大数据下基于Tensorflow框架的深度学习示例教程

) return lossdef training(loss, learning_rate): tf.summary.scalar(loss.op.name, loss) optimizer = tf.train.GradientDescentOptimizer(learning_rate) global_step = tf.Variable(0, name='global_step', trainable=False) train_op = optimizer.minimize(loss, global_step=global_step) return train_opdef read_and_decode(filename_queue): reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) features = tf.parse_single_example( serialized_example, # Defaults are not specified since both keys are required. features={ 'image_raw': tf.FixedLenFeature([], tf.string), 'label': tf.FixedLenFeature([], tf.int64), }) # Convert from a scalar string tensor (whose single string has # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape # [mnist.IMAGE_PIXELS]. image = tf.decode_raw(features['image_raw'], tf.uint8) image.set_shape([IMAGE_PIXELS]) image = tf.cast(image, tf.float32) * (1. / 255) - 0.5 # Convert label from a scalar uint8 tensor to an int32 scalar. label = tf.cast(features['label'], tf.int32) return image, labeldef inputs(batch_size): """Reads input data. Args: batch_size: Number of examples per returned batch. Returns: A tuple (images, labels), where: * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS] in the range [-0.5, 0.5]. * labels is an int32 tensor with shape [batch_size] with the true label, a number in the range [0, mnist.NUM_CLASSES). """ filename = os.path.join(FLAGS.data_dir, TRAIN_FILE) with tf.name_scope('input'): filename_queue = tf.train.string_input_producer([filename]) # Even when reading in multiple threads, share the filename # queue. image, label = read_and_decode(filename_queue) # Shuffle the examples and collect them into batch_size batches. # (Internally uses a RandomShuffleQueue.) # We run this in two threads to avoid being a bottleneck. images, sparse_labels = tf.train.shuffle_batch( [image, label], batch_size=batch_size, num_threads=2, capacity=1000 + 3 * batch_size, # Ensures a minimum amount of shuffling of examples. min_after_dequeue=1000) return images, sparse_labelsdef device_and_target(): # If FLAGS.job_name is not set, we're running single-machine TensorFlow. # Don't set a device. if FLAGS.job_name is None: raise ValueError("Must specify an explicit `job_name`") # Otherwise we're running distributed TensorFlow. print("Running distributed training") if FLAGS.task_index is None or FLAGS.task_index == "": raise ValueError("Must specify an explicit `task_index`") if FLAGS.ps_hosts is

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