Python tensorflow.scalar_summary() Examples
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Example #1
Source File: summary.py From VDAIC2017 with MIT License | 6 votes |
def summary_moving_average(): """ Create a MovingAverage op and summary for all variables in MOVING_SUMMARY_VARS_KEY. :returns: a op to maintain these average. """ with tf.name_scope('EMA_summary'): global_step_var = get_global_step_var() with tf.name_scope(None): averager = tf.train.ExponentialMovingAverage( 0.99, num_updates=global_step_var, name='EMA') vars_to_summary = tf.get_collection(MOVING_SUMMARY_VARS_KEY) avg_maintain_op = averager.apply(vars_to_summary) for idx, c in enumerate(vars_to_summary): # TODO assert scalar name = re.sub('tower[p0-9]+/', '', c.op.name) tf.scalar_summary(name, averager.average(c)) return avg_maintain_op
Example #2
Source File: neural_network.py From sentiment-analysis-tensorflow with Apache License 2.0 | 6 votes |
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)
Example #3
Source File: human_pose_nn.py From gait-recognition with BSD 3-Clause "New" or "Revised" License | 6 votes |
def _init_summaries(self): if self.is_train: logdir = os.path.join(SUMMARY_PATH, self.log_name, 'train') self.summary_writer = tf.summary.FileWriter(logdir) self.summary_writer_by_points = [tf.summary.FileWriter(os.path.join(logdir, 'point_%02d' % i)) for i in range(16)] tf.scalar_summary('Average euclidean distance', self.euclidean_dist, collections = [KEY_SUMMARIES]) for i in range(16): tf.scalar_summary('Joint euclidean distance', self.euclidean_dist_per_joint[i], collections = [KEY_SUMMARIES_PER_JOINT[i]]) self.create_summary_from_weights() self.ALL_SUMMARIES = tf.merge_all_summaries(KEY_SUMMARIES) self.SUMMARIES_PER_JOINT = [tf.merge_all_summaries(KEY_SUMMARIES_PER_JOINT[i]) for i in range(16)] else: logdir = os.path.join(SUMMARY_PATH, self.log_name, 'test') self.summary_writer = tf.summary.FileWriter(logdir)
Example #4
Source File: actor_learner.py From async-deep-rl with Apache License 2.0 | 6 votes |
def setup_summaries(self): episode_reward = tf.Variable(0.) s1 = tf.scalar_summary("Episode Reward " + str(self.actor_id), episode_reward) if self.alg_type == "a3c": summary_vars = [episode_reward] else: episode_ave_max_q = tf.Variable(0.) s2 = tf.scalar_summary("Max Q Value " + str(self.actor_id), episode_ave_max_q) logged_epsilon = tf.Variable(0.) s3 = tf.scalar_summary("Epsilon " + str(self.actor_id), logged_epsilon) summary_vars = [episode_reward, episode_ave_max_q, logged_epsilon] summary_placeholders = [tf.placeholder("float") for _ in range(len(summary_vars))] update_ops = [summary_vars[i].assign(summary_placeholders[i]) for i in range(len(summary_vars))] with tf.control_dependencies(update_ops): summary_ops = tf.merge_all_summaries() return summary_placeholders, update_ops, summary_ops
Example #5
Source File: cifar10.py From deep_image_model with Apache License 2.0 | 6 votes |
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))
Example #6
Source File: ranknet.py From tfranknet with GNU General Public License v2.0 | 6 votes |
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)
Example #7
Source File: cifar10.py From deep_image_model with Apache License 2.0 | 6 votes |
def _add_loss_summaries(total_loss): """Add summaries for losses in CIFAR-10 model. Generates moving average for all losses and associated summaries for visualizing the performance of the network. Args: total_loss: Total loss from loss(). Returns: loss_averages_op: op for generating moving averages of losses. """ # Compute the moving average of all individual losses and the total loss. loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) # Attach a scalar summary to all individual losses and the total loss; do the # same for the averaged version of the losses. for l in losses + [total_loss]: # Name each loss as '(raw)' and name the moving average version of the loss # as the original loss name. tf.scalar_summary(l.op.name +' (raw)', l) tf.scalar_summary(l.op.name, loss_averages.average(l)) return loss_averages_op
Example #8
Source File: kmeans.py From deep_image_model with Apache License 2.0 | 6 votes |
