我是张量流的初学者。我已经构建了简单的模型，但还没有尝试过像多层LSTM这样的东西，所以非常感谢任何类型的反馈：）
我目前正在尝试重新编写由sherjilozair从头开始构建的字符级模型，仅仅因为我想知道如何使用张量流（我之前已经构建了自己的小型DL库）由cs231n指定）。现在我正在努力构建一个简单的2层LSTM模型，并且不确定是什么问题。这是我到目前为止编写的代码：
class Model():
def __init__(self, batch_size, seq_length, lstm_size, num_layers, grad_clip, vocab_size):
self.lr = tf.Variable(0.0, trainable=False)
#Define input and output
self.input_data = tf.placeholder(tf.float32, [batch_size, seq_length])
self.output_data = tf.placeholder(tf.float32, [batch_size, seq_length]) #although int would be better for character level..
#Define the model
cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=lstm_size) #can choose if basic or otherwise later on...
self.cell = cell = rnn_cell.MultiRNNCell([cell] * num_layers)
self.initial_state = cell.zero_state(batch_size, tf.float32)
with tf.variable_scope("lstm"):
softmax_w = tf.get_variable("softmax_w", [lstm_size, vocab_size])
softmax_b = tf.get_variable("softmax_b", [vocab_size])
#_, enc_state = rnn.rnn(cell, encoder_inputs, dtype=dtype)
#outputs, states = rnn_decoder(decoder_inputs, enc_state, cell)
outputs, states = seq2seq.basic_rnn_seq2seq(
[self.input_data],
[self.output_data],
cell,
scope='lstm'
)
#see how attention helps improving this model state...
#was told that we should actually use samples softmax loss
self.loss = tf.nn.sampled_softmax_loss(
softmax_w,
softmax_b,
outputs,
self.output_data,
batch_size,
vocab_size
)
我目前遇到的问题是tf.nn.sampled_softmax_loss。我在调试方面走了很长一段路，并且不了解Tensorflow的输入约定。我是否每次都要输入张量列表？
我收到以下错误：
Traceback (most recent call last):
File "Model.py", line 76, in <module>
vocab_size=82
File "Model.py", line 52, in __init__
vocab_size
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/nn.py", line 1104, in sampled_softmax_loss
name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/nn.py", line 913, in _compute_sampled_logits
array_ops.expand_dims(inputs, 1),
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 506, in expand_dims
return _op_def_lib.apply_op("ExpandDims", input=input, dim=dim, name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 411, in apply_op
as_ref=input_arg.is_ref)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 566, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/constant_op.py", line 179, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/constant_op.py", line 162, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.py", line 332, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.py", line 269, in _AssertCompatible
raise TypeError("List of Tensors when single Tensor expected")
TypeError: List of Tensors when single Tensor expected
我不确定我做错了什么，无论是输入还是生成变量等。问题 - 如上所述 - 似乎是在samples_softmax_loss函数中，但我真的不确定..我是使用以下参数调用类（就像占位符一样，只是为了测试模型是否'runnable'）：
Model = Model(batch_size=32,
seq_length=128,
lstm_size=512,
num_layers=2,
grad_clip=5,
vocab_size=82
)
另外，如果我犯了其他任何错误等，请在评论中告诉我！这是我在tensorflow中使用seq2seq模型的第一个模型，所以非常感谢任何建议！
答案 0 :(得分：2)
这个特殊错误是关于传递outputs这是一个列表，当tf.nn.sampled_softmax_loss期望一个张量时。
seq2seq.basic_rnn_seq2seq函数返回大小为[batch_size x output_size]的张量列表作为第一个输出。假设您的每个输出都是一维的，您希望使用tf.concat（创建一个大小为[seq_len x batch_size x 1]的张量），tf.squeeze最后一个维度（结果{{1}）来连接输出列表}}和tf.transpose使[seq_len x batch_size]的尺寸为output，与[batch_size x seq_len]相同。
要调试问题，请使用self.output_data打印张量大小。