常連

LSTMを使うGANの実装をしています。
その際に使うpredict()が実行できずに困っております。
```keras
def build_generator(seq_length,z_dim):
  model = Sequential()
  model.add(Dense(32, input_dim=z_dim))
  model.add(Embedding(50,32,input_length=z_dim))
  model.add(LSTM(32))
  model.add(Dense(seq_length*jigen_length, activation='softmax'))
  model.add(Reshape((5, 1)))
  model.summary()
  return model

z = np.random.normal(0, 1, (batch_size, 100))
gen_text = generator.predict(z) #実行できない
```
```error
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-169-f04cfd1d3585> in <module>
      6 print(len(x_train))
      7 z = np.random.normal(0, 1, (batch_size, 100))
----> 8 gen_text = generator.predict(z)
      9 
     10 print(z)

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
    128       raise ValueError('{} is not supported in multi-worker mode.'.format(
    129           method.__name__))
--> 130     return method(self, *args, **kwargs)
    131 
    132   return tf_decorator.make_decorator(

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1597           for step in data_handler.steps():
   1598             callbacks.on_predict_batch_begin(step)
-> 1599             tmp_batch_outputs = predict_function(iterator)
   1600             if data_handler.should_sync:
   1601               context.async_wait()

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
    812       # In this case we have not created variables on the first call. So we can
    813       # run the first trace but we should fail if variables are created.
--> 814       results = self._stateful_fn(*args, **kwds)
    815       if self._created_variables:
    816         raise ValueError("Creating variables on a non-first call to a function"

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
   2827     with self._lock:
   2828       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2829     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   2830 
   2831   @property

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _filtered_call(self, args, kwargs, cancellation_manager)
   1841       `args` and `kwargs`.
   1842     """
-> 1843     return self._call_flat(
   1844         [t for t in nest.flatten((args, kwargs), expand_composites=True)
   1845          if isinstance(t, (ops.Tensor,

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1921         and executing_eagerly):
   1922       # No tape is watching; skip to running the function.
-> 1923       return self._build_call_outputs(self._inference_function.call(
   1924           ctx, args, cancellation_manager=cancellation_manager))
   1925     forward_backward = self._select_forward_and_backward_functions(

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
    543       with _InterpolateFunctionError(self):
    544         if cancellation_manager is None:
--> 545           outputs = execute.execute(
    546               str(self.signature.name),
    547               num_outputs=self._num_outputs,

~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     57   try:
     58     ctx.ensure_initialized()
---> 59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:

InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  indices[0,3] = -1 is not in [0, 50)
	 [[node sequential_117/embedding_73/embedding_lookup (defined at <ipython-input-168-f04cfd1d3585>:8) ]]
  (1) Invalid argument:  indices[0,3] = -1 is not in [0, 50)
	 [[node sequential_117/embedding_73/embedding_lookup (defined at <ipython-input-168-f04cfd1d3585>:8) ]]
	 [[sequential_117/embedding_73/embedding_lookup/_6]]
0 successful operations.
0 derived errors ignored. [Op:__inference_predict_function_68877]

Errors may have originated from an input operation.
Input Source operations connected to node sequential_117/embedding_73/embedding_lookup:
 sequential_117/embedding_73/embedding_lookup/68418 (defined at C:\Users\gest\anaconda3\lib\contextlib.py:113)

Input Source operations connected to node sequential_117/embedding_73/embedding_lookup:
 sequential_117/embedding_73/embedding_lookup/68418 (defined at C:\Users\gest\anaconda3\lib\contextlib.py:113)

Function call stack:
predict_function -> predict_function
```
また、EmbeddingレイヤーとLSTMレイヤーを削除したらpredict()が実行出来ました。
Embeddingレイヤーのみ削除、LSTMレイヤーのみ削除の場合は実行出来ませんでした。
両コードともコンパイルは通ります。
どうか宜しくお願いします。
```keras
def build_generator(seq_length,z_dim):
  model = Sequential()
  model.add(Dense(32, input_dim=z_dim))
  model.add(Dense(seq_length*jigen_length, activation='softmax'))
  #model.add(Reshape((5, 1)))
  model.summary()
  return model


z = np.random.normal(0, 1, (batch_size, 100))
gen_text = generator.predict(z)　#実行できる

```
```keras
#実際関数を呼び出してGANのモデルをコンパイルしてあげる
discriminator = build_discriminatior()
discriminator.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])
generator = build_generator(seq_length,z_dim)

#識別器の学習機能をオフにしてあげる。識別器と生成器を別々に学習させてあげられる
discriminator.trainable = False 

gan = build_gan(generator, discriminator)
gan.compile(loss='binary_crossentropy', optimizer=Adam())
```

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