def _get_train_ops(self, features, _): (_, _, losses, training_op) = clustering_ops.KMeans( self._parse_tensor_or_dict(features), self._num_clusters, self._training_initial_clusters, self._distance_metric, self._use_mini_batch, random_seed=self._random_seed, kmeans_plus_plus_num_retries=self.kmeans_plus_plus_num_retries ).training_graph() incr_step = tf.assign_add(tf.contrib.framework.get_global_step(), 1) self._loss = tf.reduce_sum(losses) tf.scalar_summary('loss/raw', self._loss) training_op = with_dependencies([training_op, incr_step], self._loss) return training_op, self._loss
Example #9
Source File: LSPModels.py From deeppose with GNU General Public License v3.0 | 6 votes |
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))
Example #10
Source File: LSPModels.py From deeppose with GNU General Public License v3.0 | 6 votes |
def _add_loss_summaries(total_loss): """Add summaries for losses in DeepPose model. Generates moving average for all losses and associated summaries for visualizing the performance of the network. Args: total_loss: Total loss from loss(). Returns: loss_averages_op: op for generating moving averages of losses. """ # Compute the moving average of all individual losses and the total loss. loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) # Attach a scalar summmary to all individual losses and the total loss; do the # same for the averaged version of the losses. for l in losses + [total_loss]: # Name each loss as '(raw)' and name the moving average version of the loss # as the original loss name. tf.scalar_summary(l.op.name +' (raw)', l) tf.scalar_summary(l.op.name, loss_averages.average(l)) return loss_averages_op
Example #11
Source File: statistic.py From gated-pixel-cnn with Apache License 2.0 | 6 votes |
def __init__(self, sess, data, runtime_base_dir, model_dir, variables, max_to_keep=20): self.sess = sess self.reset() with tf.variable_scope('t'): self.t_op = tf.Variable(0, trainable=False, name='t') self.t_add_op = self.t_op.assign_add(1) self.model_dir = os.path.join(runtime_base_dir, model_dir) self.saver = tf.train.Saver(variables + [self.t_op], max_to_keep=max_to_keep) self.writer = tf.train.SummaryWriter('%s/logs/%s' % (runtime_base_dir, model_dir), self.sess.graph) with tf.variable_scope('summary'): scalar_summary_tags = ['train_l', 'test_l'] self.summary_placeholders = {} self.summary_ops = {} for tag in scalar_summary_tags: self.summary_placeholders[tag] = tf.placeholder('float32', None, name=tag.replace(' ', '_')) self.summary_ops[tag] = tf.scalar_summary('%s/%s' % (data, tag), self.summary_placeholders[tag])
Example #12
Source File: cifar10.py From ml with Apache License 2.0 | 6 votes |
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))
Example #13
Source File: cifar10.py From ml with Apache License 2.0 | 6 votes |
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))
Example #14
Source File: deepaudio.py From iLID with MIT License | 6 votes |
def accuracy(logits, dense_labels): seen_german = tf.Variable(0, trainable=False) seen_english = tf.Variable(0, trainable=False) x = tf.nn.softmax(logits) correct_pred = tf.equal(tf.argmax(x, 1), tf.argmax(dense_labels, 1)) german_samples = tf.equal(tf.constant(1, dtype="int64"), tf.argmax(dense_labels, 1)) german_accuracy = tf.reduce_mean(tf.cast(tf.gather(correct_pred, tf.where(german_samples)), tf.float32)) sum_german_samples = seen_german.assign_add(tf.reduce_sum(tf.cast(tf.gather(dense_labels, tf.where(german_samples)), tf.int32))) tf.scalar_summary("german_accuracy", german_accuracy) english_samples = tf.equal(tf.constant(0, dtype="int64"), tf.argmax(dense_labels, 1)) english_accuracy = tf.reduce_mean(tf.cast(tf.gather(correct_pred, tf.where(english_samples)), tf.float32)) sum_english_samples = seen_english.assign_add(tf.reduce_sum(tf.cast(tf.gather(dense_labels, tf.where(english_samples)), tf.int32))) tf.scalar_summary("english_accuracy", english_accuracy) german_predictions = tf.equal(tf.constant(1, dtype="int64"), tf.argmax(x, 1)) german_predictions_count = tf.reduce_sum(tf.cast(german_predictions, tf.int32)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) tf.scalar_summary("accuracy", accuracy) return accuracy, english_accuracy, german_accuracy, german_predictions_count, sum_english_samples, sum_german_samples
Example #15
Source File: trainer.py From StackGAN with MIT License | 6 votes |
