numpy配列のエスティメータ

Question

tf.estimator.inputs.numpy_input_fnを使用してnumpy配列を使用してモデルに入力するエスティメータを作成しています。次のように：

def input_fun(data): 
    x, y = data 

    x, y = np.reshape(x, (batch_size, -1, 1)), \ 
      np.reshape(y, (batch_size, -1, 1)) 

    return tf.estimator.inputs.numpy_input_fn({'x': x}, y) 

def forward(x, params, mode): 

    layers = [tf.nn.rnn_cell.LSTMCell(n_neurons) for _ in range(n_layers)] 
    cells = tf.nn.rnn_cell.MultiRNNCell(layers) 
    outputs, state = tf.nn.dynamic_rnn(cells, x) 

    predictions = ... 

    return predictions 

def model_fn(features, labels, mode, params): 
    predict = forward(features, params, mode) 

    return tf.estimator.EstimatorSpec(predict , ...) 

def experiment_fn(config, params): 
    return learn.Experiment(
     estimator = estimator(model_fn,...), 
     train_input_fn = lambda: input_fun(train_set), 
     eval_input_fn = lambda: input_fun(eval_set)) 

それは次のことをスロー：

Traceback (most recent call last):

File "", line 1, in runfile('/Experiment.py', wdir='/TensorFlow')

File "C:\Users\hp\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile execfile(filename, namespace)

File "C:\Users\hp\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile exec(compile(f.read(), filename, 'exec'), namespace)

File "/Experiment.py", line 490, in hparams = params

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\learn_runner.py", line 218, in run return _execute_schedule(experiment, schedule)

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\learn_runner.py", line 46, in _execute_schedule return task()

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\experiment.py", line 367, in train hooks=self._train_monitors + extra_hooks)

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\experiment.py", line 807, in _call_train hooks=hooks)

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 302, in train loss = self._train_model(input_fn, hooks, saving_listeners)

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 711, in _train_model features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 694, in _call_model_fn model_fn_results = self._model_fn(features=features, **kwargs)

File "/Experiment.py", line 350, in model_fn predict = forward(features, params, mode)

File "/Experiment.py", line 335, in forward dtype = tf.float32

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py", line 562, in dynamic_rnn flat_input = [ops.convert_to_tensor(input_) for input_ in flat_input]

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\ops\rnn.py", line 562, in flat_input = [ops.convert_to_tensor(input_) for input_ in flat_input]

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 836, in convert_to_tensor as_ref=False)

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 926, in internal_convert_to_tensor ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py", line 229, in _constant_tensor_conversion_function return constant(v, dtype=dtype, name=name)

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py", line 208, in constant value, dtype=dtype, shape=shape, verify_shape=verify_shape))

File "C:\Users\hp\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 472, in make_tensor_proto "supported type." % (type(values), values))

TypeError: Failed to convert object of type <class 'function'> to Tensor. Contents: <function numpy_input_fn.<locals>.input_fn at 0x000001AB2B1DBEA0>. Consider casting elements to a supported type.

誰もがなぜ知っていますか？

Answer 1

あなたはMultiRNNCellへの細胞のリストを渡す必要があります：あなたは本当に1層RNNを作りたい場合は、

Args:

cells: list of RNNCells that will be composed in this order.

state_is_tuple: If True, accepted and returned states are n-tuples, where n = len(cells) . If False, the states are all concatenated along the column axis. This latter behavior will soon be deprecated.

cells = tf.nn.rnn_cell.MultiRNNCell([layers]) 

にコードを変更...または作成以上層。

Answer 2

私にも同様の問題がありました。私のケースでは、私のモデル（私はあなたのケースでは "forward"と思われる）xがTensorとして使われていたので例外が発生しましたが、実際は関数（特にtf.estimator.inputs.numpy_input_fn）でした。 私はこれを追加することによって、それを考え出した：

<function numpy_input_fn.<locals>.input_fn at 0x7fcc6f065740> 
<class 'function'> 

、私はまだそれを解決するための正しい方法は何であるかわからないんだけど、私は修正することができた。このような何かを印刷

print(x) 
print(type(x)) 

これに似た何かを行うことによって、それ：

input_dict, y = x() 
x = input_dict['x'] 

は、それがラインを除いてスロー