SeismoCNN/plotting.py at master · NLaureano/SeismoCNN · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
import matplotlib.pyplot as plt
import numpy as np

Training_Losses = [[780.3878665268421, 581.6063185892999, 509.24479307979345, 473.68092453479767, 435.40785744041204, 409.9606028422713, 386.8613458201289, 383.1757463812828, 361.79274827986956, 346.7525302246213], [776.4225708395243, 539.6479548513889, 503.95510017871857, 481.2114467471838, 465.536960683763, 435.6762602701783, 407.45245469361544, 387.27348294854164, 366.6152773499489, 348.679227553308], [841.2095796465874, 568.2857456505299, 483.4862739741802, 457.5858663097024, 453.79579884558916, 413.00536911934614, 392.6221082061529, 409.8684525862336, 380.01608975976706, 367.42171075195074], [990.6524220705032, 622.4001437574625, 538.1126797050238, 489.4057023078203, 486.61567682772875, 460.04761277139187, 428.297591149807, 417.99451257288456, 417.3618930131197, 418.8829487338662]]
Training_Accuracies = [[0.7289428571428571, 0.7939142857142857, 0.8184, 0.8315142857142858, 0.8422285714285714, 0.8528571428571429, 0.8578857142857143, 0.862, 0.8676857142857143, 0.8721428571428571], [0.7370571428571429, 0.8049142857142857, 0.8162285714285714, 0.8234, 0.8263428571428572, 0.838, 0.8469142857142857, 0.8552571428571428, 0.8594571428571428, 0.8644], [0.7157428571428571, 0.7896857142857143, 0.8162571428571429, 0.8236857142857142, 0.8285714285714286, 0.8378285714285715, 0.8428, 0.8438285714285715, 0.8508, 0.8574285714285714], [0.6723428571428571, 0.7699428571428572, 0.8027714285714286, 0.8177142857142857, 0.8169428571428572, 0.8274, 0.834, 0.8385714285714285, 0.8427142857142857, 0.8428]]
Validation_Losses = [[0.6271174035634205, 0.5896686325027685, 0.523745452617384, 0.5571746066877037, 0.5230749579751568, 0.6285985271642163, 0.6500235662149016, 0.5262037481471991, 0.49357809591445195, 0.5077917370352016], [1.872074731216309, 0.7414536414442549, 0.7337636250029703, 0.7094546020220799, 1.0167939792013472, 0.5955478801469135, 0.6761993825625462, 0.5525030018702434, 0.5761520881561717, 0.5346236705400382], [0.7448047016076981, 1.0641262547890091, 0.6455805394679878, 0.6867536638572718, 0.6816401791990183, 0.707659982571936, 0.7040170328632281, 0.5721463266831295, 0.8069588844278816, 0.6675466004830257], [0.8653136119721042, 1.0585165038989608, 0.7571059402766501, 0.7568725573409135, 0.7346201937669402, 0.7861211067362196, 0.5777224520589136, 1.1513410559885062, 0.585433115435254, 0.633964406456917]]
Validation_Accuracies = [[0.7362, 0.763, 0.8076, 0.7988, 0.7818, 0.7914, 0.7744, 0.8102, 0.8174, 0.8316], [0.543, 0.7966, 0.7748, 0.7838, 0.7204, 0.8058, 0.7974, 0.8142, 0.772, 0.788], [0.7374, 0.781, 0.7622, 0.7312, 0.749, 0.7324, 0.7094, 0.7832, 0.7846, 0.7396], [0.7308, 0.6718, 0.7656, 0.7778, 0.7432, 0.7564, 0.7672, 0.3064, 0.783, 0.7494]]

