tensorflow入门:tfrecord 和tf.data.TFRecordDataset的使用

今天小编就为大家分享一篇tensorflow入门:tfrecord 和tf.data.TFRecordDataset的使用，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

1.创建tfrecord

tfrecord支持写入三种格式的数据：string，int64，float32，以列表的形式分别通过tf.train.BytesList、tf.train.Int64List、tf.train.FloatList写入tf.train.Feature，如下所示：

tf.train.Feature(bytes_list=tf.train.BytesList(value=[feature.tostring()])) #feature一般是多维数组，要先转为list tf.train.Feature(int64_list=tf.train.Int64List(value=list(feature.shape))) #tostring函数后feature的形状信息会丢失，把shape也写入 tf.train.Feature(float_list=tf.train.FloatList(value=[label]))

通过上述操作，以dict的形式把要写入的数据汇总，并构建tf.train.Features，然后构建tf.train.Example，如下：

def get_tfrecords_example(feature, label): tfrecords_features = {} feat_shape = feature.shape tfrecords_features['feature'] = tf.train.Feature(bytes_list=tf.train.BytesList(value=[feature.tostring()])) tfrecords_features['shape'] = tf.train.Feature(int64_list=tf.train.Int64List(value=list(feat_shape))) tfrecords_features['label'] = tf.train.Feature(float_list=tf.train.FloatList(value=label)) return tf.train.Example(features=tf.train.Features(feature=tfrecords_features))

把创建的tf.train.Example序列化下，便可通过tf.python_io.TFRecordWriter写入tfrecord文件，如下：

tfrecord_wrt = tf.python_io.TFRecordWriter('xxx.tfrecord') #创建tfrecord的writer，文件名为xxx exmp = get_tfrecords_example(feats[inx], labels[inx]) #把数据写入Example exmp_serial = exmp.SerializetoString() #Example序列化 tfrecord_wrt.write(exmp_serial) #写入tfrecord文件 tfrecord_wrt.close() #写完后关闭tfrecord的writer

代码汇总：

import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets mnist = read_data_sets("MNIST_data/", one_hot=True) #把数据写入Example def get_tfrecords_example(feature, label): tfrecords_features = {} feat_shape = feature.shape tfrecords_features['feature'] = tf.train.Feature(bytes_list=tf.train.BytesList(value=[feature.tostring()])) tfrecords_features['shape'] = tf.train.Feature(int64_list=tf.train.Int64List(value=list(feat_shape))) tfrecords_features['label'] = tf.train.Feature(float_list=tf.train.FloatList(value=label)) return tf.train.Example(features=tf.train.Features(feature=tfrecords_features)) #把所有数据写入tfrecord文件 def make_tfrecord(data, outf_nm='mnist-train'): feats, labels = data outf_nm += '.tfrecord' tfrecord_wrt = tf.python_io.TFRecordWriter(outf_nm) ndatas = len(labels) for inx in range(ndatas): exmp = get_tfrecords_example(feats[inx], labels[inx]) exmp_serial = exmp.SerializetoString() tfrecord_wrt.write(exmp_serial) tfrecord_wrt.close() import random nDatas = len(mnist.train.labels) inx_lst = range(nDatas) random.shuffle(inx_lst) random.shuffle(inx_lst) ntrains = int(0.85*nDatas) # make training set data = ([mnist.train.images[i] for i in inx_lst[:ntrains]], [mnist.train.labels[i] for i in inx_lst[:ntrains]]) make_tfrecord(data, outf_nm='mnist-train') # make validation set data = ([mnist.train.images[i] for i in inx_lst[ntrains:]], [mnist.train.labels[i] for i in inx_lst[ntrains:]]) make_tfrecord(data, outf_nm='mnist-val') # make test set data = (mnist.test.images, mnist.test.labels) make_tfrecord(data, outf_nm='mnist-test')

2.tfrecord文件的使用：tf.data.TFRecordDataset

从tfrecord文件创建TFRecordDataset：

dataset = tf.data.TFRecordDataset('xxx.tfrecord')

解析tfrecord文件的每条记录，即序列化后的tf.train.Example；使用tf.parse_single_example来解析：

feats = tf.parse_single_example(serial_exmp, features=data_dict)

