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seq2seq_plot_curves.py
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seq2seq_plot_curves.py
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# Created by Albert Aparicio on 6/12/16
# coding: utf-8
# This script takes the results of a training and plots its loss curves
import h5py
import matplotlib.pyplot as plt
import numpy as np
model_description = 'seq2seq_pretrain'
with h5py.File('training_results/' + model_description + '_training_params.h5',
'r') as f:
params_loss = f.attrs.get('params_loss').decode('utf-8')
flags_loss = f.attrs.get('flags_loss').decode('utf-8')
optimizer_name = f.attrs.get('optimizer').decode('utf-8')
nb_epochs = f.attrs.get('epochs')
learning_rate = f.attrs.get('learning_rate')
metrics_names = [name.decode('utf-8') for name in
f.attrs.get('metrics_names')]
f.close()
epoch = np.loadtxt('training_results/' + model_description + '_' + params_loss
+ '_' + flags_loss + '_' + optimizer_name + '_epochs_' +
str(nb_epochs) + '_lr_' + str(learning_rate) + '_epochs.csv',
delimiter=',')
losses = np.loadtxt('training_results/' + model_description + '_' + params_loss
+ '_' + flags_loss + '_' + optimizer_name + '_epochs_' +
str(nb_epochs) + '_lr_' + str(learning_rate) +
'_loss.csv', delimiter=',')
val_losses = np.loadtxt(
'training_results/' + model_description + '_' + params_loss + '_' +
flags_loss + '_' + optimizer_name + '_epochs_' + str(nb_epochs) + '_lr_' +
str(learning_rate) + '_val_loss.csv', delimiter=',')
mcd = np.loadtxt(
'training_results/' + model_description + '_' + params_loss + '_' +
flags_loss + '_' + optimizer_name + '_epochs_' + str(nb_epochs) + '_lr_' +
str(learning_rate) + '_mcd.csv', delimiter=',')
rmse = np.loadtxt(
'training_results/' + model_description + '_' + params_loss + '_' +
flags_loss + '_' + optimizer_name + '_epochs_' + str(nb_epochs) + '_lr_' +
str(learning_rate) + '_rmse.csv', delimiter=',')
acc = np.loadtxt(
'training_results/' + model_description + '_' + params_loss + '_' +
flags_loss + '_' + optimizer_name + '_epochs_' + str(nb_epochs) + '_lr_' +
str(learning_rate) + '_acc.csv', delimiter=',')
assert (val_losses.size == losses.size)
# ##############################################
# # TODO Comment after dev
# metrics_names = ['loss', 'params_output_loss', 'flags_output_loss']
#
# ##############################################
# Losses plot
h1 = plt.figure(figsize=(14, 8))
ax1 = h1.add_subplot(111)
plt.plot(epoch, losses, epoch, val_losses, '--', linewidth=2)
# Prepare legend
legend_list = list(metrics_names) # We use list() to make a copy
for name in metrics_names:
legend_list.append('val_' + name)
plt.legend(legend_list, loc='best')
plt.suptitle('Parameters loss: ' + params_loss + ', Flags loss: ' + flags_loss +
', Optimizer: ' + optimizer_name + ', Epochs: ' + str(nb_epochs) +
', Learning rate: ' + str(learning_rate))
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Loss values')
ax1.set_xlim(0, 19)
major_xticks = np.arange(0, 20, 1)
ax1.set_xticks(major_xticks)
ax1.tick_params(which='both', direction ='out')
ax1.grid(which='both', ls='-')
plt.savefig('training_results/' + model_description + '_' + params_loss + '_' +
flags_loss + '_' + optimizer_name + '_epochs_' +
str(nb_epochs) + '_lr_' + str(learning_rate) + '_graph.eps',
bbox_inches='tight')
# plt.show()
plt.close(h1)
# Metrics plot
h2 = plt.figure(figsize=(10, 5))
ax2 = h2.add_subplot(111)
plt.plot(epoch, mcd) # , epoch, rmse, epoch, acc)
plt.legend(['MCD (dB)'], loc='best')
# , 'RMSE', 'Accuracy'
plt.suptitle("Cepstral features' MCD", fontsize = 12)
# , RMSE and ACC
ax2.set_xlabel('Epochs')
ax2.set_ylabel('MCD (dB)')
ax2.set_xlim(0, 19)
major_xticks = np.arange(0, 20, 1)
major_yticks = np.arange(np.floor(np.min(mcd)), np.ceil(np.max(mcd)), 0.2)
ax2.set_xticks(major_xticks)
ax2.set_yticks(major_yticks)
ax2.tick_params(which='both', direction ='out')
ax2.grid(which='both', ls='-')
plt.savefig('training_results/' + model_description + '_' + params_loss + '_' +
flags_loss + '_' + optimizer_name + '_epochs_' +
str(nb_epochs) + '_lr_' + str(learning_rate) + '_mcd.eps',
bbox_inches='tight')
plt.close(h2)
h2 = plt.figure(figsize=(10, 5))
ax2 = h2.add_subplot(111)
plt.plot(epoch, rmse)
plt.legend(['RMSE'], loc='best')
# , 'RMSE', 'Accuracy'
plt.suptitle("Pitch Root Mean Square Error (RMSE)", fontsize=12)
# , RMSE and ACC
ax2.set_xlabel('Epochs')
ax2.set_ylabel('Root Mean Square Error (RMSE)')
ax2.set_xlim(0, 19)
major_xticks = np.arange(0, 20, 1)
major_yticks = np.arange(0, np.ceil(np.max(rmse*100))/100, 0.01)
ax2.set_xticks(major_xticks)
ax2.set_yticks(major_yticks)
ax2.tick_params(which='both', direction ='out')
ax2.grid(which='both', ls='-')
plt.savefig('training_results/' + model_description + '_' + params_loss + '_' +
flags_loss + '_' + optimizer_name + '_epochs_' +
str(nb_epochs) + '_lr_' + str(learning_rate) + '_rmse.eps',
bbox_inches='tight')
plt.close(h2)
h2 = plt.figure(figsize=(10, 5))
ax2 = h2.add_subplot(111)
plt.plot(epoch, acc)
plt.legend(['Accuracy'], loc='best')
plt.suptitle("U/V Flag Accuracy", fontsize=12)
# , RMSE and ACC
ax2.set_xlabel('Epochs')
ax2.set_ylabel('Accuracy')
ax2.set_xlim(0, 19)
major_xticks = np.arange(0, 20, 1)
major_yticks = np.arange(
np.floor(np.min(acc*100))/100,
1.005,
0.005
)
ax2.set_xticks(major_xticks)
ax2.set_yticks(major_yticks)
ax2.tick_params(which='both', direction ='out')
ax2.grid(which='both', ls='-')
plt.savefig('training_results/' + model_description + '_' + params_loss + '_' +
flags_loss + '_' + optimizer_name + '_epochs_' +
str(nb_epochs) + '_lr_' + str(learning_rate) + '_acc.eps',
bbox_inches='tight')
plt.close(h2)
exit()