我想训练一个可以逐个字符生成文本的神经网络。经过一些研究，我决定将LSTM网络用于此任务。
我的输入的结构如下：
我有一个充满文本的文件（大约90,000,000个字符），我将其切成50个字符的重叠序列。考虑以下示例语句：The quick brown fox jumps over the lazy dog
我将文本分成多个序列：The quick brownhe quick brown_e quick brown f_quick brown foquick brown fox
我添加了下划线，这些空格将不会显示在这些地方...
这些将是我输入数据的时间步骤。输出将是每个序列之后的下一个字符，因此上述序列的_, f, o, x and _。
字符在向量中一键编码，字典中所有字符的长度，因此如果字母由A B C D组成，则字符C将表示为[0 0 1 0]
因为我无法一次将所有矢量化的文本放入内存中，所以我将其分为几批，只包含少量生成的序列供网络训练。
这样我得到我的输入占位符：
x = tf.placeholder(tf.float32, [batch_size, time_steps, char_size]
在下面的示例代码中，我使用128的
batch_size，50的time_steps和48的char_size表示具有50个大写和小写字母的标准字母。传递给
num_units的BasicLSTMCell也被任意选择为256（以下是有关我的超参数的一些教程）这是代码：
import tensorflow as tf
batch_size = 128
time_steps = 50
char_size = 50
num_units = 256
sess = tf.InteractiveSession()
X = tf.placeholder(tf.float32, [batch_size, time_steps, char_size])
cell = tf.contrib.rnn.BasicLSTMCell(num_units)
cell = tf.contrib.rnn.MultiRNNCell([cell] * 2, state_is_tuple=True)
output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
这是错误消息：
Traceback (most recent call last):
File ".\issue.py", line 16, in <module>
output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn.py", line 598, in dynamic_rnn
dtype=dtype)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn.py", line 761, in _dynamic_rnn_loop
swap_memory=swap_memory)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2775, in while_loop
result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2604, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2554, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn.py", line 746, in _time_step
(output, new_state) = call_cell()
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn.py", line 732, in <lambda>
call_cell = lambda: cell(input_t, state)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 180, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\layers\base.py", line 450, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 938, in call
cur_inp, new_state = cell(cur_inp, cur_state)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 180, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\layers\base.py", line 450, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 401, in call
concat = _linear([inputs, h], 4 * self._num_units, True)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 1039, in _linear
initializer=kernel_initializer)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 1065, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 962, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 360, in get_variable
validate_shape=validate_shape, use_resource=use_resource)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 1405, in wrapped_custom_getter
*args, **kwargs)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 183, in _rnn_get_variable
variable = getter(*args, **kwargs)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 183, in _rnn_get_variable
variable = getter(*args, **kwargs)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 352, in _true_getter
use_resource=use_resource)
File "C:\Users\uidq6096\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 669, in _get_single_variable
found_var.get_shape()))
ValueError: Trying to share variable rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel, but specified shape (512, 1024) and found shape (306, 1024).
在过去的几天里，我为此一直在挣扎，我在这里想念什么？
最佳答案
循环初始化多个单元格，而不使用[cell] * n表示法：
cells = []
for _ in range(n):
cells.append(tf.contrib.rnn.BasicLSTMCell(num_units)) # build list of cells
cell = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=True) # pass your list of cells
output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
否则，它基本上是尝试多次使用同一单元，但尺寸无法计算。我相信在1.0版本中，此行为已更改。您曾经能够摆脱原来的语法；现在您必须使用它。
关于python - 尝试在TensorFlow中使用BasicLSTMCell和dynamic_rnn建立网络时发生ValueError，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/47205248/