mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 20:20:46 +02:00
69 lines
No EOL
3.2 KiB
Python
69 lines
No EOL
3.2 KiB
Python
from utils import *
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if __name__ == "__main__":
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#### Comparison ####
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## Loss , Acc, Proba = f(epoch) ##
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files=[
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#"res/log/LeNet-100 epochs.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-4 TF)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-4 TF)-LeNet)-100 epochs (dataug:50)- 0 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-3 TF)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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#"res/log/Aug_mod(Data_augV3(Uniform-3 TF)-LeNet)-100 epochs (dataug:50)- 10 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Mix 0,5-3 TF)-LeNet)-100 epochs (dataug:0)- 1 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Mix 0.5-3 TF)-LeNet)-100 epochs (dataug:50)- 10 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-3 TF)-LeNet)-100 epochs (dataug:0)- 10 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-10 TF)-LeNet)-100 epochs (dataug:50)- 10 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-10 TF)-LeNet)-100 epochs (dataug:50)- 0 in_it.json",
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]
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#plot_compare(filenames=files, fig_name="res/compare")
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## Acc, Time, Epochs = f(n_tf) ##
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fig_name="res/TF_seq_tests_compare"
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inner_its = [10]
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dataug_epoch_starts= [0]
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TF_nb = 14 #range(1,14+1)
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N_seq_TF= [1, 2, 3, 4]
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fig, ax = plt.subplots(ncols=3, figsize=(30, 8))
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for in_it in inner_its:
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for dataug in dataug_epoch_starts:
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#filenames =["res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-{} TF)-LeNet)-200 epochs (dataug:{})- {} in_it.json".format(n_tf, dataug, in_it) for n_tf in TF_nb]
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filenames =["res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-{} TF x {})-LeNet)-200 epochs (dataug:{})- {} in_it.json".format(TF_nb, n_tf, dataug, in_it) for n_tf in N_seq_TF]
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filenames =["res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-{} TF x {})-LeNet)-100 epochs (dataug:{})- {} in_it.json".format(TF_nb, n_tf, dataug, in_it) for n_tf in N_seq_TF]
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all_data=[]
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#legend=""
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for idx, file in enumerate(filenames):
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#legend+=str(idx)+'-'+file+'\n'
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with open(file) as json_file:
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data = json.load(json_file)
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all_data.append(data)
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n_tf = N_seq_TF
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#n_tf = [len(x["Param_names"]) for x in all_data]
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acc = [x["Accuracy"] for x in all_data]
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epochs = [len(x["Log"]) for x in all_data]
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time = [x["Time"][0] for x in all_data]
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#for i in range(len(time)): time[i] *= epochs[i] #Estimation temps total
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ax[0].plot(n_tf, acc, label="{} in_it/{} dataug".format(in_it,dataug))
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ax[1].plot(n_tf, time, label="{} in_it/{} dataug".format(in_it,dataug))
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ax[2].plot(n_tf, epochs, label="{} in_it/{} dataug".format(in_it,dataug))
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#for data in all_data:
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#print(np.mean([x["param"] for x in data["Log"]], axis=0))
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#print(len(data["Param_names"]), np.argsort(np.argsort(np.mean([x["param"] for x in data["Log"]], axis=0))))
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ax[0].set_title('Acc')
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ax[1].set_title('Time')
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ax[2].set_title('Epochs')
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for a in ax: a.legend()
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fig_name = fig_name.replace('.',',')
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plt.savefig(fig_name, bbox_inches='tight')
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plt.close() |