Merge pull request mmistakes#71 from pesser/misc

Misc
theRealSuperMario · Jun 24, 2019 · fec4e39 · fec4e39
2 parents d7e1e63 + f0ea94c
commit fec4e39
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Showing 2 changed files with 30 additions and 3 deletions.
diff --git a/edflow/iterators/tf_evaluator.py b/edflow/iterators/tf_evaluator.py
@@ -77,6 +77,7 @@ def __init__(self, *args, desc="Eval", hook_freq=1, num_epochs=1, **kwargs):
         """
         kwargs.update({"desc": desc, "hook_freq": hook_freq, "num_epochs": num_epochs})
         super().__init__(*args, **kwargs)
+        self.define_graph()
 
     def initialize(self, checkpoint_path=None):
         self.restore_variables = self.model.variables
@@ -101,5 +102,8 @@ def initialize(self, checkpoint_path=None):
             )
             self.hooks += [waiter]
 
+    def define_graph(self):
+        pass
+
     def step_ops(self):
         return self.model.outputs
diff --git a/edflow/iterators/tf_trainer.py b/edflow/iterators/tf_trainer.py
@@ -1,4 +1,5 @@
 import tensorflow as tf
+import numpy as np
 
 from edflow.iterators.tf_iterator import HookedModelIterator, TFHookedModelIterator
 
@@ -233,7 +234,7 @@ def setup(self):
         )
         ihook = IntervalHook(
             [loghook],
-            interval=1,
+            interval=self.config.get("start_log_freq", 1),
             modify_each=1,
             max_interval=self.config.get("log_freq", 1000),
             get_step=self.get_global_step,
@@ -293,7 +294,18 @@ def create_train_op(self):
             opt_ops[k] = self.optimizers[k].minimize(losses[k], var_list=variables)
             print(k)
             print("============================")
-            print("\n".join([v.name for v in variables]))
+            print(
+                "\n".join(
+                    [
+                        "{:>22} {:>22} {}".format(
+                            str(v.shape.as_list()),
+                            str(np.prod(v.shape.as_list())),
+                            v.name,
+                        )
+                        for v in variables
+                    ]
+                )
+            )
             print(len(variables))
             print("============================")
         self.opt_ops = opt_ops
@@ -418,7 +430,18 @@ def create_train_op(self):
                 opt_ops[k] = optimizers[k].minimize(losses[k], var_list=variables)
                 print(i, k, self.get_learning_rate_multiplier(i))
                 print("============================")
-                print("\n".join([v.name for v in variables]))
+                print(
+                    "\n".join(
+                        [
+                            "{:>22} {:>22} {}".format(
+                                str(v.shape.as_list()),
+                                str(np.prod(v.shape.as_list())),
+                                v.name,
+                            )
+                            for v in variables
+                        ]
+                    )
+                )
                 print(len(variables))
                 print("============================")