Python tensorflow.histogram_summary() Examples

def _activation_summary(self, x):
        """Helper to create summaries for activations.
        Creates a summary that provides a histogram of activations.
        Creates a summary that measure the sparsity of activations.
        Args:
            x: Tensor
        Returns:
            nothing
        """
        # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
        # session. This helps the clarity of presentation on tensorboard.
        # Error: these summaries cause high classifier error!!!
        # All inputs to node MergeSummary/MergeSummary must be from the same frame.

        # tensor_name = re.sub('%s_[0-9]*/' % "tower", '', x.op.name)
        # tf.histogram_summary(tensor_name + '/activations', x)
        # tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) 

def _add_gradients_summaries(grads_and_vars):
  """Add histogram summaries to gradients.

  Note: The summaries are also added to the SUMMARIES collection.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The _list_ of the added summaries for grads_and_vars.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, tf.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(tf.histogram_summary(var.op.name + ':gradient',
                                            grad_values))
      summaries.append(tf.histogram_summary(var.op.name + ':gradient_norm',
                                            tf.global_norm([grad_values])))
    else:
      tf.logging.info('Var %s has no gradient', var.op.name)
  return summaries 

def init_summaries(self):
        """
        Initialize summaries for TensorBoard.
        """
        # train
        # for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
        #    tf.histogram_summary(v.name, v, collections=['train'], name='variables')
        tf.summary.scalar('LOSS/batch_train_loss', self.loss, collections=['train'])
        if hasattr(self, 'learning_rate'):
            tf.summary.scalar('learning_rate', self.learning_rate, collections=['train'])

        # test
        for v in tf.get_collection('moving_avgs'):
            tf.summary.histogram(v.name, v, collections=['test'], name='moving_avgs')

        # images
        nb_imgs = 3
        tf.summary.image('data', self.data, max_outputs=nb_imgs, collections=['images'])
        tf.summary.image('output', self.output_clipped, max_outputs=nb_imgs, collections=['images'])
        tf.summary.image('label', self.labels, max_outputs=nb_imgs, collections=['images']) 

def nn_layer_(self,input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
        """Reusable code for making a simple neural net layer.
        It does a matrix multiply, bias add, and then uses relu to nonlinearize.
        It also sets up name scoping so that the resultant graph is easy to read,
        and adds a number of summary ops.
        """
        # Adding a name scope ensures logical grouping of the layers in the graph.
        with tf.name_scope(layer_name):
            with tf.name_scope('weights'):
                weights = self.weight_variable([input_dim, output_dim])
                self.variable_summaries(weights, layer_name + '/weights')
            with tf.name_scope('biases'):
                biases = self.bias_variable([output_dim])
                self.variable_summaries(biases, layer_name + '/biases')
            with tf.name_scope('Wx_plus_b'):
                preactivate = tf.matmul(input_tensor, weights) + biases
                tf.histogram_summary(layer_name + '/pre_activations', preactivate)               
            activations = act(preactivate, 'activation')
            tf.histogram_summary(layer_name + '/activations', activations)
            
        return activations 

def variable_summaries(var, name):
        """
        Attach a lot of summaries to a Tensor for Tensorboard visualization.
        Ref: https://www.tensorflow.org/versions/r0.11/how_tos/summaries_and_tensorboard/index.html
        :param var: Variable to summarize
        :param name: Summary name
        """
        with tf.name_scope('summaries'):
            mean = tf.reduce_mean(var)
            tf.scalar_summary('mean/' + name, mean)
            with tf.name_scope('stddev'):
                stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
            tf.scalar_summary('stddev/' + name, stddev)
            tf.scalar_summary('max/' + name, tf.reduce_max(var))
            tf.scalar_summary('min/' + name, tf.reduce_min(var))
            tf.histogram_summary(name, var) 

def _activation_summary(x):
  """Helper to create summaries for activations.

