plot.py

import matplotlib.pyplot as plt

# fig, ax = plt.subplots(1, 1, figsize=(10, 3))
# ax = ax.ravel()


acc=[0.6023166179656982, 0.6138995885848999, 0.6332046389579773, 0.6332046389579773, 0.6640926599502563, 0.6872586607933044, 0.6640926599502563, 0.6679536700248718, 0.6409266591072083, 0.6679536700248718, 0.6988416910171509, 0.7104247212409973, 0.6795367002487183, 0.6602316498756409, 0.6872586607933044, 0.7335907220840454, 0.7258687019348145, 0.7027027010917664, 0.7335907220840454, 0.6988416910171509, 0.7490347623825073, 0.7490347623825073, 0.7644787430763245, 0.7528957724571228, 0.8108108043670654, 0.8185328245162964, 0.80694979429245, 0.837837815284729, 0.8610038757324219, 0.8571428656578064, 0.8880308866500854, 0.8996139168739319, 0.9189189076423645, 0.915057897567749, 0.9073359370231628, 0.9111968874931335, 0.9305019378662109, 0.915057897567749, 0.9305019378662109, 0.9343629479408264]
val=[0.3404255211353302, 0.43262410163879395, 0.6808510422706604, 0.652482271194458, 0.652482271194458, 0.5957446694374084, 0.6595744490623474, 0.673758864402771, 0.716312050819397, 0.6808510422706604, 0.6808510422706604, 0.6595744490623474, 0.6595744490623474, 0.6879432797431946, 0.7234042286872864, 0.631205677986145, 0.758865237236023, 0.8156028389930725, 0.8085106611251831, 0.7872340679168701, 0.6460993051528931, 0.8014184236526489, 0.61843971610069275, 0.8368794322013855, 0.8085106611251831, 0.8226950168609619, 0.64680851697921753, 0.8794326186180115, 0.9219858050346375, 0.8794326186180115, 0.8226950168609619, 0.9503546357154846, 0.9290780425071716, 0.9219858050346375, 0.8652482032775879, 0.8865247964859009, 0.9716312289237976, 0.978723406791687, 0.8879432797431946, 0.9143262457847595]
met = ['Accuracy', 'Loss']
# for i, metric in enumerate(["acc"]):
#     ax[i].plot(acc)
#     ax[i].plot(val)
#     ax[i].set_title("Model {}".format(met[i]))
#     ax[i].set_xlabel("epochs")
#     ax[i].set_ylabel(metric)
#     ax[i].legend(["Train", "Val"])
# fig.savefig('3dcnn_result_40.jpg')

fig = plt.figure(figsize=(9, 3))
plt.plot(acc)
plt.plot(val)
plt.title("Model Accuracy")
plt.xlabel("epochs")
plt.ylabel('ACC')
plt.legend(["Train", "Val"])
plt.savefig('3dcnn_result_40.jpg')