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plotting_results.py
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from train_config import *
import os
DIR = os.path.dirname(os.path.realpath(__file__))
single = [0.19634703, 0.24657534, 0.21613394, 0.22678843, 0.22983257, 0.24657534, 0.19330288, 0.21613394, 0.23135464, 0.20547946, 0.23896499, 0.20547946, 0.23135464, 0.21308981, 0.21765602, 0.20547946, 0.22678843, 0.1978691, 0.23135464, 0.21917808, 0.21004567, 0.20243531, 0.22070014, 0.23592085, 0.22374429, 0.21765602, 0.23135464, 0.20243531, 0.2283105, 0.21461187, 0.19025876, 0.1978691, 0.20700152, 0.20547946, 0.20243531, 0.20852359, 0.20243531, 0.19178082, 0.22678843, 0.21765602, 0.20243531, 0.21156773, 0.22070014, 0.20700152, 0.21156773, 0.21461187, 0.21613394, 0.20091324, 0.19939117, 0.20700152, 0.22222222, 0.19939117, 0.21461187, 0.21308981, 0.19634703, 0.20243531, 0.20091324, 0.21461187, 0.21156773, 0.20243531, 0.1978691, 0.21765602, 0.22526637, 0.24505328, 0.20091324, 0.19634703, 0.19330288, 0.21156773, 0.20852359, 0.20243531, 0.20547946, 0.21004567, 0.21613394, 0.21917808, 0.19482496, 0.19634703, 0.21156773, 0.20700152, 0.20395738, 0.20091324, 0.19178082, 0.19939117, 0.21004567, 0.20852359, 0.21004567, 0.20243531, 0.20700152, 0.21765602, 0.19634703, 0.21004567, 0.1978691, 0.21004567, 0.22678843, 0.24200913, 0.20243531, 0.20547946, 0.20091324, 0.20700152, 0.20091324, 0.21004567]
lstm = [0.2678843, 0.24961948, 0.23592085, 0.24809742, 0.27701673, 0.27092847, 0.30898023, 0.3759513, 0.38812786, 0.37747335, 0.41248098, 0.39878234, 0.41248098, 0.43378997, 0.39878234, 0.44292238, 0.4611872, 0.4977169, 0.5235921, 0.4824962, 0.51598173, 0.50380516, 0.48554033, 0.50076103, 0.5053272, 0.49467275, 0.5175038, 0.51141554, 0.4733638, 0.50380516, 0.4733638, 0.5327245, 0.4885845, 0.4824962, 0.4824962, 0.47792998, 0.47031963, 0.5022831, 0.51445967, 0.5281583, 0.48097414, 0.49619484, 0.49923897, 0.47792998, 0.49467275, 0.48097414, 0.4733638, 0.53120244, 0.48401827, 0.49467275, 0.5327245, 0.50076103, 0.49315068, 0.5251142, 0.5022831, 0.46270928, 0.5235921, 0.45966515, 0.48401827, 0.46270928, 0.5083714, 0.4611872, 0.53120244, 0.456621, 0.51141554, 0.4977169, 0.47945204, 0.49162862, 0.4824962, 0.5098935, 0.46423134, 0.4885845, 0.50380516, 0.5022831, 0.46423134, 0.46423134, 0.46879756, 0.45966515, 0.4870624, 0.51902586, 0.49010655, 0.49467275, 0.5372907, 0.49010655, 0.5296804, 0.48097414, 0.47792998, 0.53120244, 0.4870624, 0.5281583, 0.48097414, 0.51598173, 0.4885845, 0.50380516, 0.51445967, 0.4672755, 0.5053272, 0.48401827, 0.5235921, 0.54185694]
rnn = [0.24505328, 0.23439878, 0.2435312, 0.22678843, 0.29984778, 0.4155251, 0.47031963, 0.4733638, 0.46575344, 0.47792998, 0.4611872, 0.4870624, 0.50380516, 0.47031963, 0.4885845, 0.47488585, 0.48401827, 0.4611872, 0.47488585, 0.51141554, 0.4672755, 0.49619484, 0.47792998, 0.5068493, 0.5083714, 0.47640792, 0.44292238, 0.46879756, 0.48401827, 0.51141554, 0.47640792, 0.54185694, 0.4672755, 0.5251142, 0.45966515, 0.4885845, 0.49619484, 0.49162862, 0.50380516, 0.5053272, 0.4733638, 0.49315068, 0.47945204, 0.47488585, 0.49923897, 0.47488585, 0.46423134, 0.53120244, 0.4581431, 0.47031963, 0.48554033, 0.49467275, 0.51902586, 0.4718417, 0.5068493, 0.52207, 0.47488585, 0.47640792, 0.4977169, 0.45509893, 0.46575344, 0.47792998, 0.45966515, 0.48097414, 0.47945204, 0.4672755, 0.4885845, 0.47792998, 0.4977169, 0.5372907, 0.47945204, 0.4870624, 0.49923897, 0.5022831, 0.49467275, 0.4672755, 0.46423134, 0.50076103, 0.5205479, 0.47031963, 0.4581431, 0.50076103, 0.49467275, 0.5083714, 0.5175038, 0.456621, 0.47640792, 0.47640792, 0.47488585, 0.5205479, 0.49315068, 0.4672755, 0.4581431, 0.45053273, 0.49162862, 0.48554033, 0.49010655, 0.49619484, 0.4977169, 0.5235921]
multiple = [0.14003044, 0.14916286, 0.18417047, 0.16590562, 0.22678843, 0.14003044, 0.14916286, 0.11719939, 0.12328767, 0.152207, 0.15829529, 0.14459665, 0.19025876, 0.17199391, 0.18873668, 0.26331812, 0.23439878, 0.24657534, 0.21613394, 0.23592085, 0.2891933, 0.25114155, 0.25570777, 0.15981735, 0.17047185, 0.13394216, 0.14003044, 0.14916286, 0.152207, 0.15677321, 0.14003044, 0.13546424, 0.11872146, 0.12480974, 0.14003044, 0.10350076, 0.106544904, 0.15068494, 0.13850836, 0.14916286, 0.10502283, 0.11415525, 0.13546424, 0.10806697, 0.12937595, 0.13850836, 0.10350076, 0.15829529, 0.13394216, 0.13546424, 0.11263318, 0.09436834, 0.15068494, 0.106544904, 0.15068494, 0.10806697, 0.11872146, 0.12785389, 0.11415525, 0.120243534, 0.1263318, 0.09589041, 0.11415525, 0.14003044, 0.1369863, 0.17503805, 0.10502283, 0.12937595, 0.10958904, 0.12328767, 0.11263318, 0.12480974, 0.13546424, 0.11263318, 0.15829529, 0.11719939, 0.12480974, 0.15372907, 0.120243534, 0.1324201, 0.1369863, 0.12328767, 0.1324201, 0.120243534, 0.15525115, 0.12328767, 0.11872146, 0.1324201, 0.09741248, 0.12785389, 0.15372907, 0.1476408, 0.12480974, 0.12785389, 0.14611872, 0.10197869, 0.09893455, 0.11567732, 0.1628615, 0.12937595]
import matplotlib.pyplot as plt
plt.ylim(0,1)
plt.plot(single, "b-", label='single')
plt.plot(lstm, "g-", label='lstm')
plt.plot(rnn, "r-", label='rnn')
plt.plot(multiple, "y-", label='multiple')
plt.title("validation set accuracy")
plt.legend()
plt.savefig(DIR + "/out/" + EXPERIMENT_NAME + "_plus_multiple" + '.png')