问keras: TypeError:预期的int32，获取包含类型为'_Message‘的张量的列表

EN

import numpy
import matplotlib.pyplot as plt
import pandas
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error

# fix random seed for reproducibility
numpy.random.seed(7)

# load the dataset
dataframe = pandas.read_csv('C:/users/dell/downloads/international-airline-passengers.csv', usecols=[1], engine='python', skipfooter=3)
dataset = dataframe.values
dataset = dataset.astype('float32')

# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# split into train and test sets
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
print(len(train), len(test))

# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return numpy.array(dataX), numpy.array(dataY)

# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-40-38b1a98ab6de> in <module>()
      1 # create and fit the LSTM network
      2 model = Sequential()
----> 3 model.add(LSTM(4, input_shape=(1, look_back)))
      4 model.add(Dense(1))
      5 model.compile(loss='mean_squared_error', optimizer='adam')

C:\Program Files\Anaconda3\lib\site-packages\keras\models.py in add(self, layer)
    434                 # and create the node connecting the current layer
    435                 # to the input layer we just created.
--> 436                 layer(x)
    437 
    438             if len(layer.inbound_nodes) != 1:

C:\Program Files\Anaconda3\lib\site-packages\keras\layers\recurrent.py in __call__(self, inputs, initial_state, **kwargs)
    260         # modify the input spec to include the state.
    261         if initial_state is None:
--> 262             return super(Recurrent, self).__call__(inputs, **kwargs)
    263 
    264         if not isinstance(initial_state, (list, tuple)):

C:\Program Files\Anaconda3\lib\site-packages\keras\engine\topology.py in __call__(self, inputs, **kwargs)
    567                                          '`layer.build(batch_input_shape)`')
    568                 if len(input_shapes) == 1:
--> 569                     self.build(input_shapes[0])
    570                 else:
    571                     self.build(input_shapes)

C:\Program Files\Anaconda3\lib\site-packages\keras\layers\recurrent.py in build(self, input_shape)
   1041                                         initializer=bias_initializer,
   1042                                         regularizer=self.bias_regularizer,
-> 1043                                         constraint=self.bias_constraint)
   1044         else:
   1045             self.bias = None

C:\Program Files\Anaconda3\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
     85                 warnings.warn('Update your `' + object_name +
     86                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87             return func(*args, **kwargs)
     88         wrapper._original_function = func
     89         return wrapper

C:\Program Files\Anaconda3\lib\site-packages\keras\engine\topology.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
    389         if dtype is None:
    390             dtype = K.floatx()
--> 391         weight = K.variable(initializer(shape), dtype=dtype, name=name)
    392         if regularizer is not None:
    393             self.add_loss(regularizer(weight))

C:\Program Files\Anaconda3\lib\site-packages\keras\layers\recurrent.py in bias_initializer(shape, *args, **kwargs)
   1033                         self.bias_initializer((self.units,), *args, **kwargs),
   1034                         initializers.Ones()((self.units,), *args, **kwargs),
-> 1035                         self.bias_initializer((self.units * 2,), *args, **kwargs),
   1036                     ])
   1037             else:

C:\Program Files\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in concatenate(tensors, axis)
   1721         return tf.sparse_concat(axis, tensors)
   1722     else:
-> 1723         return tf.concat([to_dense(x) for x in tensors], axis)
   1724 
   1725 

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py in concat(concat_dim, values, name)
   1073       ops.convert_to_tensor(concat_dim,
   1074                             name="concat_dim",
-> 1075                             dtype=dtypes.int32).get_shape(
   1076                             ).assert_is_compatible_with(tensor_shape.scalar())
   1077       return identity(values[0], name=scope)

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
    667 
    668         if ret is None:
--> 669           ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    670 
    671         if ret is NotImplemented:

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
    174                                          as_ref=False):
    175   _ = as_ref
--> 176   return constant(v, dtype=dtype, name=name)
    177 
    178 

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in constant(value, dtype, shape, name, verify_shape)
    163   tensor_value = attr_value_pb2.AttrValue()
    164   tensor_value.tensor.CopyFrom(
--> 165       tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
    166   dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
    167   const_tensor = g.create_op(

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
    365       nparray = np.empty(shape, dtype=np_dt)
    366     else:
--> 367       _AssertCompatible(values, dtype)
    368       nparray = np.array(values, dtype=np_dt)
    369       # check to them.

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_util.py in _AssertCompatible(values, dtype)
    300     else:
    301       raise TypeError("Expected %s, got %s of type '%s' instead." %
--> 302                       (dtype.name, repr(mismatch), type(mismatch).__name__))
    303 
    304 

TypeError: Expected int32, got list containing Tensors of type '_Message' instead.

y = tf.reshape(y, tf.concat([tf.shape(y), [1] * (diff)], axis=0))

y = tf.reshape(y, tf.concat(values = [tf.shape(y), [1] * (diff)], axis=0))

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-48-38b1a98ab6de> in <module>()
      1 # create and fit the LSTM network
      2 model = Sequential()
----> 3 model.add(LSTM(4, input_shape=(1, look_back)))
      4 model.add(Dense(1))
      5 model.compile(loss='mean_squared_error', optimizer='adam')

C:\Program Files\Anaconda3\lib\site-packages\keras\models.py in add(self, layer)
    434                 # and create the node connecting the current layer
    435                 # to the input layer we just created.
--> 436                 layer(x)
    437 
    438             if len(layer.inbound_nodes) != 1:

C:\Program Files\Anaconda3\lib\site-packages\keras\layers\recurrent.py in __call__(self, inputs, initial_state, **kwargs)
    260         # modify the input spec to include the state.
    261         if initial_state is None:
--> 262             return super(Recurrent, self).__call__(inputs, **kwargs)
    263 
    264         if not isinstance(initial_state, (list, tuple)):

C:\Program Files\Anaconda3\lib\site-packages\keras\engine\topology.py in __call__(self, inputs, **kwargs)
    567                                          '`layer.build(batch_input_shape)`')
    568                 if len(input_shapes) == 1:
--> 569                     self.build(input_shapes[0])
    570                 else:
    571                     self.build(input_shapes)

C:\Program Files\Anaconda3\lib\site-packages\keras\layers\recurrent.py in build(self, input_shape)
   1041                                         initializer=bias_initializer,
   1042                                         regularizer=self.bias_regularizer,
-> 1043                                         constraint=self.bias_constraint)
   1044         else:
   1045             self.bias = None

C:\Program Files\Anaconda3\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
     85                 warnings.warn('Update your `' + object_name +
     86                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87             return func(*args, **kwargs)
     88         wrapper._original_function = func
     89         return wrapper

C:\Program Files\Anaconda3\lib\site-packages\keras\engine\topology.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
    389         if dtype is None:
    390             dtype = K.floatx()
--> 391         weight = K.variable(initializer(shape), dtype=dtype, name=name)
    392         if regularizer is not None:
    393             self.add_loss(regularizer(weight))

C:\Program Files\Anaconda3\lib\site-packages\keras\layers\recurrent.py in bias_initializer(shape, *args, **kwargs)
   1033                         self.bias_initializer((self.units,), *args, **kwargs),
   1034                         initializers.Ones()((self.units,), *args, **kwargs),
-> 1035                         self.bias_initializer((self.units * 2,), *args, **kwargs),
   1036                     ])
   1037             else:

C:\Program Files\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in concatenate(tensors, axis)
   1721         return tf.sparse_concat(axis, tensors)
   1722     else:
-> 1723         return tf.concat([to_dense(x) for x in tensors], axis)
   1724 
   1725 

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py in concat(concat_dim, values, name)
   1073       ops.convert_to_tensor(concat_dim,
   1074                             name="concat_dim",
-> 1075                             dtype=dtypes.int32).get_shape(
   1076                             ).assert_is_compatible_with(tensor_shape.scalar())
   1077       return identity(values[0], name=scope)

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
    667 
    668         if ret is None:
--> 669           ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    670 
    671         if ret is NotImplemented:

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
    174                                          as_ref=False):
    175   _ = as_ref
--> 176   return constant(v, dtype=dtype, name=name)
    177 
    178 

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in constant(value, dtype, shape, name, verify_shape)
    163   tensor_value = attr_value_pb2.AttrValue()
    164   tensor_value.tensor.CopyFrom(
--> 165       tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
    166   dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
    167   const_tensor = g.create_op(

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
    365       nparray = np.empty(shape, dtype=np_dt)
    366     else:
--> 367       _AssertCompatible(values, dtype)
    368       nparray = np.array(values, dtype=np_dt)
    369       # check to them.

C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_util.py in _AssertCompatible(values, dtype)
    300     else:
    301       raise TypeError("Expected %s, got %s of type '%s' instead." %
--> 302                       (dtype.name, repr(mismatch), type(mismatch).__name__))
    303 
    304 

TypeError: Expected int32, got list containing Tensors of type '_Message' instead.