我是 TensorFlow 的初学者，仍在试图弄清楚它是如何工作的，所以我不确定这个错误是我的架构问题还是更基本的问题——我正在尝试训练一个连体神经网络（我们将左右输入输入到具有相同权重的左右 NN 中，并尝试将其映射到如果输入相似则距离小、如果输入不同则距离大的特征向量）。
我得到的错误发生在回归步骤：
File "siamese.py", line 59, in <module>
network = regression(y_pred, optimizer='adam',
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tflearn/models/dnn.py", line 63, in __init__
best_val_accuracy=best_val_accuracy)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tflearn/helpers/trainer.py", line 120, in __init__
clip_gradients)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tflearn/helpers/trainer.py", line 646, in initialize_training_ops
ema_num_updates=self.training_steps)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tflearn/summaries.py", line 236, in add_loss_summaries
loss_averages_op = loss_averages.apply([loss] + other_losses)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/training/moving_averages.py", line 292, in apply
colocate_with_primary=(var.op.type == "Variable"))
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/training/slot_creator.py", line 106, in create_zeros_slot
val = array_ops.zeros(primary.get_shape().as_list(), dtype=dtype)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1071, in zeros
shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 628, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 198, in _tensor_shape_tensor_conversion_function
"Cannot convert a partially known TensorShape to a Tensor: %s" % s)
ValueError: Cannot convert a partially known TensorShape to a Tensor: (?,)
如果第一个维度需要None用于批量大小，我不知道如何解决这个问题（如果我错了，请纠正我）。
代码的相关部分如下：
BATCH_SIZE=100
def contrastive_loss(y_pred, y_true, margin=1.0):
return tf.mul(1-y_true, tf.square(y_pred)) + tf.mul(y_true, tf.square(tf.maximum((margin-y_pred),0)))
## Load dataset
f = h5py.File('./data/paired_training_data.hdf','r')
X1 = f["train_X1"]
X2 = f["train_X2"]
Y = f["train_Y_paired"]
## Inputs: 1 example (phoneme pair), dropout probability
inp_sound1 = input_data(shape=[None, 1, N_MFCC_CHANNELS, N_IN_CHANNELS])
networkL = conv_1d(inp_sound1, reuse=None, scope="conv1d")
networkL = max_pool_1x6(networkL)
networkL = fully_connected(networkL, n_units=N_FULLY_CONN, activation='relu', scope="fc1")
networkL = dropout(networkL, .5) # unshared?
networkL = fully_connected(networkL, n_units=N_FULLY_CONN, activation='relu', scope="fc2")
inp_sound2 = input_data(shape=[None, 1, N_MFCC_CHANNELS, N_IN_CHANNELS])
networkR = conv_1d(inp_sound2, reuse=True, scope="conv1d")
networkR = max_pool_1x6(networkR)
networkR = fully_connected(networkR, n_units=N_FULLY_CONN, activation='relu', reuse=True, scope="fc1")
networkR = dropout(networkR, .5)
networkR = fully_connected(networkR, n_units=N_FULLY_CONN, activation='relu', reuse=True, scope="fc2")
l2_loss = tf.reduce_sum(tf.square(tf.sub(networkL, networkR)), 1)
y_pred = tf.sqrt(l2_loss)
#y_true = input_data(shape=[None])
## Training
network = regression(y_pred, optimizer='adam',
loss=contrastive_loss, learning_rate=0.0001, to_one_hot=False)
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit([X1, X2], Y, n_epoch=10, batch_size=BATCH_SIZE, show_metric=True, validation_set=0.1)
任何帮助——尤其是了解如何在未来自己调试这些问题——将不胜感激！