"""
Generate Plot for model loss while training
tensorbaord is added as call back to model training
start tensorboard from terminal/cmd window using tensorboard --logdir ./logs
"""
[docs]class TrainViz:
"""contains method to generate loss convergence plot
"""
[docs] def training_plot(self, history,plots_path,run_id=0):
"""plot and save the training loss
:param history:keras model training output dictionary
:type history: dict (required)
:param plots_path:plot path to save plot file
:type plots_path: dict (required)
:param run_id:Run identifier used in data study to id the training
:type file_name: int
"""
import matplotlib.pyplot as plt
#summarize history for Mean Absolute Error
# plt.plot(history.history['mae'])
# plt.plot(history.history['val_mae'])
# plt.title('model MAE`')
# plt.ylabel('MAE')
# plt.xlabel('epoch')
# plt.legend(['train', 'test'], loc='upper left')
# plt.savefig(plots_path+'/'+'accuracy_'+str(run_id)+'.png')
# plt.clf()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig(plots_path+'/'+'loss_'+str(run_id)+'.png')
plt.clf()