gyglim · August 23, 2023 21:29 · Jul 18, 2019 · Apr 13, 2018 · Oct 3, 2017 · Jan 4, 2017
diff --git a/tensorboard_logging.py b/tensorboard_logging.py
@@ -1,6 +1,6 @@
 """Simple example on how to log scalars and images to tensorboard without tensor ops.
 
-License: Copyleft
+License: BSD License 2.0
 """
 __author__ = "Michael Gygli"
 

diff --git a/tensorboard_logging.py b/tensorboard_logging.py
@@ -1,4 +1,7 @@
-"""Simple example on how to log scalars and images to tensorboard without tensor ops."""
+"""Simple example on how to log scalars and images to tensorboard without tensor ops.
+
+License: Copyleft
+"""
 __author__ = "Michael Gygli"
 
 import tensorflow as tf
@@ -52,8 +55,10 @@ def log_images(self, tag, images, step):
 
     def log_histogram(self, tag, values, step, bins=1000):
         """Logs the histogram of a list/vector of values."""
-
-        # Create histogram using numpy
+        # Convert to a numpy array
+        values = np.array(values)
+
+        # Create histogram using numpy        
         counts, bin_edges = np.histogram(values, bins=bins)
 
         # Fill fields of histogram proto

diff --git a/tensorboard_logging.py b/tensorboard_logging.py
@@ -53,30 +53,29 @@ def log_images(self, tag, images, step):
     def log_histogram(self, tag, values, step, bins=1000):
         """Logs the histogram of a list/vector of values."""
 
-        if step % self.frequency == 0:
-            # Create histogram using numpy
-            counts, bin_edges = np.histogram(values, bins=bins)
+        # Create histogram using numpy
+        counts, bin_edges = np.histogram(values, bins=bins)
 
-            # Fill fields of histogram proto
-            hist = tf.HistogramProto()
-            hist.min = float(np.min(values))
-            hist.max = float(np.max(values))
-            hist.num = int(np.prod(values.shape))
-            hist.sum = float(np.sum(values))
-            hist.sum_squares = float(np.sum(values**2))
+        # Fill fields of histogram proto
+        hist = tf.HistogramProto()
+        hist.min = float(np.min(values))
+        hist.max = float(np.max(values))
+        hist.num = int(np.prod(values.shape))
+        hist.sum = float(np.sum(values))
+        hist.sum_squares = float(np.sum(values**2))
 
-            # Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
-            # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
-            # Thus, we drop the start of the first bin
-            bin_edges = bin_edges[1:]
+        # Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
+        # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
+        # Thus, we drop the start of the first bin
+        bin_edges = bin_edges[1:]
 
-            # Add bin edges and counts
-            for edge in bin_edges:
-                hist.bucket_limit.append(edge)
-            for c in counts:
-                hist.bucket.append(c)
+        # Add bin edges and counts
+        for edge in bin_edges:
+            hist.bucket_limit.append(edge)
+        for c in counts:
+            hist.bucket.append(c)
 
-            # Create and write Summary
-            summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
-            self.writer.add_summary(summary, step)
-            self.writer.flush()
+        # Create and write Summary
+        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()
diff --git a/tensorboard_logging.py b/tensorboard_logging.py
@@ -49,30 +49,34 @@ def log_images(self, tag, images, step):
         summary = tf.Summary(value=im_summaries)
         self.writer.add_summary(summary, step)
 
+
     def log_histogram(self, tag, values, step, bins=1000):
         """Logs the histogram of a list/vector of values."""
 
-        # Create histogram using numpy
-        counts, bin_edges = np.histogram(values, bins=bins)
+        if step % self.frequency == 0:
+            # Create histogram using numpy
+            counts, bin_edges = np.histogram(values, bins=bins)
 
-        # Fill fields of histogram proto
-        hist = tf.HistogramProto()
-        hist.min = np.min(values)
-        hist.max = np.max(values)
-        hist.num = np.prod(values.shape)
-        hist.sum = np.sum(values)
-        hist.sum_squares = np.sum(values**2)
+            # Fill fields of histogram proto
+            hist = tf.HistogramProto()
+            hist.min = float(np.min(values))
+            hist.max = float(np.max(values))
+            hist.num = int(np.prod(values.shape))
+            hist.sum = float(np.sum(values))
+            hist.sum_squares = float(np.sum(values**2))
 
