我正在学习使用Keras实现的神经网络进行时间序列分析。这是指向数据集的链接：airline_passanger_dataset：
代码是：
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)
这里我遇到了一个错误：
---------------------------------------------------------------------------
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。
我尝试更新tensorflow_backend.py中的tf.concat语法 （https://github.com/fchollet/keras/blob/master/keras/backend/tensorflow_backend.py）在此链接上的第1039和1042行：
Tensorflow Slim: TypeError: Expected int32, got list containing Tensors of type '_Message' instead
来自
y = tf.reshape(y, tf.concat([tf.shape(y), [1] * (diff)], axis=0))
到
y = tf.reshape(y, tf.concat(values = [tf.shape(y), [1] * (diff)], axis=0))
错误仍然相同：
---------------------------------------------------------------------------
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.
有人可以用正确的代码帮我吗？并解释我做错了什么？谢谢。
答案 0 :(得分：0)
因为您的TF太旧而产生此错误。 0.12很久以前就被释放了。您应该将其更新到最新版本，特别是在使用Keras 2.0.x时