = 1
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valid_ens = 1
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beta_type = 0.1 # 'Blundell', 'Standard', etc. Use float for const value
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wide = 1
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with open("bay", "r") as file:
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bay = int(file.read())
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if bay == 1:
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with open("tmp", "r") as file:
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wide = int(file.read())
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if os.path.exists("tmp"):
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os.remove("tmp")
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else:
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raise Exception("Tmp file not found")
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print("Bayesian configured to run with width: {}".format(wide))
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if os.path.exists("bay"):
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os.remove("bay")
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else:
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raise Exception("Bay file not found")
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@ -1,7 +1,30 @@
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############### Configuration file for Frequentist ###############
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n_epochs = 200
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import os
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n_epochs = 100
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lr = 0.001
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num_workers = 4
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valid_size = 0.2
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batch_size = 256
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wide = 1
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with open("frq", "r") as file:
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frq = int(file.read())
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if frq == 1:
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with open("tmp", "r") as file:
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wide = int(file.read())
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if os.path.exists("tmp"):
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os.remove("tmp")
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else:
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raise Exception("Tmp file not found")
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print("Frequentist configured to run with width: {}".format(wide))
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if os.path.exists("frq"):
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os.remove("frq")
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else:
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raise Exception("Frq file not found")
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@ -103,16 +103,16 @@ def run(dataset, net_type):
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#early_stop.append(valid_acc)
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#if epoch % 4 == 0 and epoch > 0:
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#print("Value 1: {} >= {}, Value 2: {} >= {}, Value 2: {} >= {}".format(early_stop[0],valid_acc-thrs,early_stop[1],valid_acc-thrs,early_stop[2],valid_acc-thrs))
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#if abs(early_stop[0]) >= valid_acc-thrs and abs(early_stop[1]) >= valid_acc-thrs and abs(early_stop[2]) >= valid_acc-thrs:
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#break
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#early_stop = []
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# print("Value 1: {} >= {}, Value 2: {} >= {}, Value 2: {} >= {}".format(early_stop[0],valid_acc-thrs,early_stop[1],valid_acc-thrs,early_stop[2],valid_acc-thrs))
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# if abs(early_stop[0]) >= valid_acc-thrs and abs(early_stop[1]) >= valid_acc-thrs and abs(early_stop[2]) >= valid_acc-thrs:
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# break
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# early_stop = []
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if train_acc >= 0.99:
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break
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#if train_acc >= 0.99:
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# break
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#if gpu_sample_draw.total_watt_consumed() > 100000:
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#break
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# break
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# save model when finished
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#if epoch == n_epochs:
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