-
Notifications
You must be signed in to change notification settings - Fork 7
/
plot.py
146 lines (132 loc) · 5.94 KB
/
plot.py
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
import matplotlib.pyplot as plt
import pylab as pl
import pickle
import csv
def read_in(loss_path):
# loss_path = "./saved_model_log/2scnn_1lstm_100_10000_9epoch/org_train_log.txt"
train_losses = {}
cnt = 0
with open(loss_path) as csvDataFile:
csvReader = csv.reader(csvDataFile)
for row in csvReader:
print "here"
train_losses[int(row[0])] = (float(row[1]))
cnt = cnt + 1
train_losses_list = []
keys = sorted(train_losses.keys())
for key in keys:
train_losses_list.append(train_losses[key])
max_len = 5000
if len(train_losses_list) < max_len:
max_len = len(train_losses_list)
# return train_losses_list
return train_losses_list
def read_validation(loss_path):
val_losses = {}
val_lers = {}
cnt = 0
with open(loss_path) as csvDataFile:
csvReader = csv.reader(csvDataFile)
for row in csvReader:
print "here"
val_losses[int(row[0])] = (float(row[2]))
val_lers[int(row[0])] = (float(row[1]))
cnt = cnt + 1
val_losses_list = []
val_lers_list = []
keys = sorted(val_losses.keys())
for key in keys:
val_losses_list.append(val_losses[key])
val_lers_list.append(val_lers[key])
# return train_losses_list
return val_losses_list, val_lers_list
# # 6 models loss vs. batch
# train_losses_list1 = read_in("./saved_model_log/1-2lcnn_1lstm_100_10000_31epoch/1lstm_2cnn_100_train_log.txt");
# train_losses_list2 = read_in("./saved_model_log/2-2lcnn_1bilstm_100_10000_30epoch/1bilstm_2cnn_100_train_log.txt");
# train_losses_list3 = read_in("./saved_model_log/3-2lcnn_2bilstm_100_10000_27epoch/2bilstm_2cnn_100_train_log.txt");
# train_losses_list4 = read_in("./saved_model_log/4-2scnn_1lstm_100_10000_9epoch/org_train_log.txt");
# train_losses_list5 = read_in("./saved_model_log/5-2scnn_2bilstm_100_10000_29epoch/2scnn_2bilstm_train_log.txt");
# train_losses_list6 = read_in("./saved_model_log/6-2scnn_2bilstm_scaled_10000_59epoch/2scnn_2bilstm_scaled_100_train_log.txt");
#
#
# fig = plt.figure(2, figsize=(40, 10))
# # x = range(300)[0:-1:5]
# plt.plot(train_losses_list1)
# plt.plot(train_losses_list2)
# plt.plot(train_losses_list3)
# plt.plot(train_losses_list4)
# plt.plot(train_losses_list5)
# plt.plot(train_losses_list6)
# max_len = max(len(train_losses_list1), len(train_losses_list2), len(train_losses_list3), len(train_losses_list4), len(train_losses_list5), len(train_losses_list6))
# plt.title('MNIST Sequence Recognition: loss vs. batch (batch size = 16)')
# axes = plt.gca()
# axes.set_ylim([0, 2500])
# # axes.set_xlim([500, 2500])
# # axes.set_ylim([0, 3000])
# # axes.set_xlim([0, 20000])
# # axes.set_xlim([100, 200])
# plt.ylabel('loss')
# plt.xlabel('batch')
# plt.legend(['model-1', 'model-2', 'model-3', 'model-4', 'model-5', 'model-6'], loc='upper left')
# plt.xticks([0,100,200,300,400], ["500", "1500", "2500", "3500", "4500"])
# plt.show()
# # plt.savefig("./plots/loss.png")
# # model-6-random train
# train_losses_list6 = read_in("./saved_model_log/6-2scnn_2bilstm_scaled_100_10000_random_retrained_123/2scnn_2bilstm_scaled_100_random_retrained_train_log.txt");
# train_losses_list6 = train_losses_list6[0:-1:50] # scale by 50
# fig = plt.figure(2, figsize=(40, 10))
# plt.plot(train_losses_list6)
# plt.title('Model-6 Training (random): loss vs. batch (batch size = 16)', fontsize=28)
# plt.ylabel('loss', fontsize=18)
# plt.xlabel('batch (*50)', fontsize=18)
# axes = plt.gca()
# # axes.set_ylim([0, 600])
# # axes.set_xlim([500, 2500])
# # plt.legend(['model-1', 'model-2', 'model-3', 'model-4', 'model-5', 'model-6'], loc='upper left')
# plt.xticks([0,100,200,300,400], ["0", "5000", "10000", "15000", "20000"])
# # plt.show()
# plt.savefig("./plots/model-6_random_retrained_train_loss.png")
# model-6-random validation
validation_losses_list6, validation_lers_list6 = read_validation(
"./saved_model_log/6-2scnn_2bilstm_scaled_100_10000_random_retrained_123/2scnn_2bilstm_scaled_100_random_retrained_validation_log.txt");
# validation_losses_list6 = validation_losses_list6[0:-1:50] # scale by 50
fig = plt.figure(2, figsize=(40, 10))
plt.plot(validation_lers_list6)
plt.title('Model-6 Validation (random): LER vs. epoch (batch size = 16)', fontsize=28)
plt.ylabel('LER', fontsize=18)
plt.xlabel('epoch', fontsize=18)
# axes = plt.gca()
# axes.set_ylim([0, 600])
# axes.set_xlim([500, 2500])
# plt.legend(['model-1', 'model-2', 'model-3', 'model-4', 'model-5', 'model-6'], loc='upper left')
# plt.xticks([0, 100, 200, 300, 400], ["0", "5000", "10000", "15000", "20000"])
# plt.show()
plt.savefig("./plots/model-6_random_retrained_validation_ler.png")
# # model-6 validation
# val_losses_list6, val_lers_list6 = read_validation("./saved_model_log/6-2scnn_2bilstm_scaled_10000_59epoch/2scnn_2bilstm_scaled_100_validation_log.txt");
# # # LER
# # fig = plt.figure(2, figsize=(40, 10))
# # plt.plot(val_lers_list6)
# # plt.title('Model-6 Validation: LER vs. epoch', fontsize=28)
# # plt.ylabel('LER(%)', fontsize=18)
# # plt.xlabel('batch', fontsize=18)
# # axes = plt.gca()
# # # axes.set_ylim([0, 600])
# # # axes.set_xlim([500, 2500])
# # # plt.legend(['model-1', 'model-2', 'model-3', 'model-4', 'model-5', 'model-6'], loc='upper left')
# # # plt.xticks([0,500,1000,1500,2000,2500,3000,3500], ["0", "5000", "10000", "15000", "20000", "25000", "30000", "35000"])
# # # plt.show()
# # plt.savefig("./plots/model-6_validation_ler.png")
# # loss
# fig = plt.figure(2, figsize=(40, 10))
# plt.plot(val_losses_list6)
# plt.title('Model-6 Validation: loss vs. epoch', fontsize=28)
# plt.ylabel('loss', fontsize=18)
# plt.xlabel('batch', fontsize=18)
# axes = plt.gca()
# # axes.set_ylim([0, 600])
# # axes.set_xlim([500, 2500])
# # plt.legend(['model-1', 'model-2', 'model-3', 'model-4', 'model-5', 'model-6'], loc='upper left')
# # plt.xticks([0,500,1000,1500,2000,2500,3000,3500], ["0", "5000", "10000", "15000", "20000", "25000", "30000", "35000"])
# # plt.show()
# plt.savefig("./plots/model-6_validation_loss.png")