Wd_Training_Losses = [[841.2095796465874, 568.2857456505299, 483.4862739741802, 457.5858663097024, 453.79579884558916, 413.00536911934614, 392.6221082061529, 409.8684525862336, 380.01608975976706, 367.42171075195074], [831.6726414561272, 575.2061340212822, 487.39878426492214, 455.71661818027496, 434.4057615250349, 419.019670791924, 403.54747304320335, 411.90213610231876, 406.8481795862317, 399.38269159942865], [786.9764240682125, 593.3104690313339, 538.3415373712778, 497.5261242315173, 473.00523414462805, 434.5810895934701, 462.0147164352238, 408.1247585788369, 393.1515834480524, 384.5563756301999], [809.4327759891748, 580.9143016934395, 486.47448921203613, 457.16357868909836, 445.563773997128, 414.90574729442596, 408.2754151150584, 393.12286181747913, 394.4019312784076, 379.91125517338514], [828.1145914793015, 597.3572927564383, 515.882151633501, 465.69048123061657, 440.66657135635614, 438.30880331248045, 426.96665946394205, 429.21672513335943, 412.44569125398993, 405.3992765918374]]
Wd_Training_Accuracies = [[0.7157428571428571, 0.7896857142857143, 0.8162571428571429, 0.8236857142857142, 0.8285714285714286, 0.8378285714285715, 0.8428, 0.8438285714285715, 0.8508, 0.8574285714285714], [0.7174571428571429, 0.7867714285714286, 0.8147714285714286, 0.8252857142857143, 0.835, 0.8399714285714286, 0.8456857142857143, 0.8446285714285714, 0.8459714285714286, 0.8480571428571428], [0.7315142857142857, 0.7834571428571429, 0.8023428571428571, 0.818, 0.8291714285714286, 0.8359142857142857, 0.8328285714285715, 0.8451428571428572, 0.8510571428571428, 0.8547714285714286], [0.7236, 0.7889714285714285, 0.8194, 0.8281142857142857, 0.8336, 0.8425142857142857, 0.8438, 0.8500571428571428, 0.8523142857142857, 0.8555714285714285], [0.7246857142857143, 0.7851142857142858, 0.8047142857142857, 0.825, 0.8326285714285714, 0.8351714285714286, 0.8388285714285715, 0.8382571428571428, 0.8424, 0.8452571428571428]]
Wd_Validation_Losses = [[0.7448047016076981, 1.0641262547890091, 0.6455805394679878, 0.6867536638572718, 0.6816401791990183, 0.707659982571936, 0.7040170328632281, 0.5721463266831295, 0.8069588844278816, 0.6675466004830257], [0.7634917664679752, 0.6521409674055257, 0.6752629919796233, 0.5442843482752514, 0.6654253274581994, 0.5629707329022656, 1.2454043419877434, 0.7241836755898348, 0.6206670518323874, 0.596277787427234], [0.671646724840638, 0.6770330184394386, 0.822033466806837, 0.669817436272931, 0.5637118267785212, 0.5941981938993854, 0.5685651527278742, 0.7726061647864664, 0.522720488867942, 0.5324620972773072], [0.7426626613944959, 0.7684425377542046, 0.6554242628774826, 0.6032808061428131, 0.7442352769860796, 0.7014497489116753, 0.8654787819951203, 0.6660745072231931, 0.7246752438271881, 0.6487540388183229], [0.8125096806295359, 0.7083148985721504, 0.6673957503335491, 0.6413318949520208, 0.5861541786391264, 0.6779263230266085, 0.7645442657599784, 0.5079788653903706, 0.6431882809491674, 0.6505132339372757]]
Wd_Validation_Accuracies = [[0.7374, 0.781, 0.7622, 0.7312, 0.749, 0.7324, 0.7094, 0.7832, 0.7846, 0.7396], [0.7672, 0.7448, 0.7562, 0.7862, 0.7714, 0.796, 0.7326, 0.7714, 0.782, 0.7348], [0.7424, 0.7738, 0.6848, 0.7464, 0.796, 0.8128, 0.7946, 0.728, 0.8096, 0.7822], [0.7356, 0.724, 0.7478, 0.7808, 0.774, 0.7906, 0.774, 0.7976, 0.7594, 0.7826], [0.7174, 0.7832, 0.7638, 0.7842, 0.797, 0.775, 0.7752, 0.7918, 0.7824, 0.7122]]


learning_rates = ['1e-4', '5e-4', '1e-3 [DEFAULT]', '3e-3']
weightDecayRates = ['1e-5 [DEFAULT]', '0', '1e-6', '1e-4', '1e-3'] #Default = 1e-5
epochs = list(range(1, 11))

