我对Tensorflow完全是新手。我一直在尝试将Deep MNIST教程改编为预测MovieLens数据集的电影评级。我稍微简化了模型，使其不再使用5分制，而是简单的二元Y/N评级（类似于Netflix最新的评级系统）。我试图仅使用部分评级来预测新项目的偏好。在训练模型时，我在堆栈跟踪中得到了以下错误：
Traceback (most recent call last): File "/Users/Eric/dev/Coding Academy >Tutorials/tf_impl/deep_tf_group_rec_SO.py", line 223, in <module> train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5}) File "/Library/Python/2.7/site->packages/tensorflow/python/framework/ops.py", line 1550, in run _run_using_default_session(self, feed_dict, self.graph, session) File "/Library/Python/2.7/site->packages/tensorflow/python/framework/ops.py", line 3764, in >_run_using_default_session session.run(operation, feed_dict) File "/Library/Python/2.7/site->packages/tensorflow/python/client/session.py", line 767, in run run_metadata_ptr) File "/Library/Python/2.7/site->packages/tensorflow/python/client/session.py", line 965, in _run feed_dict_string, options, run_metadata) File "/Library/Python/2.7/site->packages/tensorflow/python/client/session.py", line 1015, in _do_run target_list, options, run_metadata) File "/Library/Python/2.7/site->packages/tensorflow/python/client/session.py", line 1035, in _do_call raise type(e)(node_def, op, message)tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and >labels must be same size: logits_size=[1,2] labels_size=[50,2] [[Node: SoftmaxCrossEntropyWithLogits = >SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, >_device="/job:localhost/replica:0/task:0/cpu:0"](Reshape_2, Reshape_3)]]Caused by op u'SoftmaxCrossEntropyWithLogits', defined at: File "/Users/Eric/dev/Coding Academy >Tutorials/tf_impl/deep_tf_group_rec_SO.py", line 209, in <module> cross_entropy = >tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, >logits=y_conv)) File "/Library/Python/2.7/site->packages/tensorflow/python/ops/nn_ops.py", line 1617, in >softmax_cross_entropy_with_logits precise_logits, labels, name=name) File "/Library/Python/2.7/site->packages/tensorflow/python/ops/gen_nn_ops.py", line 2265, in >_softmax_cross_entropy_with_logits features=features, labels=labels, name=name) File "/Library/Python/2.7/site->packages/tensorflow/python/framework/op_def_library.py", line 763, in >apply_op op_def=op_def) File "/Library/Python/2.7/site->packages/tensorflow/python/framework/ops.py", line 2327, in create_op original_op=self._default_original_op, op_def=op_def) File "/Library/Python/2.7/site->packages/tensorflow/python/framework/ops.py", line 1226, in __init__ self._traceback = _extract_stack()InvalidArgumentError (see above for traceback): logits and labels must >be same size: logits_size=[1,2] labels_size=[50,2] [[Node: SoftmaxCrossEntropyWithLogits = >SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, >_device="/job:localhost/replica:0/task:0/cpu:0"](Reshape_2, Reshape_3)]]导致错误的代码可以在这里查看 here
模型中使用的变量大小：
-
x (?, 1682)
-
y_ (?, 2)
- x_history (?, 290, 290, 1)
- h_pool1 (?, 145, 145, 32)
- h_pool2 (?, 73, 73, 64)
- h_pool3 (?, 37, 37, 128)
- h_pool4 (?, 19, 19, 256)
- h_pool5 (?, 10, 10, 512)
- h_fc1 (?, 1024)
- h_fc1_drop (?, 1024)
- y_conv (?, 2)
回答：
问题出在你将输入批次（形状为50个训练实例 x 1682个特征）重塑为[-1, 290, 290, 1]的这行代码：
x_history = tf.reshape(x, [-1, 290, 290, 1])你可以通过运行以下代码来查看x_history最终的形状：
x_history.eval(feed_dict={x:batch[0], y_:batch[1], keep_prob:1.0}).shape=> (1, 290, 290, 1)这实际上是将50个实例的所有特征放入一个单一实例中（第一维度是1，而它需要是50），然后通过网络的其余部分运行。因此，你的交叉熵评估失败了，因为它无法将50个目标标签的批次与网络的单一输出对齐。
你需要选择层形状，以便通过网络保留批次维度（你在形状输出中看到的？）。一种方法是将你的特征填充到1764，然后重塑为[-1,42,42,1]，因为42*42 = 1764。
值得注意的是，二维卷积最常用于自然是二维的图像数据。鉴于你的特征不是二维的，你可能最好从一个更简单的全连接层网络开始？