其中，data_dict是一个dict，包含的key是写入tfrecord文件时用的key，相应的value则是tf.FixedLenFeature([], tf.string)、tf.FixedLenFeature([], tf.int64)、tf.FixedLenFeature([], tf.float32)，分别对应不同的数据类型，汇总即有：

def parse_exmp(serial_exmp): #label中[10]是因为一个label是一个有10个元素的列表，shape中的[x]为shape的长度 feats = tf.parse_single_example(serial_exmp, features={'feature':tf.FixedLenFeature([], tf.string), 'label':tf.FixedLenFeature([10],tf.float32), 'shape':tf.FixedLenFeature([x], tf.int64)}) image = tf.decode_raw(feats['feature'], tf.float32) label = feats['label'] shape = tf.cast(feats['shape'], tf.int32) return image, label, shape

解析tfrecord文件中的所有记录，使用dataset的map方法，如下：

dataset = dataset.map(parse_exmp)

map方法可以接受任意函数以对dataset中的数据进行处理；另外，可使用repeat、shuffle、batch方法对dataset进行重复、混洗、分批；用repeat复制dataset以进行多个epoch；如下：

dataset = dataset.repeat(epochs).shuffle(buffer_size).batch(batch_size)

解析完数据后，便可以取出数据进行使用，通过创建iterator来进行，如下：

iterator = dataset.make_one_shot_iterator() batch_image, batch_label, batch_shape = iterator.get_next()

要把不同dataset的数据Feed进行模型，则需要先创建iterator handle，即iterator placeholder，如下：

handle = tf.placeholder(tf.string, shape=[]) iterator = tf.data.Iterator.from_string_handle(handle, dataset_train.output_types, dataset_train.output_shapes) image, label, shape = iterator.get_next()

然后为各个dataset创建handle，以Feed_dict传入placeholder，如下：

with tf.Session() as sess: handle_train, handle_val, handle_test = sess.run( [x.string_handle() for x in [iter_train, iter_val, iter_test]]) sess.run([loss, train_op], Feed_dict={handle: handle_train}

