Question

我是Tensorflow的新手。我关注了一些在线帖子，并编写了代码以从生成器获取数据。代码如下：

def gen(my_list_of_files):
    for fl in my_list_of_files:
        with open(fl) as f:
            for line in f.readlines():
                json_line = json.loads(line)
                features = json_line['features']
                labels = json_line['labels']
                yield features, labels

def get_dataset():
     generator = lambda: gen()
     return tf.data.Dataset.from_generator(generator, (tf.float32, tf.float32))

def get_input():
     dataset = get_dataset()
     dataset = dataset.shuffle(buffer_size=buffer_size)
     dataset = dataset.repeat().unbatch(tf.contrib.data.unbatch())
     dataset = dataset.batch(batch_size, drop_remainder=False)

     # This is where the problem is
     features, labels = dataset.make_one_shot_iterator().get_next()

     return features, labels

运行此命令时，出现错误：

InvalidArgumentError (see above for traceback): Input element must have a non-scalar value in each component.
     [[node IteratorGetNext (defined at /blah/blah/blah) ]]

我产生的值如下：

[1, 2, 3, 4, 5, 6] # features
7 # label

我对错误的理解是，它不能遍历数据集，因为它不是向量。我的理解正确吗？我该如何解决？

Answer 1

{
   "features": ["1","2"],
   "labels": "2"

}

执行此代码时，我看不到您的错误。

def gen():
    with open('jsondataset') as f:
        data = json.load(f)
        features = data['features']
        labels = data['labels']
        print( features)
        yield features, labels

def get_dataset():
     generator = lambda: gen()
     return tf.data.Dataset.from_generator(generator, (tf.float32, tf.float32))

def get_input():
     dataset = get_dataset()
     dataset = dataset.shuffle(buffer_size=5)
     dataset = dataset.batch(5, drop_remainder=False)

     # This is where the problem is
     iter = dataset.make_one_shot_iterator()
     features, labels = iter.get_next()

     with tf.Session() as sess:
         print(sess.run([features,labels]))


def main():
    get_input()

if __name__ == "__main__":
    main()

[array（[[1。，2.]]，dtype = float32），array（[2。]，dtype = float32）

使用Tensorflow Dataset.from_generator生成的数据在调用iterator.get_next（）时导致错误

1 个答案: