tensorboard

I am trying to get summary of a training process of the neural net below. import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets(".\MNIST",one_hot=True) # Create the model def train_and_test(hidden1,hidden2, learning_rate, epochs, batch_size): with tf.name_scope("first_layer"): input_data = tf.placeholder(tf.float32, [batch_size, 784], name = "input") weights1 = tf.Variable( tf.random_normal(shape =[784, hidden1],stddev=0.1),name = "weights") bias = tf.Variable(tf.constant(0.0,shape =[hidden1]), name = "bias")

Short question: How can I select which checkpoint to view in TensorBoard's embeddings tab? Longer version of the question: I want to visualize word embeddings with TensorBoard. To that end, after reading the official tutorial ( mirror ) I added following code: embedding_writer = tf.summary.FileWriter(model_folder) embeddings_projector_config = projector.ProjectorConfig() embedding = embeddings_projector_config.embeddings.add() embedding.tensor_name = model.W.name # W corresponds to the embeddings' weights. projector.visualize_embeddings(embedding_writer, embeddings_projector_config) #

In Tensorboard there is a button save the computation graph as a png, is there a way to export it as vector graphics like eps? cya You can save in svg format with SVG Crowbar Bookmark the "SVG Crowbar 2" Open tensorboard and the graph you want Open your bookmarks tab, click the bookmark (it may take a long time) Download the corresponding svg (it will output lots of svg, I'm not sure which one is which) 来源： https://stackoverflow.com/questions/40952970/exporting-tensorboard-computation-graph-as-vector-graphics

I am loading a tensorboard for my ml engine experiment that is running in local mode and got the following warning: "Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events. Overwriting the graph with the newest event. W0825 19:26:12.435613 Reloader event_accumulator.py:311] Found more than one metagraph event per run. Overwriting the metagraph with the newest event." Originally, I suspected that this was because I had not cleared my --logdir=$OUTPUT_PATH (as other posts suggested -- however, even if I performed rm -rf

I am considering to move my code base to tf.estimator.Estimator , but I cannot find an example on how to use it in combination with tensorboard summaries. MWE: import numpy as np import tensorflow as tf tf.logging.set_verbosity(tf.logging.INFO) # Declare list of features, we only have one real-valued feature def model(features, labels, mode): # Build a linear model and predict values W = tf.get_variable("W", [1], dtype=tf.float64) b = tf.get_variable("b", [1], dtype=tf.float64) y = W*features['x'] + b loss = tf.reduce_sum(tf.square(y - labels)) # Summaries to display for TRAINING and TESTING

How do I install TensorFlow's tensorboard? Mike Harris The steps to install Tensorflow are here: https://www.tensorflow.org/install/ For example, on Linux for CPU-only (no GPU), you would type this command: pip install -U pip pip install tensorflow Since TensorFlow depends on TensorBoard , running the following command should not be necessary: pip install tensorboard Try typing which tensorboard in your terminal. It should exist if you installed with pip as mentioned in the tensorboard README (although the documentation doesn't tell you that you can now launch tensorboard without doing