我试图在我自己的数据集上训练'Wide & Deep Learning'模型，当我将模型拟合到训练集时会发生此错误。
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-15-8f5351c1fdf8> in <module>()
----> 1 m.fit(input_fn=train_input_fn, steps=200)
/Users/prisma/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.pyc in fit(self, x, y, input_fn, steps, batch_size, monitors, max_steps)
331 steps=steps,
332 monitors=monitors,
--> 333 max_steps=max_steps)
334 logging.info('Loss for final step: %s.', loss)
335 return self
/Users/prisma/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.pyc 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)
660 features, targets = input_fn()
661 self._check_inputs(features, targets)
--> 662 train_op, loss_op = self._get_train_ops(features, targets)
663
664 # Add default monitors.
/Users/prisma/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.pyc in _get_train_ops(self, features, targets)
188 logits = self._logits(features, is_training=True)
189 if self._enable_centered_bias:
--> 190 centered_bias_step = [self._centered_bias_step(targets, features)]
191 else:
192 centered_bias_step = []
/Users/prisma/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.pyc in _centered_bias_step(self, targets, features)
272 with ops.name_scope(None, "centered_bias", (targets, features)):
273 training_loss = self._target_column.training_loss(
--> 274 logits, targets, features)
275 # Learn central bias by an optimizer. 0.1 is a convervative lr for a
276 # single variable.
/Users/prisma/anaconda/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/target_column.pyc in training_loss(self, logits, target, features, name)
204 """
205 target = target[self.name] if isinstance(target, dict) else target
--> 206 loss_unweighted = self._loss_fn(logits, target)
207
208 weight_tensor = self.get_weight_tensor(features)
/Users/prisma/anaconda/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/target_column.pyc in _log_loss_with_two_classes(logits, target)
387 target = array_ops.expand_dims(target, dim=[1])
388 loss_vec = nn.sigmoid_cross_entropy_with_logits(logits,
--> 389 math_ops.to_float(target))
390 return loss_vec
391
/Users/prisma/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/nn.pyc in sigmoid_cross_entropy_with_logits(logits, targets, name)
432 except ValueError:
433 raise ValueError("logits and targets must have the same shape (%s vs %s)"
--> 434 % (logits.get_shape(), targets.get_shape()))
435
436 # The logistic loss formula from above is
ValueError: logits and targets must have the same shape ((?, 1) vs (13647309, 24))
我无法弄清楚为什么logits的形状（？，1）而不是（13647309,24）。 input_fn函数应该返回一个大小为（13647309,24）的特征字典和一个形状的标签张量（13647309,24）。就我而言，logits应该是模型的输出，但DNNLinearCombinedClassifier中没有指定输出大小的位置，因此我假设输出大小自动调整为与标签尺寸相同，即（13647309,24）。我不知道为什么会出现这种错误，但我猜我的模型出了问题。由于整个代码太长而无法粘贴，我只需将模型构建部分粘贴在此处。
model_dir = tempfile.mkdtemp()
m = tf.contrib.learn.DNNLinearCombinedClassifier(
model_dir=model_dir,
linear_feature_columns=wide_columns,
dnn_feature_columns=deep_columns,
dnn_hidden_units=[100, 50])
我没有从tensorflow教程改变模型的参数。我刚刚定义了＆＃39; wide_columns＆＃39;和＆＃39; deep_columns＆＃39;就我自己的数据集而言。模型或输入功能有问题吗？我无法在tf.learn api网站上找到DNNLinearCombinedClassifier的参考资料。
更新输入功能的代码
def input_fn(df):
continuous_cols = {k: tf.constant(df[k].values)
for k in CONTINUOUS_COLUMNS}
categorical_cols = {k: tf.SparseTensor(
indices=[[i, 0] for i in range(df[k].size)],
values=df[k].values,
shape=[df[k].size, 1])
for k in CATEGORICAL_COLUMNS}
feature_cols = dict(continuous_cols.items() + categorical_cols.items())
label = tf.constant(df[Label_COLUMNS].values)
return feature_cols, label
＆＃39; Label_COLUMNS＆＃39;。
中有24个频道