Python tensorflow.HistogramProto() Examples

The following are 30 code examples of tensorflow.HistogramProto(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow , or try the search function .
Example #1
Source File: tb.py    From Jacinle with MIT License 6 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
Example #2
Source File: utilities.py    From Transferable-Adversarial-Training with MIT License 6 votes vote down vote up 
def log_histogram(self, tag, values, step = None, bins = 1000):
        if not step:
            step = self.step
        values = np.array(values)
        counts, bin_edges = np.histogram(values, bins=bins)
        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))
        for edge in bin_edges:
            hist.bucket_limit.append(edge)
        for c in counts:
            hist.bucket.append(c)
            
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #3
Source File: logger.py    From rl with MIT License 6 votes vote down vote up 
def log_histogram(self, tag, values, bins=1000):
    """ https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514 """
    values = np.array(values)

    counts, bin_edges = np.histogram(values, bins=bins)

    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))

    bin_edges = bin_edges[1:]

    for edge in bin_edges:
      hist.bucket_limit.append(edge)
    for c in counts:
      hist.bucket.append(c)

    summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
    self.writer.add_summary(summary, self._hparams.global_step)
    self.writer.flush()
Example #4
Source File: generate_testdata.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def _MakeHistogram(values):
  """Convert values into a histogram proto using logic from histogram.cc."""
  limits = _MakeHistogramBuckets()
  counts = [0] * len(limits)
  for v in values:
    idx = bisect.bisect_left(limits, v)
    counts[idx] += 1

  limit_counts = [(limits[i], counts[i]) for i in xrange(len(limits))
                  if counts[i]]
  bucket_limit = [lc[0] for lc in limit_counts]
  bucket = [lc[1] for lc in limit_counts]
  sum_sq = sum(v * v for v in values)
  return tf.HistogramProto(min=min(values),
                           max=max(values),
                           num=len(values),
                           sum=sum(values),
                           sum_squares=sum_sq,
                           bucket_limit=bucket_limit,
                           bucket=bucket)
Example #5
Source File: event_accumulator_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def AddHistogram(self,
                   tag,
                   wall_time=0,
                   step=0,
                   hmin=1,
                   hmax=2,
                   hnum=3,
                   hsum=4,
                   hsum_squares=5,
                   hbucket_limit=None,
                   hbucket=None):
    histo = tf.HistogramProto(min=hmin,
                              max=hmax,
                              num=hnum,
                              sum=hsum,
                              sum_squares=hsum_squares,
                              bucket_limit=hbucket_limit,
                              bucket=hbucket)
    event = tf.summary.Event(
        wall_time=wall_time,
        step=step,
        summary=tf.Summary(value=[tf.Summary.Value(
            tag=tag, histo=histo)]))
    self.AddEvent(event)
Example #6
Source File: utilities.py    From Separate_to_Adapt with MIT License 6 votes vote down vote up 
def log_histogram(self, tag, values, step = None, bins = 1000):
        if not step:
            step = self.step
        values = np.array(values)
        counts, bin_edges = np.histogram(values, bins=bins)
        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))
        for edge in bin_edges:
            hist.bucket_limit.append(edge)
        for c in counts:
            hist.bucket.append(c)
            
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #7
Source File: logger.py    From DivMatch with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #8
Source File: logger.py    From SalsaNext with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #9
Source File: utilities.py    From Transferable-Adversarial-Training with MIT License 5 votes vote down vote up 
def log_bar(self, tag, values, xs = None, step = None):
        if not step:
            step = self.step

        values = np.asarray(values).flatten()
        if not xs:
            axises = list(range(len(values)))
        else:
            axises = xs
        hist = tf.HistogramProto()
        hist.min = float(min(axises))
        hist.max = float(max(axises))
        hist.num = sum(values)
        hist.sum = sum([y * x for (x, y) in zip(axises, values)])
        hist.sum_squares = sum([y * (x ** 2) for (x, y) in zip(axises, values)])

        for edge in axises:
            hist.bucket_limit.append(edge - 1e-10)
            hist.bucket_limit.append(edge + 1e-10)
        for c in values:
            hist.bucket.append(0)
            hist.bucket.append(c)

        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, self.step)
        self.writer.flush()
Example #10
Source File: logger.py    From EXTD_Pytorch with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #11
Source File: logger.py    From AUNets with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #12
Source File: logging.py    From aster.pytorch with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
    """Log a histogram of the tensor of values."""

