From 2272f67d9bb46e7b53fc2bf7a15cdcd961f67f07 Mon Sep 17 00:00:00 2001
From: detlef <ds2@physik.de>
Date: Wed, 21 Aug 2019 20:50:45 +0200
Subject: [PATCH] - nicer printing

---
 mini_imagenet_dataloader.py | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/mini_imagenet_dataloader.py b/mini_imagenet_dataloader.py
index fbcc4cf..f3160e3 100644
--- a/mini_imagenet_dataloader.py
+++ b/mini_imagenet_dataloader.py
@@ -471,12 +471,6 @@ def all_layers(model):
             layers.append(l)
     return layers
        
-for l in all_layers(siamese_net):
-    l2=l
-    if isinstance(l,TimeDistributed):
-        l2=l.layer
-    print(l2.name,l2.trainable, len(l2.get_weights()))
-
 lambda_model_layers = all_layers(lambda_model)
 for l in range(len(lambda_model_layers)):
     l2=lambda_model_layers[l]
@@ -486,7 +480,7 @@ def all_layers(model):
         p='timedi'
     if args.enable_only_layers_of_list is not None:
         l2.trainable = False
-    print(l, p,l2.name,l2.trainable, len(l2.get_weights()))
+    print('{:10} {:10} {:20} {:10}  {:10}'.format(l, p,l2.name, ("fixed", "trainable")[l2.trainable], l2.count_params()))
 
 if args.enable_only_layers_of_list is not None:
     print('\nenable some layers for training')
@@ -500,7 +494,7 @@ def all_layers(model):
         if isinstance(l2,TimeDistributed):
             l2=l2.layer
             p='timedi'
-        print(l, p,l2.name,l2.trainable, len(l2.get_weights()))
+        print('{:10} {:10} {:20} {:10}  {:10}'.format(l, p,l2.name, ("fixed", "trainable")[l2.trainable], l2.count_params()))
 
 #after loading to set learning rate
 lambda_model.compile(loss='categorical_crossentropy', optimizer=op.SGD(args.lr), metrics=['categorical_accuracy'])