我正在尝试构建一个lstm模型 . 但我得到了
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"]()]]
这是我的笔记本代码，当我调用第三行（适合分类器）时发生错误：
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)
有我的模型定义（一点视觉错误，但所有函数都在lstm_model函数中）： ``
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
我做错了什么？有任何建议如何解决这个问题？非常感谢未来的建议）
1 回答
已修改 .fit 方法以支持input_fn作为参数，而不是训练数据及其标签 . 请看this示例 .