  Creates a summary that provides a histogram of activations.
  Creates a summary that measures the sparsity of activations.

  Args:
    x: Tensor
  Returns:
    nothing
  """
  # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
  # session. This helps the clarity of presentation on tensorboard.
  tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
  tf.histogram_summary(tensor_name + '/activations', x)
  tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) 

def _activation_summary(x):
    """Helper to create summaries for activations.
    
    Creates a summary that provides a histogram of activations.
    Creates a summary that measure the sparsity of activations.
    
    Args:
      x: Tensor
    Returns:
      nothing
    """
    # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
    # session. This helps the clarity of presentation on tensorboard.
    tensor_name = re.sub('%s_[0-9]*/' % LSPGlobals.TOWER_NAME, '', x.op.name)
    tf.histogram_summary(tensor_name + '/activations', x)
    tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) 

def fc_layer(self, bottom, name):
        with tf.variable_scope(name) as scope:
            shape = bottom.get_shape().as_list()
            dim = 1
            for d in shape[1:]:
                dim *= d
            x = tf.reshape(bottom, [-1, dim])

            with tf.device('/cpu:0'):
                weights = self.get_fc_weight(name)
                biases = self.get_fc_bias(name)

            # Fully connected layer. Note that the '+' operation automatically
            # broadcasts the biases.
            fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
            #tf.histogram_summary('adascan/'+name+'_activations', fc)
            #tf.histogram_summary('adascan/'+name+'_weights', weights)
            scope.reuse_variables()
            return fc 

def _add_gradients_summaries(grads_and_vars):
  """Add histogram summaries to gradients.

  Note: The summaries are also added to the SUMMARIES collection.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The _list_ of the added summaries for grads_and_vars.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, tf.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(tf.histogram_summary(var.op.name + ':gradient',
                                            grad_values))
      summaries.append(tf.histogram_summary(var.op.name + ':gradient_norm',
                                            tf.global_norm([grad_values])))
    else:
      tf.logging.info('Var %s has no gradient', var.op.name)
  return summaries 

def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
  """Reusable code for making a simple neural net layer.
    
  It does a matrix multiply, bias add, and then uses relu to nonlinearize.
  It also sets up name scoping so that the resultant graph is easy to read,
  and adds a number of summary ops.
  """
  # Adding a name scope ensures logical grouping of the layers in the graph.
  with tf.name_scope(layer_name):
    # This Variable will hold the state of the weights for the layer
    with tf.name_scope('weights'):
      weights = weight_variable([input_dim, output_dim])
      variable_summaries(weights, layer_name + '/weights')
    with tf.name_scope('biases'):
      biases = bias_variable([output_dim])
      variable_summaries(biases, layer_name + '/biases')
    with tf.name_scope('Wx_plus_b'):
      preactivate = tf.matmul(input_tensor, weights) + biases
      tf.histogram_summary(layer_name + '/pre_activations', preactivate)
    activations = act(preactivate, 'activation')
    tf.histogram_summary(layer_name + '/activations', activations)
    return activations 

def _setup_training(self):
        """
        Set up a data flow graph for fine tuning
        """
        layer_num = self.layer_num
        act_func = ACTIVATE_FUNC[self.activate_func]
        sigma = self.sigma
        lr = self.learning_rate
        weights = self.weights
        biases = self.biases
        data1, data2 = self.data1, self.data2
        batch_size = self.batch_size
        optimizer = OPTIMIZER[self.optimizer]
        with tf.name_scope("training"):
            s1 = self._obtain_score(data1, weights, biases, act_func, "1")
            s2 = self._obtain_score(data2, weights, biases, act_func, "2")
            with tf.name_scope("cost"):
                sum_cost = tf.reduce_sum(tf.log(1 + tf.exp(-sigma*(s1-s2))))
                self.cost = cost = sum_cost / batch_size
        self.optimize = optimizer(lr).minimize(cost)

        for n in range(layer_num-1):
            tf.histogram_summary("weight"+str(n), weights[n])
            tf.histogram_summary("bias"+str(n), biases[n])
        tf.scalar_summary("cost", cost) 

def _activation_summary(x):
  """Helper to create summaries for activations.

