--- a
+++ b/common/logger.py
@@ -0,0 +1,57 @@
+
+from io import BytesIO
+
+from PIL import Image
+import tensorflow as tf
+
+
+class Logger(object):
+
+    def __init__(self, log_dir):
+        self.writer = tf.compat.v1.summary.FileWriter(log_dir)
+
+    def scalar_summary(self, tag, value, step):
+        summary = tf.compat.v1.Summary(value=[tf.compat.v1.Summary.Value(tag=tag, simple_value=value)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()
+
+    def image_summary(self, tag, image, step):
+        s = BytesIO()
+        Image.fromarray(image).save(s, format="png")
+
+        # Create an Image object
+        img_sum = tf.compat.v1.Summary.Image(
+            encoded_image_string=s.getvalue(),
+            height=image.shape[0],
+            width=image.shape[1],
+        )
+
+        # Create and write Summary
+        summary = tf.compat.v1.Summary(value=[tf.compat.v1.Summary.Value(tag=tag, image=img_sum)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()
+
+    def image_list_summary(self, tag, images, step):
+        if len(images) == 0:
+            return
+        img_summaries = []
+        for i, img in enumerate(images):
+            s = BytesIO()
+            Image.fromarray(img).save(s, format="png")
+
+            # Create an Image object
+            img_sum = tf.compat.v1.Summary.Image(
+                encoded_image_string=s.getvalue(),
+                height=img.shape[0],
+                width=img.shape[1],
+            )
+
+            # Create a Summary value
+            img_summaries.append(
+                tf.compat.v1.Summary.Value(tag="{}/{}".format(tag, i), image=img_sum)
+            )
+
+        # Create and write Summary
+        summary = tf.compat.v1.Summary(value=img_summaries)
+        self.writer.add_summary(summary, step)
+        self.writer.flush()