TensorFlow GridLSTM示例 主要问题

GridLSTM有两种实现：

tf.contrib.rnn.GridLSTMCell

tf.contrib.grid\n.Grid1LSTMCell

在

tf.nn.dynamic\u rnn

中使用这两种方法都会给我带来错误，我的问题是： 是否有任何实现、存储库等在最新的tensorflow API中使用任一单元

我知道，但它关注的是seq2seq，我无法将其应用于动态或静态

当前进展和小问题 如果我使用的是

tf.contrib.rnn.GridLSTMCell

，那么我的代码如下所示：

tf.reset_default_graph()

rnn_cell_sizes = [512, 512]

cells = []
for size in rnn_cell_sizes:
    cell = tf.contrib.rnn.GridLSTMCell(num_units=size)
    cells.append(cell)

cell = tf.contrib.rnn.MultiRNNCell(cells)
input_placeholder = tf.placeholder(tf.float32, [None, sequence_length, fft_size * feature_vector])

tf.nn.dynamic_rnn(cell=cell, inputs=input_placeholder, dtype=tf.float32)

tf.reset_default_graph()

rnn_cell_sizes = [512, 512]

cells = []
for size in rnn_cell_sizes:
    cell = tf.contrib.grid_rnn.Grid1BasicLSTMCell(num_units=size, output_is_tuple=True)
    cells.append(cell)

cell = tf.contrib.rnn.MultiRNNCell(cells)
input_placeholder = tf.placeholder(tf.float32, [None, sequence_length, fft_size * feature_vector])

tf.nn.dynamic_rnn(cell=cell, inputs=input_placeholder, dtype=tf.float32)

这将返回如下错误：

ValueError:必须指定num\u frequency\u块

这是因为需要num_frequency_块。我能看出这是一张单子，但我搞不清楚它是干什么用的

或者，我可以使用

tf.contrib.grid\u rnn

中的一些单元格，例如

tf.contrib.grid\u rnn.Grid1BasicLSTMCell

然后，我的代码变成这样：

tf.reset_default_graph()

rnn_cell_sizes = [512, 512]

cells = []
for size in rnn_cell_sizes:
    cell = tf.contrib.rnn.GridLSTMCell(num_units=size)
    cells.append(cell)

cell = tf.contrib.rnn.MultiRNNCell(cells)
input_placeholder = tf.placeholder(tf.float32, [None, sequence_length, fft_size * feature_vector])

tf.nn.dynamic_rnn(cell=cell, inputs=input_placeholder, dtype=tf.float32)

tf.reset_default_graph()

rnn_cell_sizes = [512, 512]

cells = []
for size in rnn_cell_sizes:
    cell = tf.contrib.grid_rnn.Grid1BasicLSTMCell(num_units=size, output_is_tuple=True)
    cells.append(cell)

cell = tf.contrib.rnn.MultiRNNCell(cells)
input_placeholder = tf.placeholder(tf.float32, [None, sequence_length, fft_size * feature_vector])

tf.nn.dynamic_rnn(cell=cell, inputs=input_placeholder, dtype=tf.float32)

这将抛出一个大堆栈跟踪和一个类似这样的错误

----------------------------------------------------------------------

-----
TypeError                                 Traceback (most recent call last)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py in zeros(shape, dtype, name)
   1438       shape = tensor_shape.as_shape(shape)
-> 1439       output = constant(zero, shape=shape, dtype=dtype, name=name)
   1440     except (TypeError, ValueError):

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name, verify_shape)
    207       tensor_util.make_tensor_proto(
--> 208           value, dtype=dtype, shape=shape, verify_shape=verify_shape))
    209   dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
    379     # exception when dtype is set to np.int64
--> 380     if shape is not None and np.prod(shape, dtype=np.int64) == 0:
    381       nparray = np.empty(shape, dtype=np_dt)

/usr/local/lib/python3.5/dist-packages/numpy/core/fromnumeric.py in prod(a, axis, dtype, out, keepdims)
   2517     return _methods._prod(a, axis=axis, dtype=dtype,
-> 2518                           out=out, **kwargs)
   2519 

/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py in _prod(a, axis, dtype, out, keepdims)
     34 def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
---> 35     return umr_prod(a, axis, dtype, out, keepdims)
     36 

