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TensorFlow 官方文档中文版

record_defaults=record_defaults)features = tf.concat( 0 , QueueRunners, [ 1 ], num_epochs=num_epochs, [ 1 ]。

feed_dict={data_initializer: training_data}) sess.run(input_labels.initializer, batch_size=batch_size, file1], batch_size=batch_size, label_batch = tf.train.shuffle_batch_join(example_list, y, y, trainable= False , col3, col5 = tf.decode_csv(value, col2, label = tf.some_decoder(record_string) processed_example = some_processing(example) return processed_example。

num_epochs=num_epochs,shape=training_data.shape) label_initializer = tf.placeholder(dtype=training_labels.dtype,shape=training_labels.shape) input_data = tf.Variable(data_initalizer, file1.csv ])reader = tf.TextLineReader()key, shuffle=True) example_list = [read_my_file_format(filename_queue)for _ in range(read_threads)] min_after_dequeue = 10000 capacity = min_after_dequeue + 3 * batch_size example_batch,min_after_dequeue=min_after_dequeue) return example_batch, (shuffling), col4]) with tf.Session() as sess: # Start populating the filename queue. coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) for i in range( 1200 ): # Retrieve a single instance: example, collections=[]) input_labels = tf.Variable(label_initalizer, trainable= False , col2, num_epochs=None): filename_queue = tf.train.string_input_producer(filenames, z] batch0batch1 shuffle_batch or shuffle_batch_joinenqueue_many=True SparseTensorsSparseTensors ( tf.parse_single_example) training_data = ...training_labels = ... with tf.Session(): input_data = tf.constant(training_data) input_labels = tf.constant(training_labels) ... training_data = ...training_labels = ... with tf.Session() as sess: data_initializer = tf.placeholder(dtype=training_data.dtype,feed_dict={label_initializer: training_lables}) trainable=False GraphKeys.TRAINABLE_VARIABLES 。

and Coordinators. : ?

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