# Plot Training Loss
plt.figure(figsize=(10, 6))
for i, loss in enumerate(Training_Losses):
    plt.plot(epochs, loss, label=f'lr={learning_rates[i]}')
plt.title("[By Learning Rate] Training Loss vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# Plot Training Accuracy
plt.figure(figsize=(10, 6))
for i, acc in enumerate(Training_Accuracies):
    plt.plot(epochs, acc, label=f'lr={learning_rates[i]}')
plt.title("[By Learning Rate] Training Accuracy vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# Plot Validation Loss
plt.figure(figsize=(10, 6))
for i, val_loss in enumerate(Validation_Losses):
    plt.plot(epochs, val_loss, label=f'lr={learning_rates[i]}')
plt.title("[By Learning Rate] Validation Loss vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# Plot Validation Accuracy
plt.figure(figsize=(10, 6))
for i, val_acc in enumerate(Validation_Accuracies):
    plt.plot(epochs, val_acc, label=f'lr={learning_rates[i]}')
plt.title("[By Learning Rate] Validation Accuracy vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

#---------------------------------------------------------------

# WD Plot Training Loss
plt.figure(figsize=(10, 6))
for i, loss in enumerate(Wd_Training_Losses):
    plt.plot(epochs, loss, label=f'wd={weightDecayRates[i]}')
plt.title("[By Weight Decay] Training Loss vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# WD Plot Training Accuracy
plt.figure(figsize=(10, 6))
for i, acc in enumerate(Wd_Training_Accuracies):
    plt.plot(epochs, acc, label=f'wd={weightDecayRates[i]}')
plt.title("[By Weight Decay] Training Accuracy vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# WD Plot Validation Loss
plt.figure(figsize=(10, 6))
for i, val_loss in enumerate(Wd_Validation_Losses):
    plt.plot(epochs, val_loss, label=f'wd={weightDecayRates[i]}')
plt.title("[By Weight Decay] Validation Loss vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# WD Plot Validation Accuracy
plt.figure(figsize=(10, 6))
for i, val_acc in enumerate(Wd_Validation_Accuracies):
    plt.plot(epochs, val_acc, label=f'wd={weightDecayRates[i]}')
plt.title("[By Weight Decay] Validation Accuracy vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

#---------------------------On scales 0-1
# Plot Training Accuracy
plt.figure(figsize=(10, 6))
for i, acc in enumerate(Training_Accuracies):
    plt.plot(epochs, acc, label=f'lr={learning_rates[i]}')
plt.title("[0 to 1] [By Learning Rate] Training Accuracy vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.ylim(0, 1)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# Plot Validation Accuracy
plt.figure(figsize=(10, 6))
for i, val_acc in enumerate(Validation_Accuracies):
    plt.plot(epochs, val_acc, label=f'lr={learning_rates[i]}')
plt.title("[0 to 1] [By Learning Rate] Validation Accuracy vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.ylim(0, 1)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# WD Plot Training Accuracy
plt.figure(figsize=(10, 6))
for i, acc in enumerate(Wd_Training_Accuracies):
    plt.plot(epochs, acc, label=f'wd={weightDecayRates[i]}')
plt.title("[0 to 1] [By Weight Decay] Training Accuracy vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.ylim(0, 1)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# WD Plot Validation Accuracy
plt.figure(figsize=(10, 6))
for i, val_acc in enumerate(Wd_Validation_Accuracies):
    plt.plot(epochs, val_acc, label=f'wd={weightDecayRates[i]}')
plt.title("[0 to 1] [By Weight Decay] Validation Accuracy vs Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.ylim(0, 1)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()