'int'オブジェクトに属性 '__getitem__'がありませんmxnet

Question

私は 'int'には 'getitem'がありませんが、データ読み込みの作業中にチュートリアルからmxnetを学習しようとしていますが、私はできませんエラーの場所を見つける私を助けてくださいありがとう：

import mxnet as mx 
import numpy as np 

class SimpleData : 
    def __init__(self,data,label,pad = 0): 
     self.data = data 
     self.label = label 
     self.pad = pad 

class SimpleIter: 
     def __init__(self,mean,std,data_shape,label_shape,num_of_classes,num_batch = 10): 
     self._provide_data = zip(['data'],data_shape[0]) 
     self._provide_label = zip(['softmax_label'],label_shape[0]) 
     self.cur_batch = 0 
     self.num_batch = 10 
     self.mean = mean 
     self.std = std 
     self.data_shape = data_shape[0] 
     self.label_shape = label_shape[0] 
     self.num_of_classes = num_of_classes 

    def __iter__(self): 
     return self 

    def __next__(self): 
     return self.next() 

    def reset(self): 
     self.cur_batch = 0 

    @property 
    def provide_data(self): 
     return self._provide_data 

    @property 
    def provide_label(self): 
     return self._provide_label 

    def next(self): 
     if(self.cur_batch < self.num_batch): 
      self.cur_batch += 1 
      data = [mx.nd.array(np.random.normal(self.mean,self.std, ((self.data_shape)[0][0]/self.num_batch,self.data_shape[0][1])))] 
      label = [mx.nd.array(np.random.randint(0,10, ((self.data_shape)[0][1]/self.num_batch)))] 
      print data 
      print label 
      return SimpleBatch(data,label) 
     else: 
      raise StopIteration 

class SyntheticData: 
    def  __init__(self,mean,std,num_records,num_of_features,num_classes): 
     self.mean = mean 
     self.std = std 
     self.data_shape = zip(num_records,num_of_features) 
     self.label_shape = zip(num_records,) 
     self.num_classes = num_classes 

     def get_iter(self): 
      return  SimpleIter(self.mean,self.std,self.data_shape,self.label_shape,self.num_classes) 
net = mx.sym.Variable('data') 
net = mx.sym.FullyConnected(data = net,name = 'fc1',num_hidden = 64) 
net = mx.sym.Activation(data = net,name = 'relu_1',act_type = 'relu') 
net = mx.sym.FullyConnected(data = net,name = 'fc2',num_hidden = 10) 
net = mx.sym.SoftmaxOutput(data = net,name = 'softmax') 
data = SyntheticData(10,128,[100],[100],10) 
mod.fit(data.get_iter(), 
    eval_data=data.get_iter(), 
    optimizer='sgd', 
    optimizer_params={'learning_rate':0.1}, 
    eval_metric='acc', 
    num_epoch = 5) 

にエラーがある：

TypeError         Traceback (most recent call last) 
<ipython-input-273-a7375f022406> in <module>() 
     4   optimizer_params={'learning_rate':0.1}, 
     5   eval_metric='acc', 
----> 6   num_epoch = 5) 

/usr/local/lib/python2.7/dist-packages/mxnet-0.9.4-py2.7.egg/mxnet/module/base_module.pyc in fit(self, train_data, eval_data, eval_metric, epoch_end_callback, batch_end_callback, kvstore, optimizer, optimizer_params, eval_end_callback, eval_batch_end_callback, initializer, arg_params, aux_params, allow_missing, force_rebind, force_init, begin_epoch, num_epoch, validation_metric, monitor) 
    440 
    441   self.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label, 
--> 442     for_training=True, force_rebind=force_rebind) 
    443   if monitor is not None: 
    444    self.install_monitor(monitor) 

/usr/local/lib/python2.7/dist-packages/mxnet-0.9.4-py2.7.egg/mxnet/module/module.pyc in bind(self, data_shapes, label_shapes, for_training, inputs_need_grad, force_rebind, shared_module, grad_req) 
    386              fixed_param_names=self._fixed_param_names, 
    387              grad_req=grad_req, 
--> 388              state_names=self._state_names) 
    389   self._total_exec_bytes = self._exec_group._total_exec_bytes 
    390   if shared_module is not None: 

/usr/local/lib/python2.7/dist-packages/mxnet-0.9.4-py2.7.egg/mxnet/module/executor_group.pyc in __init__(self, symbol, contexts, workload, data_shapes, label_shapes, param_names, for_training, inputs_need_grad, shared_group, logger, fixed_param_names, grad_req, state_names) 
    203        for name in self.symbol.list_outputs()] 
    204 
--> 205   self.bind_exec(data_shapes, label_shapes, shared_group) 
    206 
    207  def decide_slices(self, data_shapes): 

/usr/local/lib/python2.7/dist-packages/mxnet-0.9.4-py2.7.egg/mxnet/module/executor_group.pyc in bind_exec(self, data_shapes, label_shapes, shared_group, reshape) 
    282 
    283   # calculate workload and bind executors 
--> 284   self.data_layouts = self.decide_slices(data_shapes) 
    285   if label_shapes is not None: 
    286    # call it to make sure labels has the same batch size  as data 

/usr/local/lib/python2.7/dist-packages/mxnet-0.9.4- py2.7.egg/mxnet/module/executor_group.pyc in decide_slices(self,  data_shapes) 
     220     continue 
     221 
-->  222    batch_size = shape[axis] 
     223    if self.batch_size is not None: 
     224     assert batch_size == self.batch_size, ("all data  must have the same batch size: " 

TypeError: 'int' object has no attribute '__getitem__' 

Answer 1

私はあなたの問題はあなたのdata_shapeの定義であると思います。

self.data_shape = data_shape[0]

あなたがそれを定義したように、self.data_shapeは単なるint型です。

self.data_shape = data_shape

shape[axis]がdecide_slicesからアクセスされたとき、それは要素の数を得ることができるように：あなたのケースでは、私はそれだけであるべきだと思います。