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'])
Example #16
Source File: model.py From web_page_classification with MIT License | 6 votes |
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))
Example #17
Source File: selector.py From dynamic-coattention-network with MIT License | 6 votes |
def _add_train_op(self): params = self._params self._lr_rate = tf.maximum( params.min_lr, tf.train.exponential_decay(params.lr, self._global_step, 30000, 0.98)) tvars = tf.trainable_variables() # use reserved gpu for gradient computation with tf.device(self._get_gpu(self._num_gpus-1)): grads, global_norm = tf.clip_by_global_norm( tf.gradients(self._loss, tvars), params.max_grad_norm) tf.scalar_summary('global_norm', global_norm) optimizer = tf.train.AdamOptimizer(self._lr_rate) tf.scalar_summary('learning rate', self._lr_rate) with tf.device(self._next_device()): self._train_op = optimizer.apply_gradients( zip(grads, tvars), global_step=self._global_step, name='train_step') self._summaries = tf.merge_all_summaries() return self._train_op, self._loss,
Example #18
Source File: deepaudio.py From iLID with MIT License | 6 votes |
def _add_loss_summaries(total_loss): """Add summaries for losses in model. Generates moving average for all losses and associated summaries for visualizing the performance of the network. Args: total_loss: Total loss from loss(). Returns: loss_averages_op: op for generating moving averages of losses. """ # Compute the moving average of all individual losses and the total loss. loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) # Attach a scalar summmary to all individual losses and the total loss; do the # same for the averaged version of the losses. for l in losses + [total_loss]: # Name each loss as '(raw)' and name the moving average version of the loss # as the original loss name. tf.scalar_summary(l.op.name +' (raw)', l) tf.scalar_summary(l.op.name, loss_averages.average(l)) return loss_averages_op
Example #19
Source File: summary.py From VDAIC2017 with MIT License | 5 votes |
def add_activation_summary(x, name=None): """ Add summary to graph for an activation tensor x. If name is None, use x.name. """ ndim = x.get_shape().ndims assert ndim >= 2, \ "Summary a scalar with histogram? Maybe use scalar instead. FIXME!" if name is None: name = x.name with tf.name_scope('act_summary'): tf.histogram_summary(name + '/activation', x) tf.scalar_summary(name + '/activation_sparsity', tf.nn.zero_fraction(x)) tf.scalar_summary( name + '/activation_rms', rms(x))
Example #20
Source File: network.py From iLID with MIT License | 5 votes |
def set_activation_summary(self): '''Log each layers activations and sparsity.''' tf.image_summary("input images", self.input_layer.output, max_images=100) for var in tf.trainable_variables(): tf.histogram_summary(var.op.name, var) for layer in self.hidden_layers: tf.histogram_summary(layer.name + '/activations', layer.output) tf.scalar_summary(layer.name + '/sparsity', tf.nn.zero_fraction(layer.output))
Example #21
Source File: item2vec.py From board-yet with GNU General Public License v3.0 | 5 votes |
def init_graphs(self): # TODO(carson): Get variables representing batches and labels self.batch = tf.placeholder(dtype=tf.int32, shape=[None, self.batch_size]) self.labels = tf.placeholder(dtype=tf.int32, shape=[None, self.batch_size]) batch_logits, neg_logits = self.build_training_graph(self.batch, self.labels) self.loss = self.loss_function(batch_logits, neg_logits) tf.scalar_summary("NCE loss", self.loss) self.train_node = self.build_optimize_graph(self.loss) tf.initialize_all_variables().run() self.saver = tf.train.Saver()
Example #22
Source File: net_utils.py From scene-graph-TF-release with MIT License | 5 votes |
def exp_average_summary(ops, dep_ops, decay=0.9, name='avg', scope_pfix='', raw_pfix=' (raw)', avg_pfix=' (avg)'): averages = tf.train.ExponentialMovingAverage(decay, name=name) averages_op = averages.apply(ops) for op in ops: tf.scalar_summary(scope_pfix + op.name + raw_pfix, op) tf.scalar_summary(scope_pfix + op.name + avg_pfix, averages.average(op)) with tf.control_dependencies([averages_op]): for i, dep_op in enumerate(dep_ops): dep_ops[i] = tf.identity(dep_op, name=dep_op.name.split(':')[0]) return dep_ops
