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我正在嘗試構建lstm模型。但我發現Tensorflow，帶有Estimator（model_fn）的佔位符？
這裏是我的筆記本電腦的代碼，就會發生錯誤，當我打電話第三行（FIT分類）：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 trainOperationand lossTensor. 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 alistinstead 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"]()]]
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)
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