plot_error_loss.py

#Plots results from csv files

import os
import scipy.io as io
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

plt.style.use('seaborn')

#Select which key to plot results for
BATCH_SIZE = 15     # mini_batch size
# MAX_EPOCH = 50      # maximum epoch to train
hidden_size = 256    # size of hidden layer
n_layers = 2        # number of lstm layers
box_size = 'S' #'M' 'L'
ts_delete_step_size = 10

# key = 'LSTM_B{}_h{}_l{}_bb{}' .format(BATCH_SIZE, hidden_size, n_layers, box_size)
# key = 'LSTM_LL1_B{}_h{}_l{}_bb{}' .format(BATCH_SIZE, hidden_size, n_layers, box_size)
# key = 'LSTM_LL2_B{}_h{}_l{}_bb{}' .format(BATCH_SIZE, hidden_size, n_layers, box_size)
# key = 'LSTM_B{}_h{}_l{}_bb{}_ss{}' .format(BATCH_SIZE, hidden_size, n_layers, box_size,ts_delete_step_size)
# key = 'LSTM_LL1_DO_B{}_h{}_l{}_bb{}_ss{}' .format(BATCH_SIZE, hidden_size, n_layers, box_size,ts_delete_step_size)
key = 'LSTM_LL2_DO_B{}_h{}_l{}_bb{}_ss{}' .format(BATCH_SIZE, hidden_size, n_layers, box_size,ts_delete_step_size)


path_to_results = os.path.join(os.getcwd(), 'LSTM_training_results', '{}_results.csv'.format(key))
results = pd.read_csv(path_to_results)
# os.path.join(path_to_results, os.listdir(path_to_data)[storm_idx-1])

#plot acc
plt.figure(1)
plt.plot(results['train_acc'])
plt.plot(results['test_acc'])
plt.legend(['train_acc','test_acc'])
plt.title('Average Error')
plt.xlabel('Epoch #')
plt.ylabel('Error (m)')
#save error plot
plt.savefig('LSTM_training_results/result_plots/{}_Error_plot.png'.format(key), transparent=False)

#plot loss
plt.figure(2)
plt.plot(results['train_loss'])
plt.plot(results['test_loss'])
plt.legend(['train_loss','test_loss'])
plt.title('Loss')
plt.xlabel('Epoch #')
plt.ylabel('Loss')
#save loss plot
plt.savefig('LSTM_training_results/result_plots/{}_Loss_plot.png'.format(key), transparent=False)


plt.show()