Tensorflow、Estimator（model_fn）でプレースホルダを供給していますか？

Question

私はlstmモデルを構築しようとしています。

    classificator = tf.contrib.learn.Estimator(model_fn=lstm_model(timesteps,rnn_layers,dense_layers)) 
    validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(X_train_TF, Y_train, 
                 every_n_steps=1000, early_stopping_rounds = 1000) 
    classificator.fit(X_train_TF, Y_train, monitors = [validation_monitor], 
         batch_size = batch_size, steps = training_steps) 

あり、私のモデル定義（少し視覚的なバグがあるが、すべての機能がにあります。しかし、私はここに私のノートPCのコードは、エラーが発生した私は、三行目（フィット分類器）を呼び出すとき

     InvalidArgumentError Traceback (most recent call last) 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args) 
     964  try: 
    --> 965  return fn(*args) 
     966  except errors.OpError as e: 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata) 
     946         feed_dict, fetch_list, target_list, 
    --> 947         status, run_metadata) 
     948 
    /home/george/anaconda3/lib/python3.5/contextlib.py in exit(self, type, value, traceback) 
     65    try: 
    ---> 66     next(self.gen) 
     67    except StopIteration: 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/errors.py in raise_exception_on_not_ok_status() 
     449   compat.as_text(pywrap_tensorflow.TF_Message(status)), 
    --> 450   pywrap_tensorflow.TF_GetCode(status)) 
     451 finally: 
    InvalidArgumentError: You must feed a value for placeholder tensor 'input' with dtype float 
     [[Node: input = Placeholderdtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]] 
    During handling of the above exception, another exception occurred: 
    InvalidArgumentError      Traceback (most recent call last) 
    in() 
      1 classificator.fit(X_train_TF, Y_train, monitors = [validation_monitor], 
    ----> 2     batch_size = batch_size, steps = training_steps) 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py in fit(self, x, y, input_fn, steps, batch_size, monitors, max_steps) 
     217        steps=steps, 
     218        monitors=monitors, 
    --> 219        max_steps=max_steps) 
     220  logging.info('Loss for final step: %s.', loss) 
     221  return self 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py in _train_model(self, input_fn, steps, feed_fn, init_op, init_feed_fn, init_fn, device_fn, monitors, log_every_steps, fail_on_nan_loss, max_steps) 
     477  features, targets = input_fn() 
     478  self._check_inputs(features, targets) 
    --> 479  train_op, loss_op = self._get_train_ops(features, targets) 
     480 
     481  # Add default monitors. 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py in _get_train_ops(self, features, targets) 
     747  Tuple of train Operation and loss Tensor. 
     748  """ 
    --> 749  _, loss, train_op = self._call_model_fn(features, targets, ModeKeys.TRAIN) 
     750  return train_op, loss 
     751 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py in _call_model_fn(self, features, targets, mode) 
     731  else: 
     732   return self._model_fn(features, targets, mode=mode) 
    --> 733  return self._model_fn(features, targets) 
     734 
     735 def _get_train_ops(self, features, targets): 
    /home/george/ipython/project/lstm_model.py in model(X, y) 
     61   output = lstm_layers(output[-1],dense_layers) 
     62   prediction, loss = tflearn.run_n({"outputs": output, "last_states": layers}, n=1, 
    ---> 63           feed_dict=None) 
     64   train_operation = tflayers.optimize_loss(loss, tf.contrib.framework.get_global_step(), optimizer=optimizer, 
     65             learning_rate=learning_rate) 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/graph_actions.py in run_n(output_dict, feed_dict, restore_checkpoint_path, n) 
     795  output_dict=output_dict, 
     796  feed_dicts=itertools.repeat(feed_dict, n), 
    --> 797  restore_checkpoint_path=restore_checkpoint_path) 
     798 
     799 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/graph_actions.py in run_feeds(*args, **kwargs) 
     850 def run_feeds(*args, **kwargs): 
     851 """See run_feeds_iter(). Returns a list instead of an iterator.""" 
    --> 852 return list(run_feeds_iter(*args, **kwargs)) 
     853 
     854 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/graph_actions.py in run_feeds_iter(output_dict, feed_dicts, restore_checkpoint_path) 
     841   threads = queue_runner.start_queue_runners(session, coord=coord) 
     842   for f in feed_dicts: 
    --> 843   yield session.run(output_dict, f) 
     844  finally: 
     845   coord.request_stop() 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata) 
     708  try: 
     709  result = self._run(None, fetches, feed_dict, options_ptr, 
    --> 710       run_metadata_ptr) 
     711  if run_metadata: 
     712   proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 
     906  if final_fetches or final_targets: 
     907  results = self._do_run(handle, final_targets, final_fetches, 
    --> 908        feed_dict_string, options, run_metadata) 
     909  else: 
     910  results = [] 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata) 
     956  if handle is None: 
     957  return self._do_call(_run_fn, self._session, feed_dict, fetch_list, 
    --> 958       target_list, options, run_metadata) 
     959  else: 
     960  return self._do_call(_prun_fn, self._session, handle, feed_dict, 
    /home/george/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args) 
     976   except KeyError: 
     977   pass 
    --> 978  raise type(e)(node_def, op, message) 
     979 
     980 def _extend_graph(self):
 

InvalidArgumentError: You must feed a value for placeholder tensor 'input' with dtype float 
    [[Node: input = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 
 

を取得していますlstm_model関数）：

 

def lstm_model(num_utnits, rnn_layers, dense_layers=None, learning_rate=0.1, optimizer='Adagrad'): 
    def lstm_cells(layers): 
     if isinstance(layers[0], dict): 
      return [tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.LSTMCell(layer['num_units'], 
                      state_is_tuple=True), 
                layer['keep_prob']) 
        if layer.get('keep_prob') else tf.nn.rnn_cell.LSTMCell(layer['num_units'], 
                      state_is_tuple=True) 
        for layer in layers] 
     return [tf.nn.rnn_cell.LSTMCell(steps, state_is_tuple=True) for steps in layers] 

    def lstm_layers(input_layers, layers): 
     if layers and isinstance(layers, dict): 
      return tflayers.stack(input_layers, tflayers.fully_connected, 
            layers['layers']) # check later 
     elif layers: 
      return tflayers.stack(input_layers, tflayers.fully_connected, layers) 
     else: 
      return input_layers 

    def model(X, y): 
     stacked_lstm = tf.nn.rnn_cell.MultiRNNCell(lstm_cells(rnn_layers), state_is_tuple=True) 
     output, layers = tf.nn.dynamic_rnn(cell=stacked_lstm, inputs=X, dtype=dtypes.float32,) 
     output = lstm_layers(output[-1], dense_layers) 
     prediction, loss = tflearn.run_n({"outputs": output, "last_states": layers}, n=1, 
             feed_dict=None) 
     train_operation = tflayers.optimize_loss(loss, tf.contrib.framework.get_global_step(), optimizer=optimizer, 
               learning_rate=learning_rate) 
     return prediction, loss, train_operation 

    return model 
 

どうしたのですか？どのようにこの問題を解決するための任意の提案？とにかく、将来の提案をしてくれてありがとう）

Answer 1

は.fitメソッドは、引数として、代わりにトレーニングデータとそのラベルのinput_fnをサポートするように変更されました。 thisの例をご覧ください。