results

scripts/training_reg.py

@@ -173,8 +173,8 @@ def fix_weight(dst_v):
     pos = np.argwhere(np.abs(b - dst_v) == np.abs((b - dst_v)).min())[0,0]
     if dst_v - b[pos] < 0:
         pos = pos-1
-    return np.sqrt(w[pos]/h.max())
-#    return w[pos]/h.max()
+#    return np.sqrt(w[pos]/h.max())
+    return w[pos]/h.max()
 
 fix_weight_v = np.vectorize(fix_weight)    
 weights = fix_weight_v(dst_min)
@@ -341,8 +341,8 @@ for epoch in range(num_epochs):
 #        dataset_tr = Dataset(data_in_scaled[ixs_tr], data_out_scaled[ixs_tr])
 #        data_loader_tr = utils_data.DataLoader(dataset_tr, batch_size=BATCH_SIZE, num_workers = 4, shuffle=False, sampler = sampler)        
 
-torch.save(dst_net.state_dict(), 'models/dst_reg_64_16_sqrt_all.pth')    
+torch.save(dst_net.state_dict(), '/home/mcristofo/DST/models/dst_reg_64_16_all.pth')    
 
-np.savetxt('hist/history_tr_rmse_mae_reg_64_16_sqrt_all.txt', history_tr)
-np.savetxt('hist/history_valid_rmse_mae_reg_64_16_sqrt_all.txt', history_valid)
-np.savetxt('hist/history_ts_rmse_mae_reg_64_16_sqrt_all.txt', history_ts)
+np.savetxt('/home/mcristofo/DST/hist/history_tr_rmse_mae_reg_64_16_all.txt', history_tr)
+np.savetxt('/home/mcristofo/DST/hist/history_valid_rmse_mae_reg_64_16_all.txt', history_valid)
+np.savetxt('/home/mcristofo/DST/hist/history_ts_rmse_mae_reg_64_16_all.txt', history_ts)