  Creates a summary that provides a histogram of activations.
  Creates a summary that measure the sparsity of activations.

  Args:
    x: Tensor
  Returns:
    nothing
  """
  # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
  # session. This helps the clarity of presentation on tensorboard.
  tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
  # tf.histogram_summary(tensor_name + '/activations', x)
  tf.summary.histogram(tensor_name + '/activations', x)
  # tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
  tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) 

def _activation_summary(x):
  """Helper to create summaries for activations.

  Creates a summary that provides a histogram of activations.
  Creates a summary that measure the sparsity of activations.

  Args:
    x: Tensor
  Returns:
    nothing
  """
  # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
  # session. This helps the clarity of presentation on tensorboard.
  tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
  # tf.histogram_summary(tensor_name + '/activations', x)
  tf.summary.histogram(tensor_name + '/activations', x)
  # tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
  tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) 

def define_summaries(self):
        '''Helper function for init_opt'''
        all_sum = {'g': [], 'd': [], 'hr_g': [], 'hr_d': [], 'hist': []}
        for k, v in self.log_vars:
            if k.startswith('g'):
                all_sum['g'].append(tf.scalar_summary(k, v))
            elif k.startswith('d'):
                all_sum['d'].append(tf.scalar_summary(k, v))
            elif k.startswith('hr_g'):
                all_sum['hr_g'].append(tf.scalar_summary(k, v))
            elif k.startswith('hr_d'):
                all_sum['hr_d'].append(tf.scalar_summary(k, v))
            elif k.startswith('hist'):
                all_sum['hist'].append(tf.histogram_summary(k, v))

        self.g_sum = tf.merge_summary(all_sum['g'])
        self.d_sum = tf.merge_summary(all_sum['d'])
        self.hr_g_sum = tf.merge_summary(all_sum['hr_g'])
        self.hr_d_sum = tf.merge_summary(all_sum['hr_d'])
        self.hist_sum = tf.merge_summary(all_sum['hist']) 

def _activation_summary(x):
    """Helper to create summaries for activations.

    Creates a summary that provides a histogram of activations.
    Creates a summary that measure the sparsity of activations.

    Args:
      x: Tensor
    Returns:
      nothing
    """
    # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
    # session. This helps the clarity of presentation on tensorboard.
    tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
    tf.histogram_summary(tensor_name + '/activations', x)
    tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) 

def _add_gradients_summaries(grads_and_vars):
  """Add histogram summaries to gradients.

  Note: The summaries are also added to the SUMMARIES collection.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The _list_ of the added summaries for grads_and_vars.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, tf.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(tf.histogram_summary(var.op.name + ':gradient',
                                            grad_values))
      summaries.append(tf.histogram_summary(var.op.name + ':gradient_norm',
                                            tf.global_norm([grad_values])))
    else:
      tf.logging.info('Var %s has no gradient', var.op.name)
  return summaries 

def _decay(self):
        """L2 weight decay loss."""
        costs = []
        for var in tf.trainable_variables():
            if var.op.name.find(r'weights') > 0:
                costs.append(tf.nn.l2_loss(var))
                # tf.histogram_summary(var.op.name, var)

        return tf.multiply(self.weight_decay_rate, tf.add_n(costs)) 

def _add_variables_summaries(learning_rate):
  summaries = []
  for variable in slim.get_model_variables():
    summaries.append(tf.histogram_summary(variable.op.name, variable))
  summaries.append(tf.scalar_summary('training/Learning Rate', learning_rate))
  return summaries 

def GetWeightDecay(self):
    """L2 weight decay loss."""
    costs = []
    for var in self.trainable_variables:
      if var.op.name.find(r'DW') > 0:
        costs.append(tf.nn.l2_loss(var))
        # tf.histogram_summary(var.op.name, var)

