Tip Top Code

Via: https://blog.metaflow.fr/tensorflow-saving-restoring-and-mixing-multiple-models-c4c94d5d7125 TensorFlow: saving/restoring and mixing multiple models Before going any further, make sure you read the  very small primer i made on Tensorflow here Why start with those informations? Because, it is of tremendous importance to understand what can be saved at the different level of your code to avoid messing around cluelessly… How to actually save and load something The saver object Any interactions with your filesystem to have persistent data through different sessions can be handled with the  Saver  object. The constructor allows you to control 3 things: The  target : This is used in case of a distributed architecture to handle computation. You can specify which TF server or ‘target’ you want to compute on. The  graph:  the graph you want the  Session  to handle. The tricky things here for beginners, is the fact tha...

via: https://kevincodeidea.wordpress.com/2016/08/02/tensorflow-save-and-load-a-model-in-a-serious-way-from-different-files/ It has been a long time since my last post. Recently I am working in a group developing a deep, online, traceable, better-than-current-method neural network. After carefully comparing theano and tensorflow, we decide to use the latter. The main reason is actually not technical, we simply “predict” tensorflow will have a bright future and will be better maintained. Back to the topic. Since it is an online algorithm, one important requirement is that one has to be able to save the model (not just some script-like operations, but also meta data, trained weights and the whole structure) to the disk and should be able to load the whole thing without a problem. The way I construct a model can be simplified as this: A basic model class contains all the tensorflow variables, in this application, the weights for each layer Several training functions that  cons...

Via:  http://stackoverflow.com/questions/36693740/whats-the-difference-between-tf-placeholder-and-tf-variable In short, you use  tf.Variable  for trainable variables such as weights (W) and biases (B) for your model. weights = tf.Variable( tf.truncated_normal([IMAGE_PIXELS, hidden1_units], stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))), name='weights') biases = tf.Variable(tf.zeros([hidden1_units]), name='biases') tf.placeholder  is used to feed actual training examples. images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, IMAGE_PIXELS)) labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size)) This is how you feed the training examples during the training: for step in xrange(FLAGS.max_steps): feed_dict = { images_placeholder: images_feed, labels_placeholder: labels_feed, } _, loss_value = sess.run([train_op, loss], feed_dict=feed_dict) Your  tf.variables  will be trained ...