keras：TypeError：預期的int32，得到包含'_Message'類型張量的列表而不是

我正在學習使用由Keras實現的神經網絡的時間序列分析。這裏是鏈接到數據集：airline_passanger_dataset：keras：TypeError：預期的int32，得到包含'_Message'類型張量的列表而不是

https://datamarket.com/data/set/22u3/international-airline-passengers-monthly-totals-in-thousands-jan-49-dec-60#!ds=22u3&display=line

代碼是：

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)

在這裏，我遇到了一個錯誤：

--------------------------------------------------------------------------- 
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.

我的Python版本是3.5.2，tensorflow版本是0.12.0，keras版本是2.0.6。

我試圖在線1039和1042更新tensorflow_backend.py （https://github.com/fchollet/keras/blob/master/keras/backend/tensorflow_backend.py）tf.concat語法來回此鏈接：從

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

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

到

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

的錯誤仍然是相同的：

--------------------------------------------------------------------------- 
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.

有人可以幫我用正確的代碼嗎？並解釋我做錯了什麼？謝謝。

來源

2017-07-23 Ashish Menkudale

你的TensorFlow太舊了，請升級它。 –

你會推薦什麼python和tensorflow的組合？我有一個Windows系統。我只能用CPU版本炒作。我不得不嘗試不同的python版本和tensorflow版本才能安裝tensorflow軟件包，直到找到這個工作組合。（py 3.5.2和tf r0.12）謝謝。 –

我會建議你不要使用Windows :)，所以你完全避免了這些問題 –

產生此錯誤是因爲您的TF太舊。 0.12是很久以前發佈的。你應該更新它到最新版本，特別是當你使用Keras 2.0.x時

來源

2017-07-24 19:32:22

keras：TypeError：預期的int32，得到包含'_Message'類型張量的列表而不是

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