TypeError: __int__ returned non-int (type NoneType)

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-51-f9b0851a2255> in <module>()
     11 input_placeholder = tf.placeholder(tf.float32, [None, sequence_length, fft_size * feature_vector])
     12 
---> 13 tf.nn.dynamic_rnn(cell=cell, inputs=input_placeholder, dtype=tf.float32)

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn.py in dynamic_rnn(cell, inputs, sequence_length, initial_state, dtype, parallel_iterations, swap_memory, time_major, scope)
    612         swap_memory=swap_memory,
    613         sequence_length=sequence_length,
--> 614         dtype=dtype)
    615 
    616     # Outputs of _dynamic_rnn_loop are always shaped [time, batch, depth].

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn.py in _dynamic_rnn_loop(cell, inputs, initial_state, parallel_iterations, swap_memory, sequence_length, dtype)
    775       loop_vars=(time, output_ta, state),
    776       parallel_iterations=parallel_iterations,
--> 777       swap_memory=swap_memory)
    778 
    779   # Unpack final output if not using output tuples.

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control_flow_ops.py in while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name)
   2814     loop_context = WhileContext(parallel_iterations, back_prop, swap_memory)  # pylint: disable=redefined-outer-name
   2815     ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context)
-> 2816     result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
   2817     return result
   2818 

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control_flow_ops.py in BuildLoop(self, pred, body, loop_vars, shape_invariants)
   2638       self.Enter()
   2639       original_body_result, exit_vars = self._BuildLoop(
-> 2640           pred, body, original_loop_vars, loop_vars, shape_invariants)
   2641     finally:
   2642       self.Exit()

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control_flow_ops.py in _BuildLoop(self, pred, body, original_loop_vars, loop_vars, shape_invariants)
   2588         structure=original_loop_vars,
   2589         flat_sequence=vars_for_body_with_tensor_arrays)
-> 2590     body_result = body(*packed_vars_for_body)
   2591     if not nest.is_sequence(body_result):
   2592       body_result = [body_result]

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn.py in _time_step(time, output_ta_t, state)
    760           skip_conditionals=True)
    761     else:
--> 762       (output, new_state) = call_cell()
    763 
    764     # Pack state if using state tuples

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn.py in <lambda>()
    746 
    747     input_t = nest.pack_sequence_as(structure=inputs, flat_sequence=input_t)
--> 748     call_cell = lambda: cell(input_t, state)
    749 
    750     if sequence_length is not None:

/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/grid_rnn/python/ops/grid_rnn_cell.py in __call__(self, inputs, state, scope)
    192                  new_output, new_state, True)
    193       _propagate(conf.priority, conf, self._cells,
--> 194                  c_prev, m_prev, new_output, new_state, False)
    195 
    196       # collect outputs and states

/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/grid_rnn/python/ops/grid_rnn_cell.py in _propagate(dim_indices, conf, cells, c_prev, m_prev, new_output, new_state, first_call)
    627   else:
    628     cell_inputs = array_ops.zeros([m_prev[0].get_shape().as_list()[0], 0],
--> 629                                   m_prev[0].dtype)
    630 
    631   last_dim_output = (new_output[-1]

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py in zeros(shape, dtype, name)
   1439       output = constant(zero, shape=shape, dtype=dtype, name=name)
   1440     except (TypeError, ValueError):
-> 1441       shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
   1442       output = fill(shape, constant(zero, dtype=dtype), name=name)
   1443   assert output.dtype.base_dtype == dtype

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, preferred_dtype)
    834       name=name,
    835       preferred_dtype=preferred_dtype,
--> 836       as_ref=False)
    837 
    838 

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
    924 
    925     if ret is None:
--> 926       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    927 
    928     if ret is NotImplemented:

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py in _tensor_shape_tensor_conversion_function(s, dtype, name, as_ref)
    248   if not s.is_fully_defined():
    249     raise ValueError(
--> 250         "Cannot convert a partially known TensorShape to a Tensor: %s" % s)
    251   s_list = s.as_list()
    252   int64_value = 0

ValueError: Cannot convert a partially known TensorShape to a Tensor: (?, 0)