    # Create a histogram using numpy
    counts, bin_edges = np.histogram(values, bins=bins)

    # Fill the fields of the 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))

    # 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)

    # Create and write Summary
    summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
    self.writer.add_summary(summary, step)
    self.writer.flush()
Example #13
Source File: logger.py    From pytorch-tutorial with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #14
Source File: logger.py    From CMCS-Temporal-Action-Localization with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #15
Source File: logger.py    From causal-infogan with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #16
Source File: utilities.py    From Separate_to_Adapt with MIT License 5 votes vote down vote up 
def log_bar(self, tag, values, xs = None, step = None):
        if not step:
            step = self.step

        values = np.asarray(values).flatten()
        if not xs:
            axises = list(range(len(values)))
        else:
            axises = xs
        hist = tf.HistogramProto()
        hist.min = float(min(axises))
        hist.max = float(max(axises))
        hist.num = sum(values)
        hist.sum = sum([y * x for (x, y) in zip(axises, values)])
        hist.sum_squares = sum([y * (x ** 2) for (x, y) in zip(axises, values)])

        for edge in axises:
            hist.bucket_limit.append(edge - 1e-10)
            hist.bucket_limit.append(edge + 1e-10)
        for c in values:
            hist.bucket.append(0)
            hist.bucket.append(c)

        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, self.step)
        self.writer.flush()
Example #17
Source File: logger.py    From road_building_extraction with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #18
Source File: logger.py    From One-Shot-Object-Detection with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #19
Source File: logger.py    From bonnetal with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
    """Log a histogram of the tensor of values."""

    # Create a histogram using numpy
    counts, bin_edges = np.histogram(values, bins=bins)

    # Fill the fields of the 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))

    # 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)

    # Create and write Summary
    summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
    self.writer.add_summary(summary, step)
    self.writer.flush()
Example #20
Source File: tensorboardlogger.py    From PAST-ReID with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #21
Source File: tensorboard_logging.py    From TensorSwarm with MIT License 5 votes vote down vote up 
def log_histogram(self, tag, values, step, bins=1000):
        """Logs the histogram of a list/vector of values."""
        # 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
        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:]

        # 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()
Example #22
Source File: logger.py    From MVCNN-PyTorch with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #23
Source File: logger.py    From bottom-up-features with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #24
Source File: logger.py    From CIOD with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #25
Source File: logger.py    From RL-Surgical-Gesture-Segmentation with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #26
Source File: logger.py    From DeepRecommender with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
    """Log a histogram of the tensor of values."""

    # Create a histogram using numpy
    counts, bin_edges = np.histogram(values, bins=bins)

    # Fill the fields of the 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))

    # 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)

    # Create and write Summary
    summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
    self.writer.add_summary(summary, step)
    self.writer.flush()
Example #27
Source File: logger.py    From AutoDL-Projects with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
    """Log a histogram of the tensor of values."""
    if not self.use_tf: raise ValueError('Do not have tensorflow')
    import tensorflow as tf

    # Create a histogram using numpy
    counts, bin_edges = np.histogram(values, bins=bins)

    # Fill the fields of the 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))

    # 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)

    # Create and write Summary
    summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
    self.writer.add_summary(summary, step)
    self.writer.flush()
Example #28
Source File: logger.py    From high-res-stereo with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #29
Source File: logger.py    From tatk with Apache License 2.0 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
Example #30
Source File: tblogger.py    From SmoothParticleNets with MIT License 5 votes vote down vote up 
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the 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))

        # 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)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()