Tensorflow train.batch問題

我有一個數據集，每個項目有40個特徵值。當我嘗試使用張量流（我對張量流是新的）構建神經網絡時，這行代碼引發了一個錯誤。Tensorflow train.batch問題

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

錯誤：

ValueError: Dimensions 1 and 40 are not compatible

編輯： 輸入列表由在每個項目包含csv文件讀取特徵數據

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])

作爲40個值來計算一個示例，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

來源

2017-09-01 user5722540

能告訴你input_list'是如何計算'？ – GeertH

@GeertH，我已經更新了這個問題！ – user5722540

您應該首先將輸入列表轉換爲單個數組。您可以提供一張張量/張數列表到，但是每張張將會被分成大小爲40的批次。目前，您正在提供批量大小爲1的張量列表，並且您要求爲這些張量的每個張量創建批量大小40。由於您無法通過1個示例創建40個示例，因此會出現維度不匹配。所以請改爲：

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]

然後您可以使用輸入和標籤來定義您的網絡和丟失功能。例如（只是一個例子，代碼未測試）：

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)

另外請注意，你需要調用只有一次，而不是在一個循環。每次運行運行需要inputs或labels,inputs和labels將評估到不同的批次。

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))

來源

2017-09-01 09:27:43 GeertH

現在它給出的值必須是列表錯誤 – user5722540

對不起，我編輯了我的答案。更新這一行：'批次= tf.train.batch（[input_list]，batch_size = batch_size，enqueue_many = True，容量= 3）' – GeertH

它進入無限循環，並且什麼都不打印:( – user5722540

您可以傳遞input_list作爲張量列表。 tf.train.batch

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

來源

2017-09-01 13:07:13

Tensorflow train.batch問題

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