----------------------------------------------------------------------
-----
TypeError回溯（最近一次调用上次）
/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py为零（形状、数据类型、名称）
1438形状=张量形状。as形状（形状）
->1439输出=常数（零，shape=shape，dtype=dtype，name=name）
1440除外（TypeError，ValueError）：
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py in constant（值、数据类型、形状、名称、验证形状）
207张量使用。生成张量原型(
-->208值，dtype=dtype，shape=shape，verify_shape=verify_shape））
209 dtype\u value=attr\u value\u pb2.AttrValue（type=tensor\u value.tensor.dtype）
/make_tensor_proto中的usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py（值、数据类型、形状、验证形状）
379#当dtype设置为np.int64时出现异常
-->380如果shape不是None且np.prod（shape，dtype=np.int64）==0：
381 nparray=np.empty（shape，dtype=np_dt）
/产品中的usr/local/lib/python3.5/dist-packages/numpy/core/fromneric.py（a、axis、dtype、out、keepdims）
2517返回方法。生产（a，轴=轴，数据类型=数据类型，
->2518输出=输出，**kwargs）
2519
/usr/local/lib/python3.5/dist-packages/numpy/core//u methods.py in\u prod（a、axis、dtype、out、keepdims）
34 def_prod（a，axis=None，dtype=None，out=None，keepdims=False）：
--->35返回umr_产品（a、轴、数据类型、输出、保持）
36
TypeError:\uuuuu int\uuuu返回的非int（类型NoneType）
在处理上述异常期间，发生了另一个异常：
ValueError回溯（最近一次调用上次）
在（）
11输入\u占位符=tf.占位符（tf.float32，[无，序列长度，fft大小*特征向量]）
12
--->13 tf.nn.dynamic\u rnn（单元格=单元格，输入=输入\u占位符，数据类型=tf.float32）
/动态rnn中的usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn.py（单元格、输入、序列长度、初始状态、数据类型、并行迭代、交换内存、时间、范围）
612交换内存=交换内存，
613序列长度=序列长度，
-->614数据类型=数据类型）
615
616#动态循环的输出总是成形的[时间、批次、深度]。
/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn.py in_动态循环（单元、输入、初始状态、并行迭代、交换内存、序列长度、数据类型）
775循环变量=（时间、输出、状态），
776并行迭代=并行迭代，
-->777交换内存=交换内存）
778
779#如果不使用输出元组，则解压缩最终输出。
/while_循环中的usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control_flow_ops.py（cond、body、loop_vars、shape_不变量、并行迭代、back_prop、swap_memory、name）
2814循环上下文=WhileContext（并行迭代、备份、交换内存）#pylint:disable=重新定义的外部名称
2815 ops.将_添加到_集合（ops.GraphKeys.WHILE_上下文、循环_上下文）
->2816 result=loop\u context.BuildLoop（cond、body、loop\u vars、shape\u不变量）
2817返回结果
2818
/BuildLoop中的usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control\u flow\u ops.py（self、pred、body、loop\u vars、shape\u不变量）
2638 self.Enter（）
2639原始\u body\u结果，退出\u vars=self.\u BuildLoop(
->2640 pred，主体，原始循环变量，循环变量，形状不变量）
2641最后：
2642 self.Exit（）
/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control_flow_ops.py in_BuildLoop（self、pred、body、original_loop_vars、loop_vars、shape_不变量）
2588结构=原始循环变量，
2589平面\u序列=带\u张量\u阵列的\u体\u的vars\u）
->2590阀体结果=阀体（*阀体的密封阀）
2591如果不是嵌套，则为嵌套序列（正文\u结果）：
2592 body_result=[body_result]
/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn.py in_time_步骤（时间、输出、状态）
760跳过（条件=真）
761其他：
-->762（输出，新状态）=调用单元（）
763
764#如果使用状态元组，则打包状态
/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn.py in（）
746
747输入\u t=nest.pack\u序列\u as（结构=输入，平坦\u序列=输入）
-->748调用单元=λ：单元（输入，状态）
749
750如果序列长度不是无：
/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/grid\u rnn/python/ops/grid\u rnn\u cell.py in\uuuuu调用（self、input、state、scope）
192新_输出，新_状态，真）
193_传播（conf.priority，conf，self._单元格，
--> 194