tensorboard

问题 As the weights are not explicitly defined, how can I pass them to a summary writer? For exemple: conv1 = tf.layers.conv2d( tf.reshape(X,[FLAGS.batch,3,160,320]), filters = 16, kernel_size = (8,8), strides=(4, 4), padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(), bias_initializer=tf.zeros_initializer(), kernel_regularizer=None, name = 'conv1', activation = tf.nn.elu ) => summarize_tensor( ?????? ) Thanks! 回答1: That depends on what you are going to record in TensorBoard

问题 I created Tensorboard logs with a graph, using the tutorials/mnist/fully_connected_feed.py script (I also have the same issue on other scripts). summary_writer = tf.train.SummaryWriter("logdir", graph_def=sess.graph_def) The Tensorboard works well for Scalars, Images and Histograms, but not for graphs. I get a white page, with only the header. If I open the browser console ( Firefox 42, Ubuntu 14.04 ), I get those errors when I switch to the GRAPH tab: NS_ERROR_FAILURE: tf-tensorboard.html-12

问题 I upgraded to Tensorflow 2.0 and there is no tf.summary.FileWriter("tf_graphs", sess.graph) . I was looking through some other StackOverflow questions on this and they said to use tf.compat.v1.summary etc . Surely there must be a way to graph and visualize a tf.keras model in Tensorflow version 2. What is it? I'm looking for a tensorboard output like the one below. Thank you! 回答1: According to the docs, you can use Tensorboard to visualise graphs once your model has been trained. First,

问题 I'm using the tflearn wrapper over tensorflow to build a model, and would like to add metadata (labels) to the resultant embedding visualization. Is there a way to link a metadata.tsv file to a saved checkpoint after the fact of running it? I've created a projector_config.pbtxt file in the logdir of the checkpoint summaries, with the metadata.tsv being in the same folder. The config looks like this: embeddings { tensor_name: "Embedding/W" metadata_path: "C:/tmp/tflearn_logs/shallow_lstm/" }

问题 I want to visualize weights in convolutional layers to watch how they change. But I can not find a way to access weights in convolutional layers in tf.layers.conv2d Thank you 回答1: You could access that variable by name: weights = sess.run('<name_of_your_layer>/weights:0', feed_dict=...) If you're unsure about the name of your variable, see what it could be by printing tf.trainable_variables() 回答2: With inspiration from this: How to get CNN kernel values in Tensorflow Make sure to give it a

问题 Here is my tensorflow keras model,(you can ignore dropout layer if it makes things tough) import tensorflow as tf optimizers = tf.keras.optimizers Sequential = tf.keras.models.Sequential Dense = tf.keras.layers.Dense Dropout = tf.keras.layers.Dropout to_categorical = tf.keras.utils.to_categorical model = Sequential() model.add(Dense(256, input_shape=(20,), activation="relu")) model.add(Dropout(0.1)) model.add(Dense(256, activation="relu")) model.add(Dropout(0.1)) model.add(Dense(256,

问题 output = tf.nn.conv2d(input, weights, strides = [1,3,3,1], padding = 'VALID') My input has shape 200x225x225x1, weights is 15x15x1x64. Hence, the output has shape 200x71x71x64 since (225-15)/3 + 1 = 71 Tensorboard shows that this operation consumes totally 768MB (see pic below). Assuming it takes into account the size of input (38.6MB), weights (0.06MB) and output (246.2MB) the total memory consumption should not exceed 300MB. So where does the rest of the memory consumption come from? 回答1:

问题 In my Python code I execute train_writer = tf.summary.FileWriter(TBOARD_LOGS_DIR) train_writer.add_graph(sess.graph) I can see 1.6MB file created in E:\progs\tensorboard_logs (and no other file) but then when I execute tensorboard --logdir=E:\progs\tensorboard_logs it loads, but says: "No graph definition files were found." when I click on Graph. Additionally, running tensorboard --inspect --logdir=E:\progs\tensorboard_logs displays Found event files in: E:\progs\tensorboard_logs These tags

问题 I have wrote a simple code to try out the Tensorflow summarize feature. The code is below. import tensorflow as tf import numpy as np graph = tf.Graph() with graph.as_default(): x = tf.placeholder(tf.float32, [1, 2], name='x') W = tf.ones([2, 1], tf.float32, name='W') b = tf.constant([1.5], dtype=tf.float32, shape=(1, 1), name='bias') y_ = tf.add(tf.matmul(x, W, name='mul'), b, name='add') tf.summary.scalar('y', y_) with tf.Session(graph=graph) as session: merged = tf.summary.merge_all() fw =

问题 Problem : How can i convert a .tflite (serialised flat buffer) to .pb (frozen model)? The documentation only talks about one way conversion. Use-case is : I have a model that is trained on converted to .tflite but unfortunately, i do not have details of the model and i would like to inspect the graph, how can i do that? 回答1: I don't think there is a way to restore tflite back to pb as some information are lost after conversion. I found an indirect way to have a glimpse on what is inside