我试图让一个简单的RNN在tensorflow中工作，但我遇到了几个问题 .
我现在要做的是简单地运行RNN的正向传递，其中LSTM作为其单元类型 .
我已经删除了一些新闻文章，并希望将它们输入RNN . 我将字符串（包括所有文章的串联）拆分为字符并将字符映射到整数 . 然后我有一个热编码那些整数 .
data = [c for c in article]
chars = list(set(data))
idx_chars = {i:ch for i,ch in enumerate(chars)}
chars_idx = {ch:i for i,ch in enumerate(chars)}
int_data = [chars_idx[ch] for ch in data]
# config values
vocab_size = len(chars)
hidden_size = 100
seq_length = 25
# helper function to get one-hot encoding
def onehot(value):
result = np.zeros(vocab_size)
result[value] = 1
return result
def vectorize_input(inputs):
result = [onehot(x) for x in inputs]
return result
input = vectorize_input(int_data[:25])
现在为tensorflow代码 . 我想要遍历数据中的所有字符，并为每个正向传递使用25个字符 . 我的第一个问题是关于批量大小，如果我想按照我刚才提到的方式这样做，我的批量大小为1，对吧？因此，与输入中的一个char对应的每个向量都具有shape [1，vocab_size]，并且在我的输入中有25个这样的向量 . 所以我使用了以下张量：
seq_input = tf.placeholder(tf.int32, shape = [seq_length, 1, vocab_size])
targets = tf.placeholder(tf.int32, shape = [seq_length, 1, vocab_size])
inputs = [tf.reshape(i,(1,vocab_size)) for i in tf.split(0,seq_length,seq_input)]
我必须创建最后一个张量，因为这是rnn函数所期望的格式 .
然后我遇到了变量范围的问题，我得到以下错误：
cell = rnn_cell.BasicLSTMCell(hidden_size, input_size = vocab_size)
# note: first argument of zero_state is the batch_size
initial_state = cell.zero_state(1, tf.float32)
outputs, state = rnn.rnn(cell, inputs, initial_state= initial_state)
sess = tf.Session()
sess.run([outputs, state], feed_dict = {inputs:input})
ValueError Traceback (most recent call last)
<ipython-input-90-449af38c387d> in <module>()
7 # note: first argument of zero_state is supposed to be batch_size
8 initial_state = cell.zero_state(1, tf.float32)
----> 9 outputs, state = rnn.rnn(cell, inputs, initial_state= initial_state)
10
11 sess = tf.Session()
/Library/Python/2.7/site-packages/tensorflow/python/ops/rnn.pyc in rnn(cell, inputs, initial_state, dtype, sequence_length, scope)
124 zero_output, state, call_cell)
125 else:
--> 126 (output, state) = call_cell()
127
128 outputs.append(output)
/Library/Python/2.7/site-packages/tensorflow/python/ops/rnn.pyc in <lambda>()
117 if time > 0: vs.get_variable_scope().reuse_variables()
118 # pylint: disable=cell-var-from-loop
--> 119 call_cell = lambda: cell(input_, state)
120 # pylint: enable=cell-var-from-loop
121 if sequence_length:
/Library/Python/2.7/site-packages/tensorflow/python/ops/rnn_cell.pyc in __call__(self, inputs, state, scope)
200 # Parameters of gates are concatenated into one multiply for efficiency.
201 c, h = array_ops.split(1, 2, state)
--> 202 concat = linear([inputs, h], 4 * self._num_units, True)
203
204 # i = input_gate, j = new_input, f = forget_gate, o = output_gate
/Library/Python/2.7/site-packages/tensorflow/python/ops/rnn_cell.pyc in linear(args, output_size, bias, bias_start, scope)
700 # Now the computation.
701 with vs.variable_scope(scope or "Linear"):
--> 702 matrix = vs.get_variable("Matrix", [total_arg_size, output_size])
703 if len(args) == 1:
704 res = math_ops.matmul(args[0], matrix)
/Library/Python/2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(name, shape, dtype, initializer, trainable, collections)
254 return get_variable_scope().get_variable(_get_default_variable_store(), name,
255 shape, dtype, initializer,
--> 256 trainable, collections)
257
258
/Library/Python/2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(self, var_store, name, shape, dtype, initializer, trainable, collections)
186 with ops.name_scope(None):
187 return var_store.get_variable(full_name, shape, dtype, initializer,
--> 188 self.reuse, trainable, collections)
189
190
/Library/Python/2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(self, name, shape, dtype, initializer, reuse, trainable, collections)
99 if should_check and not reuse:
100 raise ValueError("Over-sharing: Variable %s already exists, disallowed."
--> 101 " Did you mean to set reuse=True in VarScope?" % name)
102 found_var = self._vars[name]
103 if not shape.is_compatible_with(found_var.get_shape()):
ValueError: Over-sharing: Variable forward/RNN/BasicLSTMCell/Linear/Matrix already exists, disallowed. Did you mean to set reuse=True in VarScope?
而且我不确定为什么会出现这个错误，因为我实际上没有在我的代码中指定任何变量，变量只在rnn和rnn_cell函数内创建，有人能告诉我如何修复这个错误吗？
我目前得到的另一个错误是类型错误，因为我的输入是tf.int32类型，但是在LSTM中创建的隐藏层是tf.float32类型，而rnn_cell.py代码中的Linear函数是连接的那两个张量并将它们乘以权重矩阵 . 为什么这不可能，我假设输入是一个热门编码并因此具有类型int32相对常见？
一般来说，这种方法在培训char-rnns时批量大小为1？我只看过Andrej Karpathy的代码，在那里他训练了一个基本numpy的char-rnn，他使用相同的程序，他只是按照长度为25的序列浏览整个文本 . 这是代码：https://gist.github.com/karpathy/d4dee566867f8291f086