拟合训练部分的张量森林估计值误差

代码：从sklearn导入cross_validation作为cv import numpy as np from tensorflow.contrib.learn.python.learn.estimators import estimator from tensorflow.contrib.tensor_forest.python import tensor_forest

X = np.array([[  74.,  166.,  331.,  161.,  159.,  181.,  180.],
   [ 437.,  427.,  371.,  361.,  393.,  465.,  464.],
   [ 546.,  564.,  588.,  595.,  536.,  537.,  520.],
   [  89.,   89.,   87.,   87.,  108.,  113.,  111.],
   [  75.,   90.,   74.,   89.,  130.,  140.,  135.]])
Y = np.array([[ 51.,  43.,  29.,  43.,  43.,  41.,  42.],
   [ 22.,  23.,  26.,  27.,  25.,  19.,  19.],
   [  7.,   7.,   5.,   5.,   9.,   8.,  10.],
   [ 55.,  54.,  55.,  53.,  51.,  51.,  51.],
   [ 58.,  57.,  57.,  58.,  55.,  55.,  55.]])
train_X, test_X, train_Y, test_Y = cv.train_test_split(X, Y, 
test_size=0.50, random_state=42)

def build_estimator() :
 params = tensor_forest.ForestHParams(num_classes=7, num_features=7, 
 num_trees=30, max_nodes=100)
 graph_builder_class = tensor_forest.RandomForestGraphs

 graph_builder_class = tensor_forest.TrainingLossForest
 return estimator.SKCompat(random_forest.TensorForestEstimator(
   params, graph_builder_class=graph_builder_class,
   model_dir=None))
est = build_estimator() 
train_X = train_X.astype(dtype = np.float32) 
train_Y = train_Y.astype(dtype = np.float32) 
est = est.fit(x=train_X, y=train_Y, batch_size = 100)

我的输入和输出形状都是[Number_of_samples，7] . 它完美地运行scikitlearn随机森林分类器 . 但是对于学习，我在拟合估算器时会出现以下错误：

