Upload New File

BHPD_PyTorch/fidle_pwk_additional.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+import numpy as np
+
+
+class convergence_history_CrossEntropyLoss:
+  def __init__(self):
+    """
+    Class to save the training converge properties
+    """
+    self.loss=nn.CrossEntropyLoss()
+    self.history={}                #Save convergence measures in the end of each epoch
+    self.history['loss']=[]        #value of the cost function on training data
+    self.history['accuracy']=[]         #percentage of  correctly classified instances on training data (if classification)
+    self.history['val_loss']=[]    #value of the cost function on validation data
+    self.history['val_accuracy']=[]     #percentage of  correctly classified instances on validation data (if classification)
+  
+  def update(self,current_model,xtrain,ytrain,xtest,ytest):
+    
+    #convergence information on the training data 
+    nb_training_obs=xtrain.shape[0]
+    if nb_training_obs>xtest.shape[0]:
+        nb_training_obs=xtest.shape[0]
+        
+    epoch_shuffler=np.arange(xtrain.shape[0]) 
+    np.random.shuffle(epoch_shuffler)
+    mini_batch_observations = epoch_shuffler[:nb_training_obs]
+    var_X_batch = Variable(xtrain[mini_batch_observations,:]).float()
+    var_y_batch = Variable(ytrain[mini_batch_observations])
+    y_pred_batch = current_model(var_X_batch)
+    curr_loss = self.loss(y_pred_batch, var_y_batch)
+
+    self.history['loss'].append(curr_loss.item())
+    self.history['accuracy'].append( float( (torch.argmax(y_pred_batch, dim= 1) == var_y_batch).float().mean()) )
+    
+    #convergence information on the test data 
+    var_X_batch = Variable(xtest[:,:]).float()
+    var_y_batch = Variable(ytest[:])
+    y_pred_batch = current_model(var_X_batch)
+    curr_loss = self.loss(y_pred_batch, var_y_batch)
+
+    self.history['val_loss'].append(curr_loss.item())
+    self.history['val_accuracy'].append( float( (torch.argmax(y_pred_batch, dim= 1) == var_y_batch).float().mean()) )
+
+
+
+class convergence_history_MSELoss:
+  def __init__(self):
+    """
+    Class to save the training converge properties
+    """
+    self.loss = nn.MSELoss()
+    self.MAE_loss = nn.L1Loss()
+    self.history={}                #Save convergence measures in the end of each epoch
+    self.history['loss']=[]        #value of the cost function on training data
+    self.history['mae']=[]         #mean absolute error on training data
+    self.history['val_loss']=[]    #value of the cost function on validation data
+    self.history['val_mae']=[]     #mean absolute error on validation data
+  
+  def update(self,current_model,xtrain,ytrain,xtest,ytest):
+    
+    #convergence information on the training data 
+    nb_training_obs=xtrain.shape[0]
+    if nb_training_obs>xtest.shape[0]:
+        nb_training_obs=xtest.shape[0]
+        
+    epoch_shuffler=np.arange(xtrain.shape[0]) 
+    np.random.shuffle(epoch_shuffler)
+    mini_batch_observations = epoch_shuffler[:nb_training_obs]
+    var_X_batch = Variable(xtrain[mini_batch_observations,:]).float()
+    var_y_batch = Variable(ytrain[mini_batch_observations]).float()
+    y_pred_batch = current_model(var_X_batch)
+    curr_loss = self.loss(y_pred_batch.view(-1), var_y_batch.view(-1))
+
+    self.history['loss'].append(curr_loss.item())
+    self.history['mae'].append(self.MAE_loss(y_pred_batch.view(-1), var_y_batch.view(-1)).item())
+    
+    #convergence information on the test data 
+    var_X_batch = Variable(xtest[:,:]).float()
+    var_y_batch = Variable(ytest[:]).float()
+    y_pred_batch = current_model(var_X_batch)
+    curr_loss = self.loss(y_pred_batch.view(-1), var_y_batch.view(-1))
+
+    self.history['val_loss'].append(curr_loss.item())
+    self.history['val_mae'].append(self.MAE_loss(y_pred_batch.view(-1), var_y_batch.view(-1)).item())