update custom loss with normFac

loss.py

@@ -1,58 +1,63 @@
-def custom_loss(y_true, y_pred):
+def custom_loss_wrapper(normFac=1):
     '''
-    cutmoized loss function to improve the recoil response,
+    customized loss function to improve the recoil response,
     by balancing the response above one and below one
     '''
-    import tensorflow.keras.backend as K
-    import tensorflow as tf
-
-    px_truth = K.flatten(y_true[:, 0])
-    py_truth = K.flatten(y_true[:, 1])
-    px_pred = K.flatten(y_pred[:, 0])
-    py_pred = K.flatten(y_pred[:, 1])
-
-    pt_truth = K.sqrt(px_truth*px_truth + py_truth*py_truth)
-
-    #px_truth1 = px_truth / pt_truth
-    #py_truth1 = py_truth / pt_truth
-
-    # using absolute response
-    # upar_pred = (px_truth1 * px_pred + py_truth1 * py_pred)/pt_truth
-    upar_pred = K.sqrt(px_pred * px_pred + py_pred * py_pred) - pt_truth
-    pt_cut = pt_truth > 0.
-    upar_pred = tf.boolean_mask(upar_pred, pt_cut)
-    pt_truth_filtered = tf.boolean_mask(pt_truth, pt_cut)
-
-    #filter_bin0 = pt_truth_filtered < 50.
-    filter_bin0 = tf.logical_and(pt_truth_filtered > 50.,  pt_truth_filtered < 100.)
-    filter_bin1 = tf.logical_and(pt_truth_filtered > 100., pt_truth_filtered < 200.)
-    filter_bin2 = tf.logical_and(pt_truth_filtered > 200., pt_truth_filtered < 300.)
-    filter_bin3 = tf.logical_and(pt_truth_filtered > 300., pt_truth_filtered < 400.)
-    filter_bin4 = pt_truth_filtered > 400.
-
-    upar_pred_pos_bin0 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin0, upar_pred > 0.))
-    upar_pred_neg_bin0 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin0, upar_pred < 0.))
-    upar_pred_pos_bin1 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin1, upar_pred > 0.))
-    upar_pred_neg_bin1 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin1, upar_pred < 0.))
-    upar_pred_pos_bin2 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin2, upar_pred > 0.))
-    upar_pred_neg_bin2 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin2, upar_pred < 0.))
-    upar_pred_pos_bin3 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin3, upar_pred > 0.))
-    upar_pred_neg_bin3 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin3, upar_pred < 0.))
-    upar_pred_pos_bin4 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin4, upar_pred > 0.))
-    upar_pred_neg_bin4 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin4, upar_pred < 0.))
-    #upar_pred_pos_bin5 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin5, upar_pred > 0.))
-    #upar_pred_neg_bin5 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin5, upar_pred < 0.))
-    norm = tf.reduce_sum(pt_truth_filtered)
-    dev = tf.abs(tf.reduce_sum(upar_pred_pos_bin0) + tf.reduce_sum(upar_pred_neg_bin0))
-    dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin1) + tf.reduce_sum(upar_pred_neg_bin1))
-    dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin2) + tf.reduce_sum(upar_pred_neg_bin2))
-    dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin3) + tf.reduce_sum(upar_pred_neg_bin3))
-    dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin4) + tf.reduce_sum(upar_pred_neg_bin4))
-    #dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin5) + tf.reduce_sum(upar_pred_neg_bin5))
-    dev /= norm
-
-    loss = 0.5*K.mean((px_pred - px_truth)**2 + (py_pred - py_truth)**2)
-
-    #loss += 200.*dev
-    loss += 5000.*dev
-    return loss
+
+
+    def custom_loss(y_true, y_pred):
+        import tensorflow.keras.backend as K
+        import tensorflow as tf
+
+        px_truth = K.flatten(y_true[:, 0])
+        py_truth = K.flatten(y_true[:, 1])
+        px_pred = K.flatten(y_pred[:, 0])
+        py_pred = K.flatten(y_pred[:, 1])
+
+        pt_truth = K.sqrt(px_truth*px_truth + py_truth*py_truth)
+
+        #px_truth1 = px_truth / pt_truth
+        #py_truth1 = py_truth / pt_truth
+
+        # using absolute response
+        # upar_pred = (px_truth1 * px_pred + py_truth1 * py_pred)/pt_truth
+        upar_pred = K.sqrt(px_pred * px_pred + py_pred * py_pred) - pt_truth
+        pt_cut = pt_truth > 0.
+        upar_pred = tf.boolean_mask(upar_pred, pt_cut)
+        pt_truth_filtered = tf.boolean_mask(pt_truth, pt_cut)
+
+        #filter_bin0 = pt_truth_filtered < 50./normFac
+        filter_bin0 = tf.logical_and(pt_truth_filtered > 50./normFac,  pt_truth_filtered < 100./normFac)
+        filter_bin1 = tf.logical_and(pt_truth_filtered > 100./normFac, pt_truth_filtered < 200./normFac)
+        filter_bin2 = tf.logical_and(pt_truth_filtered > 200./normFac, pt_truth_filtered < 300./normFac)
+        filter_bin3 = tf.logical_and(pt_truth_filtered > 300./normFac, pt_truth_filtered < 400./normFac)
+        filter_bin4 = pt_truth_filtered > 400./normFac
+
+        upar_pred_pos_bin0 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin0, upar_pred > 0.))
+        upar_pred_neg_bin0 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin0, upar_pred < 0.))
+        upar_pred_pos_bin1 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin1, upar_pred > 0.))
+        upar_pred_neg_bin1 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin1, upar_pred < 0.))
+        upar_pred_pos_bin2 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin2, upar_pred > 0.))
+        upar_pred_neg_bin2 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin2, upar_pred < 0.))
+        upar_pred_pos_bin3 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin3, upar_pred > 0.))
+        upar_pred_neg_bin3 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin3, upar_pred < 0.))
+        upar_pred_pos_bin4 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin4, upar_pred > 0.))
+        upar_pred_neg_bin4 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin4, upar_pred < 0.))
+        #upar_pred_pos_bin5 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin5, upar_pred > 0.))
+        #upar_pred_neg_bin5 = tf.boolean_mask(upar_pred, tf.logical_and(filter_bin5, upar_pred < 0.))
+        norm = tf.reduce_sum(pt_truth_filtered)
+        dev = tf.abs(tf.reduce_sum(upar_pred_pos_bin0) + tf.reduce_sum(upar_pred_neg_bin0))
+        dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin1) + tf.reduce_sum(upar_pred_neg_bin1))
+        dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin2) + tf.reduce_sum(upar_pred_neg_bin2))
+        dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin3) + tf.reduce_sum(upar_pred_neg_bin3))
+        dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin4) + tf.reduce_sum(upar_pred_neg_bin4))
+        #dev += tf.abs(tf.reduce_sum(upar_pred_pos_bin5) + tf.reduce_sum(upar_pred_neg_bin5))
+        dev /= norm
+
+        loss = 0.5*normFac**2*K.mean((px_pred - px_truth)**2 + (py_pred - py_truth)**2)
+
+        #loss += 200.*dev
+        loss += 5000.*dev
+        return loss
+
+    return custom_loss