INFO：tensorflow：使用params构建林= INFO：tensorflow：{'valid_leaf_threshold'：1，'split_after_samples'：250，'num_output_columns'：8，'feature_bagging_fraction'：1.0，'split_initializations_per_input'：1，'bagged_features'：无，'min_split_samples'：5，'max_nodes'：100，'num_features'：7，'num_trees'：30，'num_splits_to_consider'：7，'base_random_seed'：0，'num_outputs'：1，'dominate_fraction'：0.99，' max_fertile_nodes'：50，'bagged_num_features'：7，'dominate_method'：'bootstrap'，'bagging_fraction'：1.0，'regression'：False，'num_classes'：7} ValueErrorTraceback（最近一次调用最后一次）in（）--- - > 1 est = \ test.fit（x = train_X，y = train_Y，batch_size = 100）/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator . 适合的pyc（self，x，y，batch_size，steps，max_steps，monitors）1351步骤=步骤，1352 max_steps = max_steps， - > 1353 monitors = all_monitors）1354 return self 1355 /usr/local/lib/python2.7/ DIST-包/ tensorflow /的contrib /张扭适合的r_forest / client / random_forest.pyc（self，x，y，input_fn，steps，batch_size，monitors，max_steps）262 elif输入不是None：263 self._estimator.fit（input_fn = input_fn，steps = steps，monitors =监视器， - > 264 max_steps = max_steps）265 else：266引发ValueError（'适合：必须同时提供x和y或input_fn . '）/ usr / local / lib / python2.7 / decode-packages/tensorflow / python new_func中的/util/deprecation.pyc（* args，** kwargs）278 _call_location（），decorator_utils.get_qualified_name（func），279 func.module，arg_name，date，instructions） - > 280 return func（* args，* * kwargs）281 new_func.doc = _add_deprecated_arg_notice_to_docstring（282 func.doc，date，instructions）/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.pyc in fit（self，x，y，input_fn，steps，batch_size，monitors，max_steps）424 hooks.append（basic_session_run_hooks.StopAtStepHook（steps，max_steps））425 - > 426 loss = self._train_model（input_fn = input_fn，hooks = hooks ）427 logging.info （'最后一步的损失：％s . '，损失）428在_train_model中返回自我/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.pyc（ self，input_fn，hooks）932 features，labels = input_fn（）933 self._check_inputs（features，labels） - > 934 model_fn_ops = self._call_legacy_get_train_ops（features，labels）935 ops.add_to_collection（ops.GraphKeys.LOSSES，model_fn_ops . 丢失）936 all_hooks.extend（[/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.pyc in _call_legacy_get_train_ops（self，features，labels）1001 1002 def _call_legacy_get_train_ops（self，features，labels）： - > 1003 train_ops = self._get_train_ops（features，labels）1004 if isinstance（train_ops，model_fn_lib.ModelFnOps）：＃Default signature 1005 return train_ops /usr/local/lib/python2.7/ _get_train_ops中的dist-packages / tensorflow / contrib / learn / python / learn / estimators / estimator.pyc（self，features，labels）1160 ModelFnOps对象 . 1161“”“ - > 1162返回self._call_model_fn（features，labels，model_fn_lib.ModeKeys.TRAIN）1163 1164 def _get_eval_ops（self，features，labels，metrics）：/usr/local/lib/python2.7/dist-packages _call_model_fn中的/tensorflow/contrib/learn/python/learn/estimators/estimator.pyc（自我，功能，标签，模式）1131如果model_fn_args中的'model_dir'：1132 kwargs ['model_dir'] = self.model_dir - > 1133 model_fn_results = self._model_fn（features，labels，** kwargs）1134 1135 if isinstance（model_fn_results，model_fn_lib.ModelFnOps）：/ usr / local / lib / python2.7 / dad-packages /tensorflow / contrib /tensor_forest / client / random_forest . pyc in _model_fn（features，labels）128如果标签不是None：129 training_loss = graph_builder.training_loss（ - > 130个features，labels，name = LOSS_NAME）131 training_graph = control_flow_ops.group（132 graph_builder.training_graph（/ usr / local） training_loss中的/lib/python2.7/dist-packages/tensorflow/contrib/tensor_forest/python/tensor_forest.pyc（自我，功能，标签，名称）559 560 de f training_loss（self，features，labels，name ='training_loss'）： - > 561 return array_ops.identity（self._get_loss（features，labels），name = name）562 563 /usr/local/lib/python2.7/dist-packages/tensorflow/ _get_loss中的contrib / tensor_forest / python / tensor_forest.pyc（self，features，labels）548 self._loss = control_flow_ops.cond（549 self.average_size（）> 0，_average_loss， - > 550 lambda：constant_op.constant（sys . maxsize，dtype = dtypes.float32））551 552返回cond中的self._loss /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.pyc（pred，fn1，fn2，name） 1757 context_t = CondContext（pred，pivot_1，branch = 1）1758 context_t.Enter（） - > 1759 orig_res，res_t = context_t.BuildCondBranch（fn1）1760 context_t.ExitResult（res_t）1761 context_t.Exit（）/ usr / local / BuildCondBranch中的lib / python2.7 / dist-packages / tensorflow / python / ops / control_flow_ops.pyc（self，fn）1658 def BuildCondBranch（self，fn）：1659“”“将fn（）定义的子图添加到图形中 . “”“ - > 1660 r = fn（）1661 original_r = r 1662 result = [] / usr / _average_loss（）中的local / lib / python2.7 / dist-packages / tensorflow / contrib / tensor_forest / python / tensor_forest.pyc 544 probs = self.inference_graph（features）545返回math_ops.reduce_sum（self.loss_fn（ - > 546） probs，labels））/ math_ops.to_float（array_ops.shape（labels）[0]）547 548 self._loss = control_flow_ops.cond（/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tensor_forest _loss中的/ python/tensor_forest.pyc（probs，targets）508 def _loss（probs，targets）：509 if targets.get_shape（） . ndims> 1： - > 510 targets = array_ops.squeeze（targets，squeeze_dims = [1 ]）511 one_hot_labels = array_ops.one_hot（512 math_ops.to_int32（targets），/usr /local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.pyc in squeeze（输入，轴，名称， squeeze_dims）2270 if np.isscalar（axis）：2271 axis = [axis] - > 2272 return gen_array_ops._squeeze（input，axis，name）2273 2274 /usr/local/lib/python2.7/dist-packages/tensorflow/ _squeeze中的python / ops / gen_array_ops.pyc（input，squeeze_dims，name）3496“”“ 3497 result = _op_def_lib.apply_op（“Squeeze”，input = input， - > 3498 squeeze_dims = squeeze_dims，name = name）3499返回结果3500 /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework apply_op中的/op_def_library.pyc（self，op_type_name，name，** keywords）761 op = g.create_op（op_type_name，inputs，output_types，name = scope，762 input_types = input_types，attrs = attr_protos， - > 763 op_def = op_def ）764如果output_structure：765在create_op中输出= op.outputs /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.pyc（self，op_type，inputs，dtypes，input_types，name， attrs，op_def，compute_shapes，compute_device）2395 original_op = self._default_original_op，op_def = op_def）2396 if compute_shapes： - > 2397 set_shapes_for_outputs（ret）2398 self._add_op（ret）2399 self._record_op_seen_by_control_dependencies（ret）/ usr / local / lib set_shapes_for_outputs（op）中的/python2.7/dist-packages/tensorflow/python/framework/ops.pyc 1755 shape_func = _call_cpp_shape_fn_and_require_op 1756 - > 1757 shapes = shape_func（op）1758如果形状为None：1759引发RuntimeError（/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.pyc在call_with_requiring（op）1705 1706 def call_with_requiring（op）： - > 1707 return call_cpp_shape_fn（op，require_shape_fn = True）1708 1709 _call_cpp_shape_fn_and_require_op = call_with_requiring /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/common_shapes.pyc call_cpp_shape_fn（op ，input_tensors_needed，input_tensors_as_shapes_needed，debug_python_shape_fn，require_shape_fn）608个解析度= _call_cpp_shape_fn_impl（OP，input_tensors_needed，609 input_tensors_as_shapes_needed， - > 610 debug_python_shape_fn，require_shape_fn）611如果没有isinstance（RES，字典）：612＃手柄其中_call_cpp_shape_fn_impl调用unknown_shape（运的情况下） . _call_cpp_shape_fn_impl中的/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/common_shapes.pyc（op，input_tensors_needed，input_tensors_as_shapes_needed，debug_python_shape_fn，require_shape_fn）673 missing_shape_fn = True 674 else： - > 675引发ValueError （err.message）676 677 if missing_shape_fn：ValueError：无法压缩dim [1]，预期维度为1，对于输入形状为'cond / Squeeze'（op：'Squeeze'）得到7：[？，7] .