snapshot...

modules/deeplearning/icing_cnn.py

@@ -1095,19 +1095,24 @@ def run_evaluate_static_new(data_dct, num_lines, num_elems, ckpt_dir_s_path, fli
     ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
     ckpt.restore(ckpt_manager.latest_checkpoint)
 
+    probs_2d_s = []
+    preds_2d_s = []
     for flvl in [0, 1, 2, 3, 4]:
         nn.flight_level = flvl
         nn.do_evaluate()
         probs = nn.test_probs
 
-    if NumClasses == 2:
-        preds = np.where(probs > prob_thresh, 1, 0)
-    else:
-        preds = np.argmax(probs, axis=1)
-    preds_2d = preds.reshape((num_lines, num_elems))
-    probs_2d = probs.reshape((num_lines, num_elems))
+        if NumClasses == 2:
+            preds = np.where(probs > prob_thresh, 1, 0)
+        else:
+            preds = np.argmax(probs, axis=1)
+        preds_2d = preds.reshape((num_lines, num_elems))
+        probs_2d = probs.reshape((num_lines, num_elems))
+
+        probs_2d_s.append(probs_2d)
+        preds_2d_s.append(preds_2d)
 
-    return preds_2d, probs_2d
+    return preds_2d_s, probs_2d_s
 
 
 if __name__ == "__main__":