Question

我是TensorFlow的新手，我正在使用多个CSV文件作为输入，根据Sachin Joglekar的博客（https://codesachin.wordpress.com/2015/11/28/self-organizing-maps-with-googles-tensorflow/）培训SOM模型。我按照https://www.tensorflow.org/programmers_guide/reading_data上的教程来读取小批量中的CSV文件队列。我的代码正在运行，但我想从阅读器打印出解码的CSV输入，以验证输入管道是否正常工作。由于CSV文件输入不是图表的一部分，我无法使用Tensor.eval（self.sess）打印它。当我尝试使用self.label.eval（session = tf.Session（graph = self.label.graph））打印出解码的记录标签时，我的脚本挂起并且不提供任何输出。有没有办法让我验证我的输入管道是否正常工作？以下是我的代码的相关摘要：

主要功能

def main(argv):
    som = SOM(somDim1, somDim2, windowSizes[win], iterations, learningRate, 
    neighborhood, fileNameList, batchSize)
    som.train(batchSize, fileNameList, windowSizes[win])

图形

def __init__(self, m, n, dim, iterations, alpha, sigma, fileNameList, batchSize):

    ##INITIALIZE GRAPH
    self.graph = tf.Graph()

    ##POPULATE GRAPH WITH NECESSARY COMPONENTS
    with self.graph.as_default():

        ##PLACEHOLDERS FOR TRAINING INPUTS
        #These should be placeholders according to the TensorFlow framework,
        #but we are declaring them as variables so that we can assign them directly
        #to values read in from the CSV files.
        batchInputLg = np.zeros((dim, batchSize))
        labelFloat = np.zeros((3, batchSize))
        self.label = tf.cast(labelFloat, "string")
        self.batchInput = tf.cast(batchInputLg, "float32")

        """
        ...the rest of the graph...
        """

        self.trainingOp = tf.assign(self.weightageVects, newWeightagesOp)

        ##INITIALIZE SESSION
        self.sess = tf.Session()

        ##INITIALIZE VARIABLES
        initOp = tf.global_variables_initializer()
        self.sess.run(initOp)

输入管道功能

"""
Read in the features and metadata from the CSV files for each chromosome.
"""
def readFromCsv(self, fileNameQ, dim):
    reader = tf.TextLineReader()
    _, csvLine = reader.read(fileNameQ)
    recordDefaults = [["\0"] for cl in range(dim - 1)]
    recordStr = tf.decode_csv(csvLine, record_defaults=recordDefaults)
    self.label = tf.stack(recordStr[0:2])
    #self.label.eval(session = tf.Session(graph=self.label.graph))
    self.features = tf.to_float(tf.stack(recordStr[3:dim - 1]))
    return (self.features, self.label)

"""
Read in the features and metadata from the CSV files for each chromosome.
"""
def inputPipeline(self, batchSize, fileNameList, dim, num_epochs=None):
    fileNameQ = tf.train.string_input_producer(fileNameList, shuffle = True)  
    minAfterDequeue = 10000
    capacity = minAfterDequeue + 3 * batchSize
    example, label = self.readFromCsv(fileNameQ, dim)
    exampleBatchStr, labelBatch = tf.train.shuffle_batch([example, label], batch_size=batchSize, capacity=capacity, min_after_dequeue=minAfterDequeue)
    exampleBatch = tf.cast(exampleBatchStr, "float")
    return (exampleBatch, labelBatch)

培训功能

def train(self, batchSize, fileNameList, dim):
    #Start the queue runners.
    # Start input enqueue threads.
    coordFile = tf.train.Coordinator()
    self.coord = tf.train.Coordinator()
    threadsFile = tf.train.start_queue_runners(sess=self.sess, coord=coordFile)
    self.threads = tf.train.start_queue_runners(sess=self.sess, coord=self.coord)

    #Training iterations
    self.iterationInput = 0
    try:
        for iter in range(self.iterations):
            #Train with each vector one by one
            self.iterationInput += 1
            while not self.coord.should_stop():
                #Fill in input data.
                [self.batchInput, self.label] = self.inputPipeline(batchSize, fileNameList, dim)
                self.sess.run(self.trainingOp)

    except tf.errors.OutOfRangeError:
        print('Done training -- epoch limit reached')

    # When done, ask the threads to stop.
    self.coord.request_stop()

Answer 1

我找到了解决方案。我没有在图表中初始化标签和批量输入张量并在train（）函数中分配它们，我应该将赋值语句放在图形中，如下所示：

##TRAINING INPUTS
self.batchInput, self.label = self.inputPipeline(batchSize, fileNameList, dim)

然后，列车功能变为：

def train(self, batchSize, fileNameList, dim):
    with self.sess:
        # Start populating the filename queue.
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)

        #Training iterations
        self.iterationInput = 0
        try:
            for iter in range(self.iterations):
                #Train with each vector one by one
                self.iterationInput += 1
                while not coord.should_stop():
                    #Fill in input data.
                    self.sess.run([self.batchInput, self.label])
                    self.sess.run(self.trainingOp)
                    print self.label.eval(session = self.sess)
        except tf.errors.OutOfRangeError:
            print('Done training -- epoch limit reached')

        # When done, ask the threads to stop.
        coord.request_stop()
        coord.join(threads)

对CSV文件队列的TensorFlow输入管道进行故障排除

1 个答案: