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For more information, refer to Yann Le Cun's MNIST page or Chris Olah's visualizations of MNIST.

At the top of the function builds the graph as far as needed to return the tensor that would contain the output predictions.

x = q.dequeue_many(batch_size) # Inference, train op, and accuracy calculation after this point # ... Session(graph=graph) as sess: # Initialize variables sess.run(tf.global_variables_initializer()) sess.run(tf.local_variables_initializer()) # Start populating the queue. Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) try: for epoch in range(epochs): print("-"*60) for step in range(batches_per_epoch): if coord.should_stop(): break train_batch = sess.run(x, feed_dict=) print("TRAIN_BATCH: ".format(train_batch)) valid_batch = sess.run(x, feed_dict=) print("\n VALID_BATCH : \n".format(valid_batch)) except Exception, e: coord.request_stop(e) finally: coord.request_stop() coord.join(threads) ------------------------------------------------------------ TRAIN_BATCH: ['train_file_0' 'train_file_1' 'train_file_2'] TRAIN_BATCH: ['train_file_3' 'train_file_4' 'train_file_5'] VALID_BATCH : ['train_file_0' 'train_file_1' 'train_file_2'] ------------------------------------------------------------ TRAIN_BATCH: ['train_file_3' 'train_file_4' 'train_file_5'] TRAIN_BATCH: ['train_file_0' 'train_file_1' 'train_file_2'] VALID_BATCH : ['train_file_3' 'train_file_4' 'train_file_5'] ------------------------------------------------------------ TRAIN_BATCH: ['train_file_0' 'train_file_1' 'train_file_2'] TRAIN_BATCH: ['train_file_3' 'train_file_4' 'train_file_5'] VALID_BATCH : ['train_file_0' 'train_file_1' 'train_file_2'] ------------------------------------------------------------ TRAIN_BATCH: ['train_file_3' 'train_file_4' 'train_file_5'] in the hopes that it will flush out any aditional training data from the queue so it can make use of the validation data, i get the following output, which shows that it is terminating as soon as it gets through one epoch of training data, and does not get to go through loading evaluation data. Thanks sygi, yes, i prefer to move away from placeholders for the current project.

I am dealing with images files that come in all kinds of shapes and sizes, so i cannot easily import those into a numpy array.

Screenshots: what does not found in block entries mean and when i run steam that pos corrupt file thing runs and when i try to paly cs in steam a black screen comes up and i have to restart my pos comp u know watsup Probably not.

The intended audience for this tutorial is experienced machine learning users interested in using Tensor Flow.

import tensorflow as tf # DATA train_items = ["train_file_".format(i) for i in range(6)] valid_items = ["valid_file_".format(i) for i in range(3)] # SETTINGS batch_size = 3 batches_per_epoch = 2 epochs = 2 # CREATE GRAPH graph = tf. Would i have to create additional coordinators and queue runners as well?

Graph() with graph.as_default(): file_list = tf.placeholder(dtype=tf.string, shape=None) # Create a queue consisting of the strings in `file_list` q = tf.train.string_input_producer(train_items, shuffle=False, num_epochs=None) # Create batch of items. What other approach could i take to switch between training and validation data? data = tf.placeholder(tf.float32, [None, DATA_SHAPE]) for _ in xrange(num_epochs): some_training = read_some_data() sess.run(train_op, feed_dict=) some_testing = read_some_test_data() sess.run(eval_op, feed_dict=) train_filenames = tf.string_input_producer(["training_file"]) train_q = some_reader(train_filenames) test_filenames = tf.string_input_producer(["testing_file"]) test_q = some_reader(test_filenames) am_testing = tf.placeholder(dtype=bool,shape=()) data = tf.cond(am_testing, lambda:test_q, lambda:train_q) train_op, accuracy = model(data) for _ in xrange(num_epochs): sess.run(train_op, feed_dict=) sess.run(accuracy, feed_dict=) The second approach is considered unsafe though -- in this post it is encouraged to build two separate graphs for training and testing (with sharing weights), which is yet another way to achieve what you want.

If you try to run a deprecated version of your server (acquired with HLDSUpdate Tool), you will get one of the following : You can try to update using HLDSUpdate Tool but nothing will change, you will be stuck with your old version, and you will keep getting the same errors.

The key is to get the new TF2 server software, that uses .