    return tf.mul(self.hps.weight_decay_rate, tf.add_n(costs)) 

def _decay(self):
    """L2 weight decay loss."""
    costs = []
    for var in tf.trainable_variables():
      if var.op.name.find(r'DW') > 0:
        costs.append(tf.nn.l2_loss(var))
        # tf.histogram_summary(var.op.name, var)

    return tf.mul(self.hps.weight_decay_rate, tf.add_n(costs)) 

def _init_summaries(self):
        self.accuracy = tf.placeholder_with_default(0.0, shape=(), name='Accuracy')
        self.accuracy_summary = tf.scalar_summary('Accuracy summary', self.accuracy)

        self.f_pos_summary = tf.histogram_summary('f_pos', self.f_pos)
        self.f_neg_summary = tf.histogram_summary('f_neg', self.f_neg)

        self.loss_summary = tf.scalar_summary('Mini-batch loss', self.loss)
        self.summary_op = tf.merge_summary(
            [
                self.f_pos_summary,
                self.f_neg_summary,
                self.loss_summary
            ]
        ) 

def _decay(self):
    """L2 weight decay loss."""
    costs = []
    for var in tf.trainable_variables():
      if var.op.name.find(r'DW') > 0:
        costs.append(tf.nn.l2_loss(var))
        # tf.histogram_summary(var.op.name, var)

    return tf.multiply(self.hps.weight_decay_rate, tf.add_n(costs)) 

def _decay(self):
        """L2 weight decay loss."""
        costs = []
        for var in tf.trainable_variables():
            if var.op.name.find(r'DW') > 0:
                costs.append(tf.nn.l2_loss(var))
                # tf.histogram_summary(var.op.name, var)

        return tf.multiply(self.weight_decay_rate, tf.add_n(costs)) 

def _decay(self):
        """L2 weight decay loss."""
        costs = []
        for var in tf.trainable_variables():
            if var.op.name.find(r'DW') > 0:
                costs.append(tf.nn.l2_loss(var))
                # tf.histogram_summary(var.op.name, var)

        return tf.multiply(self.weight_decay_rate, tf.add_n(costs)) 

def _decay(self):
        """L2 weight decay loss."""
        costs = []
        for var in tf.trainable_variables():
            if var.op.name.find(r'DW') > 0:
                costs.append(tf.nn.l2_loss(var))
                # tf.histogram_summary(var.op.name, var)

        return tf.multiply(self.weight_decay_rate, tf.add_n(costs)) 

def add_activation_summary(var):
    if var is not None:
        tf.histogram_summary(var.op.name + "/activation", var)
        tf.scalar_summary(var.op.name + "/sparsity", tf.nn.zero_fraction(var)) 

def variable_summaries(var, name):
  """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
  with tf.name_scope('summaries'):
    mean = tf.reduce_mean(var)
    scalar_summary('mean/' + name, mean)
    with tf.name_scope('stddev'):
      stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
    scalar_summary('sttdev/' + name, stddev)
    scalar_summary('max/' + name, tf.reduce_max(var))
    scalar_summary('min/' + name, tf.reduce_min(var))
    if(int(tf.__version__.split(".")[0])<1): ### For tf v<1.0
      tf.histogram_summary(name, var)
    else: ### For tf v>=1.0 
      tf.summary.histogram(name, var) 

def _activation_summary(self, tensor):
        tensor_name = tensor.op.name
        tf.histogram_summary(tensor_name + '/activations', tensor)
        tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(tensor)) 

def add_gradient_summary(grad, var):
    if grad is not None:
        tf.histogram_summary(var.op.name + "/gradient", grad) 

def add_activation_summary(var):
    tf.histogram_summary(var.op.name + "/activation", var)
    tf.scalar_summary(var.op.name + "/sparsity", tf.nn.zero_fraction(var))