-        # Requires equal number of bins, where the first goes from -DBL_MAX to bin_edges[1]
-        # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
-        bin_edges = bin_edges[1:]
+            # Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
+            # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
+            # Thus, we drop the start of the first bin
+            bin_edges = bin_edges[1:]
 
-        for edge in bin_edges:
-            hist.bucket_limit.append(edge)
-        for c in counts:
-            hist.bucket.append(c)
-
-        # Create and write Summary
-        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
-        self.writer.add_summary(summary, step)
-        self.writer.flush()        
+            # Add bin edges and counts
+            for edge in bin_edges:
+                hist.bucket_limit.append(edge)
+            for c in counts:
+                hist.bucket.append(c)
+
+            # Create and write Summary
+            summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
+            self.writer.add_summary(summary, step)
+            self.writer.flush()
diff --git a/tensorboard_logging.py b/tensorboard_logging.py
@@ -4,7 +4,7 @@
 import tensorflow as tf
 from StringIO import StringIO
 import matplotlib.pyplot as plt
-
+import numpy as np
 
 class Logger(object):
     """Logging in tensorboard without tensorflow ops."""
@@ -48,3 +48,31 @@ def log_images(self, tag, images, step):
         # Create and write Summary
         summary = tf.Summary(value=im_summaries)
         self.writer.add_summary(summary, step)
+
+    def log_histogram(self, tag, values, step, bins=1000):
+        """Logs the histogram of a list/vector of values."""
+
+        # Create histogram using numpy
+        counts, bin_edges = np.histogram(values, bins=bins)
+
+        # Fill fields of histogram proto
+        hist = tf.HistogramProto()
+        hist.min = np.min(values)
+        hist.max = np.max(values)
+        hist.num = np.prod(values.shape)
+        hist.sum = np.sum(values)
+        hist.sum_squares = np.sum(values**2)
+
+        # Requires equal number of bins, where the first goes from -DBL_MAX to bin_edges[1]
+        # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
+        bin_edges = bin_edges[1:]
+
+        for edge in bin_edges:
+            hist.bucket_limit.append(edge)
+        for c in counts:
+            hist.bucket.append(c)
+
+        # Create and write Summary
+        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()        
diff --git a/tensorboard_logging.py b/tensorboard_logging.py
@@ -1,7 +1,7 @@
 """Simple example on how to log scalars and images to tensorboard without tensor ops."""
 __author__ = "Michael Gygli"
 
-import tensorboard as tf
+import tensorflow as tf
 from StringIO import StringIO
 import matplotlib.pyplot as plt
 

diff --git a/tensorboard_logging.py b/tensorboard_logging.py
@@ -0,0 +1,50 @@
+"""Simple example on how to log scalars and images to tensorboard without tensor ops."""
+__author__ = "Michael Gygli"
+
+import tensorboard as tf
+from StringIO import StringIO
+import matplotlib.pyplot as plt
+
+
+class Logger(object):
+    """Logging in tensorboard without tensorflow ops."""
+
+    def __init__(self, log_dir):
+        """Creates a summary writer logging to log_dir."""
+        self.writer = tf.summary.FileWriter(log_dir)
+
+    def log_scalar(self, tag, value, step):
+        """Log a scalar variable.
+
+        Parameter
+        ----------
+        tag : basestring
+            Name of the scalar
+        value
+        step : int
+            training iteration
+        """
+        summary = tf.Summary(value=[tf.Summary.Value(tag=tag,
+                                                     simple_value=value)])
+        self.writer.add_summary(summary, step)
+
+    def log_images(self, tag, images, step):
+        """Logs a list of images."""
+
+        im_summaries = []
+        for nr, img in enumerate(images):
+            # Write the image to a string
+            s = StringIO()
+            plt.imsave(s, img, format='png')
+
+            # Create an Image object
+            img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
+                                       height=img.shape[0],
+                                       width=img.shape[1])
+            # Create a Summary value
+            im_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, nr),
+                                                 image=img_sum))
+
+        # Create and write Summary
+        summary = tf.Summary(value=im_summaries)
+        self.writer.add_summary(summary, step)