Example #23
Source File: network.py From scene-graph-TF-release with MIT License | 5 votes |
def _variable_summaries(self, var, name): """Attach a lot of summaries to a Tensor.""" with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.scalar_summary('mean/' + name, mean) tf.histogram_summary(name, var)
Example #24
Source File: model.py From web_page_classification with MIT License | 5 votes |
def _add_loss_summaries(self, total_loss): """Add summaries for losses in CNN model. Generates moving average for all losses and associated summaries for visualizing the performance of the network. Args: total_loss: Total loss from loss(). Returns: loss_averages_op: op for generating moving averages of losses. """ # # Compute the moving average of all individual losses and the total loss. # loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') # losses = tf.get_collection('losses') # loss_averages_op = loss_averages.apply(losses + [total_loss]) # # # Attach a scalar summary to all individual losses and the total loss; do the # # same for the averaged version of the losses. # for l in losses + [total_loss]: # # Name each loss as '(raw)' and name the moving average version of the loss # # as the original loss name. # tf.scalar_summary(l.op.name + ' (raw)', l) # tf.scalar_summary(l.op.name, loss_averages.average(l)) # losses = tf.get_collection('REGULARIZATION_LOSSES') # all_losses = losses + [total_loss] all_losses = [total_loss] # is it necessary to add all REGULARIZATION_LOSSES ????? for l in all_losses: tf.scalar_summary(l.op.name, l)
Example #25
Source File: opt.py From phillip with GNU General Public License v3.0 | 5 votes |
def optimize(self, loss, params=None, predictions=None, metric=None): grads_vars = self.optimizer.compute_gradients(loss, var_list=params) grads, params = map(list, zip(*grads_vars)) if self.natural: grads = self.natgrad(params, grads, predictions, metric) grads = [tf.check_numerics(g, "NaN gradient in param %d" % i) for i, g in enumerate(grads)] flat_params, flat_grads = [tf.abs(tf.concat(axis=0, values=[tf.reshape(t, [-1]) for t in ts])) for ts in (params, grads)] #flat_ratios = flat_grads / flat_params #tf.scalar_summary('grad_param_max', tf.reduce_max(flat_ratios)) #tf.scalar_summary('grad_param_avg', tf.reduce_mean(flat_ratios)) grad_max = tf.reduce_max(flat_grads) tf.summary.scalar('grad_max', grad_max) tf.summary.scalar('grad_avg', tf.reduce_mean(flat_grads)) if self.clip: clip = tf.minimum(self.clip, grad_max) / grad_max grads = [g*clip for g in grads] return self.optimizer.apply_gradients(zip(grads, params))
Example #26
Source File: neural_network.py From sentiment-analysis-tensorflow with Apache License 2.0 | 5 votes |
def __loss(self, losses): """ :param losses: Cross entropy losses with shape [batch_size] :return: Cross entropy loss mean """ with tf.name_scope('loss'): loss = tf.reduce_mean(losses, name='loss') tf.scalar_summary('loss', loss) return loss
Example #27
Source File: kaggle_mnist_alexnet_model.py From tensorflow-alexnet with MIT License | 5 votes |
def accuracy(logits, labels): # accuracy with tf.name_scope('accuracy'): accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)), tf.float32)) tf.scalar_summary('accuracy', accuracy) return accuracy
Example #28
Source File: network.py From iLID with MIT License | 5 votes |
def set_accuracy(self): correct_pred = tf.equal(tf.argmax(self.layers.output, 1), tf.argmax(self.y, 1)) self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) tf.scalar_summary("accuracy", self.accuracy)
Example #29
Source File: deepaudio.py From iLID with MIT License | 5 votes |
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 """ tensor_name = x.op.name tf.histogram_summary(tensor_name + '/activations', x) tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
Example #30
Source File: network.py From iLID with MIT License | 5 votes |
def set_optimizer(self, learning_rate, decay_steps, optimizer = tf.train.AdamOptimizer): global_step = tf.Variable(0, trainable=False) lr = tf.train.exponential_decay(learning_rate, global_step, decay_steps, 0.1, staircase=True) tf.scalar_summary("learning_rate", lr) self.optimizer = optimizer(learning_rate=lr).minimize(self.cost, global_step = global_step)