0
WideAndDeepモデルを実行しようとすると、誰かがこのTensorFlow（1.3）エラーで私を助けることができます。私の正気の限界で。Tensorflowサポートされていないフィードの種類
エラー： tensorflow.python.framework.errors_impl.InternalError：サポートされていないフィードタイプ
フルスタック：私は検索から見てきた唯一のことは、このコードがある
2017-11-09 00:42:06.024911: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Found device 0 with properties:
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 0000:00:1e.0
totalMemory: 11.17GiB freeMemory: 11.10GiB
2017-11-09 00:42:06.024939: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1055] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0, compute capability: 3.7)
Traceback (most recent call last):
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_call
return fn(*args)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1300, in _run_fn
status, run_metadata)
File "/home/centos/anaconda3/lib/python3.6/contextlib.py", line 89, in __exit__
next(self.gen)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 467, in raise_exception_on_not_ok_status
c_api.TF_GetCode(status.status))
tensorflow.python.framework.errors_impl.InternalError: Unsupported feed type
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "BareBonesWideDeep.py", line 152, in <module>
train_and_eval('', 'wide_n_deep', 50,'', '')
File "BareBonesWideDeep.py", line 129, in train_and_eval
m.train(input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True), steps=train_steps)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 255, in train
loss = self._train_model(input_fn=input_fn, hooks=hooks)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 717, in _train_model
_, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 537, in __exit__
self._close_internal(exception_type)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 574, in _close_internal
self._sess.close()
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 820, in close
self._sess.close()
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 941, in close
ignore_live_threads=True)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/coordinator.py", line 389, in join
six.reraise(*self._exc_info_to_raise)
File "/home/centos/anaconda3/lib/python3.6/site-packages/six.py", line 685, in reraise
raise value.with_traceback(tb)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/inputs/queues/feeding_queue_runner.py", line 94, in _run
sess.run(enqueue_op, feed_dict=feed_dict)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 889, in run
run_metadata_ptr)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1118, in _run
feed_dict_tensor, options, run_metadata)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1315, in _do_run
options, run_metadata)
File "/home/centos/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1334, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: Unsupported feed type
Tensorflowのもので、PythonからTF内部へのデータマッピングを行い、 "サポートされていないフィードの種類"をスローします。それはinput_fcnを呼び出す場所スタックトレース内の私のコードの最後がm.trainさ
f_1 float64
f_2 float64
f_3 float64
f_4 float64
f_5 float64
f_6 float64
f_7 float64
f_8 float64
f_9 float64
f_10 float64
f_11 float64
f_12 object
f_13 float64
：
print("Training.....")
print('num_epochs train_and_eval:' + str(train_steps))
m.train(input_fn=input_fn(train_file_name, num_epochs=None,
shuffle=True), steps=train_steps)
def input_fn(data_file, num_epochs, shuffle):
"""Input builder function."""
df_data = pd.read_csv('data.csv')
df_data.fillna(0, inplace = True)
df_data = df_data[df_data['y_actual_bucket']>=-4]
df_data.f_12 = df_data.f_12.astype(str)
df_data['f_12'] = 'a'
print("CSV file data dimensions:",df_data.shape)
print('num_epochs input_fn:' + str(num_epochs))
labels = df_data["y_actual_bucket"]#.apply(lambda x: ">50K" in x).astype(int)
labels = labels+4
df_data = df_data.iloc[:,5:]
train_test_cutoff = int(df_data.shape[0]*.7)
if data_file == 'fake1':
df_data = df_data.iloc[:train_test_cutoff,:]
labels = labels.iloc[:train_test_cutoff]
print("Training data shape:", df_data.shape)
else:
df_data = df_data.iloc[train_test_cutoff:,:]
labels = labels.iloc[train_test_cutoff:]
print("test data shape:", df_data.shape)
print(df_data.dtypes)
return tf.estimator.inputs.pandas_input_fn(
x=df_data,
y=labels,
batch_size=100,
num_epochs=num_epochs,
shuffle=shuffle,
num_threads=1)