tensorboard

I'm looking for a tensorboard embedding example, with iris data for example like the embedding projector http://projector.tensorflow.org/ But unfortunately i couldn't find one. Just a little bit information about how to do it in https://www.tensorflow.org/how_tos/embedding_viz/ Does someone knows a basic tutorial for this functionality? Basics: 1) Setup a 2D tensor variable(s) that holds your embedding(s). embedding_var = tf.Variable(....) 2) Periodically save your embeddings in a LOG_DIR. 3) Associate metadata with your embedding. It sounds like you want to get the Visualization section with

Is there a way to display the average of multiple different runs on tensorflow? I can only see them on the same graph (by sending the path of the different runs), but I want to see their average on the graph Please follow issue 376 to see progress on this. It's an active feature request with some progress in the last month, but as of now, there's not a way to do what you want. Yet. As @dga mentioned this is not implemented yet. Here is some code that uses EventAccumulator to combine scalar tensorflow summary values. This can be extended to accommodate the other summary types. import os from

This is a follow-up of another question by me : Error with 8-bit Quantization in Tensorflow Basically, I would like to install the Tensorflow with 8-bit quantization support. Currently, I installed Tensorflow 0.9 with pip installation method on CentOS 7 machine (without GPU support). I could compile and run the codes as given in Pete Warden's blog post. But, I can't import the functions given in Pete Warden's reply. I would like to add the quantization support. I couldn't find any details about the quantization part in the Tensorflow documentation also. Can anybody share the details on how to

launch tensorboard with tensorboard --logdir=/home/vagrant/notebook at tensorboard:6006 > graph, it says No graph definition files were found. To store a graph, create a tf.python.training.summary_io.SummaryWriter and pass the graph either via the constructor, or by calling its add_graph() method. import tensorflow as tf sess = tf.Session() writer = tf.python.training.summary_io.SummaryWriter("/home/vagrant/notebook", sess.graph_def) However the page is still empty, how can I start playing with tensorboard? current tensorboard result wanted An empty graph that can add nodes, editable. update

I'm using TensorBoard to visualize network metrics and graph. I create a session sess = tf.InteractiveSession() and build the graph in Jupyter notebook. In the graph, I include two summary scalars: with tf.variable_scope('summary') as scope: loss_summary = tf.summary.scalar('Loss', cross_entropy) train_accuracy_summary = tf.summary.scalar('Train_accuracy', accuracy) I then create a summary_writer = tf.summary.FileWriter(logdir, sess.graph) and run: _,loss_sum,train_accuracy_sum=sess.run([...],feed_dict=feed_dict) I write the metrics: summary_writer.add_summary(loss_sum, i) summary_writer.add

I'm trying to visualize a tensor summary in tensorboard. However I can't see the tensor summary at all in the board. Here is my code: out = tf.strided_slice(logits, begin=[self.args.uttWindowSize-1, 0], end=[-self.args.uttWindowSize+1, self.args.numClasses], strides=[1, 1], name='softmax_truncated') tf.summary.tensor_summary('softmax_input', out) where out is a multi-dimensional tensor. I guess there must be something wrong with my code. Probably I used the tensor_summary function incorrectly. Michael Gygli What you do is you create a summary op, but you don't invoke it and don't write the

I am using several LSTM layers to form a deep recurrent neural network. I would like to monitor the weights of each LSTM layer during training. However, I couldn't find out how to attach summaries of the LSTM layer weights to TensorBoard. Any suggestions on how this can be done? The code is as follows: cells = [] with tf.name_scope("cell_1"): cell1 = tf.contrib.rnn.LSTMCell(self.embd_size, state_is_tuple=True, initializer=self.initializer) cell1 = tf.contrib.rnn.DropoutWrapper(cell1, input_keep_prob=self.input_dropout, output_keep_prob=self.output_dropout, state_keep_prob=self.recurrent

I've written the following simple MLP network for the MNIST db. from __future__ import print_function import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout from keras import callbacks batch_size = 100 num_classes = 10 epochs = 20 tb = callbacks.TensorBoard(log_dir='/Users/shlomi.shwartz/tensorflow/notebooks/logs/minist', histogram_freq=10, batch_size=32, write_graph=True, write_grads=True, write_images=True, embeddings_freq=10, embeddings_layer_names=None, embeddings_metadata=None) early_stop = callbacks.EarlyStopping(monitor=