汇总：

3.mnist实验

import tensorflow as tf train_f, val_f, test_f = ['mnist-%s.tfrecord'%i for i in ['train', 'val', 'test']] def parse_exmp(serial_exmp): feats = tf.parse_single_example(serial_exmp, features={'feature':tf.FixedLenFeature([], tf.string), 'label':tf.FixedLenFeature([10],tf.float32), 'shape':tf.FixedLenFeature([], tf.int64)}) image = tf.decode_raw(feats['feature'], tf.float32) label = feats['label'] shape = tf.cast(feats['shape'], tf.int32) return image, label, shape def get_dataset(fname): dataset = tf.data.TFRecordDataset(fname) return dataset.map(parse_exmp) # use padded_batch method if padding needed epochs = 16 batch_size = 50 # when batch_size can't be divided by nDatas, like 56, # there will be a batch data with nums less than batch_size # training dataset nDatasTrain = 46750 dataset_train = get_dataset(train_f) dataset_train = dataset_train.repeat(epochs).shuffle(1000).batch(batch_size) # make sure repeat is ahead batch # this is different from dataset.shuffle(1000).batch(batch_size).repeat(epochs) # the latter means that there will be a batch data with nums less than batch_size for each epoch # if when batch_size can't be divided by nDatas. nBatchs = nDatasTrain*epochs//batch_size # evalation dataset nDatasVal = 8250 dataset_val = get_dataset(val_f) dataset_val = dataset_val.batch(nDatasVal).repeat(nBatchs//100*2) # test dataset nDatasTest = 10000 dataset_test = get_dataset(test_f) dataset_test = dataset_test.batch(nDatasTest) # make dataset iterator iter_train = dataset_train.make_one_shot_iterator() iter_val = dataset_val.make_one_shot_iterator() iter_test = dataset_test.make_one_shot_iterator() # make Feedable iterator, i.e. iterator placeholder handle = tf.placeholder(tf.string, shape=[]) iterator = tf.data.Iterator.from_string_handle(handle, dataset_train.output_types, dataset_train.output_shapes) x, y_, _ = iterator.get_next() # cnn x_image = tf.reshape(x, [-1,28,28,1]) w_init = tf.truncated_normal_initializer(stddev=0.1, seed=9) b_init = tf.constant_initializer(0.1) cnn1 = tf.layers.conv2d(x_image, 32, (5,5), padding='same', activation=tf.nn.relu, kernel_initializer=w_init, bias_initializer=b_init) mxpl1 = tf.layers.max_pooling2d(cnn1, 2, strides=2, padding='same') cnn2 = tf.layers.conv2d(mxpl1, 64, (5,5), padding='same', activation=tf.nn.relu, kernel_initializer=w_init, bias_initializer=b_init) mxpl2 = tf.layers.max_pooling2d(cnn2, 2, strides=2, padding='same') mxpl2_flat = tf.reshape(mxpl2, [-1,7*7*64]) fc1 = tf.layers.dense(mxpl2_flat, 1024, activation=tf.nn.relu, kernel_initializer=w_init, bias_initializer=b_init) keep_prob = tf.placeholder('float') fc1_drop = tf.nn.dropout(fc1, keep_prob) logits = tf.layers.dense(fc1_drop, 10, kernel_initializer=w_init, bias_initializer=b_init) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_)) optmz = tf.train.AdamOptimizer(1e-4) train_op = optmz.minimize(loss) def get_eval_op(logits, labels): corr_prd = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1)) return tf.reduce_mean(tf.cast(corr_prd, 'float')) eval_op = get_eval_op(logits, y_) init = tf.initialize_all_variables() # summary logdir = './logs/m4d2a' def summary_op(datapart='train'): tf.summary.scalar(datapart + '-loss', loss) tf.summary.scalar(datapart + '-eval', eval_op) return tf.summary.merge_all() summary_op_train = summary_op() summary_op_val = summary_op('val') # whether to restore or not ckpts_dir = 'ckpts/' ckpt_nm = 'cnn-ckpt' saver = tf.train.Saver(max_to_keep=50) # defaults to save all variables, using dict {'x':x,...} to save specified ones. restore_step = '' start_step = 0 train_steps = nBatchs best_loss = 1e6 best_step = 0 # import os # os.environ["CUDA_VISIBLE_DEVICES"] = "0" # config = tf.ConfigProto() # config.gpu_options.per_process_gpu_memory_fraction = 0.9 # config.gpu_options.allow_growth=True # allocate when needed # with tf.Session(config=config) as sess: with tf.Session() as sess: sess.run(init) handle_train, handle_val, handle_test = sess.run( [x.string_handle() for x in [iter_train, iter_val, iter_test]]) if restore_step: ckpt = tf.train.get_checkpoint_state(ckpts_dir) if ckpt and ckpt.model_checkpoint_path: # ckpt.model_checkpoint_path means the latest ckpt if restore_step == 'latest': ckpt_f = tf.train.latest_checkpoint(ckpts_dir) start_step = int(ckpt_f.split('-')[-1]) + 1 else: ckpt_f = ckpts_dir+ckpt_nm+'-'+restore_step print('loading wgt file: '+ ckpt_f) saver.restore(sess, ckpt_f) summary_wrt = tf.summary.FileWriter(logdir,sess.graph) if restore_step in ['', 'latest']: for i in range(start_step, train_steps): _, cur_loss, cur_train_eval, summary = sess.run([train_op, loss, eval_op, summary_op_train], Feed_dict={handle: handle_train, keep_prob: 0.5} ) # log to stdout and eval validation set if i % 100 == 0 or i == train_steps-1: saver.save(sess, ckpts_dir+ckpt_nm, global_step=i) # save variables summary_wrt.add_summary(summary, global_step=i) cur_val_loss, cur_val_eval, summary = sess.run([loss, eval_op, summary_op_val], Feed_dict={handle: handle_val, keep_prob: 1.0}) if cur_val_loss

实验结果：

以上这篇tensorflow入门:tfrecord 和tf.data.TFRecordDataset的使用就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持编程之家。

tensorflow入门:tfrecord 和tf.data.TFRecordDataset的使用

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