Tensorflow train.batch问题

我有一个数据集，每个项目有40个特征值。当我尝试使用张量流（我对张量流是新的）构建神经网络时，这行代码引发了一个错误。Tensorflow train.batch问题

for _ in range(n_batches): 
      batches = tf.train.batch(input_list, batch_size=batch_size, enqueue_many=True, capacity=3)

错误：

ValueError: Dimensions 1 and 40 are not compatible

编辑： 输入列表由在每个项目包含csv文件读取特征数据

with open('0.csv') as csvfile: 
spamreader = csv.reader(csvfile, delimiter=',',quotechar='|', quoting=csv.QUOTE_MINIMAL) 
for row in spamreader: 
    input_list.append(row) 
    label.append([0,1])

作为40个值来计算一个示例，csv文件如下所示：

-303.49402956575165,122.79163957162679,-11.468865795473208,3.9811327671171512,15.337415109639783,-14.108867251441396,-6.2515380667284548,-2.5776250066394879,-11.151238822575237,0.80064220417869125,0.27982062264574636,-7.4540404067320498,1.1294160621043878,-0.19718258671031155,-4.8998361975682672,1.4096255573297396,-1.7156108756597495,-4.8841327368201828,1.1763404024624535,-1.4828051938843485,-4.4185680752011773,-0.23408318097388262,-1.4313336679963722,-2.350002729608391,-0.012519210688780043,-1.1211450861767087,-0.28546877503470003,1.0960108052925126,0.017672759764874924,2.2723680886768522,2.9337076178657373,0.80627947017775015,2.7373411634266391,2.6117883927402459,-0.17306332146016015,1.3495579191555498,1.2670127235105628,-1.2019882636572772,-0.19807357792275704,-0.11667725657652298 
-324.95895982872742,129.16902362793437,-8.5782578426411202,4.8909691557978645,9.679460715739177,-21.516263281123845,-8.0416220828154454,-3.8078900557142812,-13.927945903788769,0.43952636061160394,-0.69085228125901854,-9.051802560349115,3.2384649283450346,0.51938767915475448,-7.6485369361057103,2.1827631457774346,-0.2975737631792475,-8.3214260824025619,-0.52396570369004059,1.1065859071072002,-2.3500738274356539,4.2447931265345034,7.879566058882304,6.0660506076869103,6.012983546020755,4.4649038449901903,2.1070954443797651,-0.26862183717859767,-1.495853591930427,0.52413729310912371,-0.85169785225746941,-3.675692955742599,-1.2819459279342635,-1.3243977571633176,-3.4214154132554886,-1.025319003875736,-1.5668864629221912,-4.3026170282653107,-1.9139061426068631,-0.64944140674848683

来源

2017-09-01 user5722540

能告诉你input_list'是如何计算'？ – GeertH

@GeertH，我已经更新了这个问题！ – user5722540

您应该首先将输入列表转换为单个数组。您可以提供一张张量/张数列表到，但是每张张将会被分成大小为40的批次。目前，您正在提供批量大小为1的张量列表，并且您要求为这些张量的每个张量创建批量大小40。由于您无法通过1个示例创建40个示例，因此会出现维度不匹配。所以请改为：

import numpy as np input_list = np.array(input_list) labels = np.array(labels) batch = tf.train.batch([input_list, labels], batch_size=batch_size, enqueue_many=True, capacity=3) inputs = batch[0] labels = batch[1]

然后您可以使用输入和标签来定义您的网络和丢失功能。例如（只是一个例子，代码未测试）：

hidden = tf.layers.dense(inputs, 2) loss = tf.softmax_cross_entropy_with_logits(labels=labels, logits=hidden) optimizer = tf.train.AdamOptimizer() train_op = optimizer.minimize(loss)

另外请注意，你需要调用只有一次，而不是在一个循环。每次运行运行需要inputs或labels,inputs和labels将评估到不同的批次。

with tf.Session() as sess: with tf.contrib.slim.queues.QueueRunners(sess): for i in range(n_batches): _, loss_value = sess.run([train_op, loss]) print("Loss for batch {}: {}".format(i, loss_value))

来源

2017-09-01 09:27:43 GeertH

现在它给出的值必须是列表错误 – user5722540

对不起，我编辑了我的答案。更新这一行：'批次= tf.train.batch（[input_list]，batch_size = batch_size，enqueue_many = True，容量= 3）' – GeertH

它进入无限循环，并且什么都不打印:( – user5722540

您可以传递input_list作为张量列表。 tf.train.batch

for _ in range(n_batches): 
    batches = tf.train.batch([input_list], batch_size=batch_size, 
      enqueue_many=True, capacity=3)

来源

2017-09-01 13:07:13

Tensorflow train.batch问题

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