我正在运行网站上的示例：http://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/
from pandas import DataFrame
from pandas import Series
from pandas import concat
from pandas import read_csv
from pandas import datetime
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from math import sqrt
from matplotlib import pyplot
import numpy
# date-time parsing function for loading the dataset
def parser(x):
return datetime.strptime('190'+x, '%Y-%m')
# frame a sequence as a supervised learning problem
def timeseries_to_supervised(data, lag=1):
df = DataFrame(data)
columns = [df.shift(i) for i in range(1, lag+1)]
columns.append(df)
df = concat(columns, axis=1)
df.fillna(0, inplace=True)
return df
# create a differenced series
def difference(dataset, interval=1):
diff = list()
for i in range(interval, len(dataset)):
value = dataset[i] - dataset[i - interval]
diff.append(value)
return Series(diff)
# invert differenced value
def inverse_difference(history, yhat, interval=1):
return yhat + history[-interval]
# scale train and test data to [-1, 1]
def scale(train, test):
# fit scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
# transform train
train = train.reshape(train.shape[0], train.shape[1])
train_scaled = scaler.transform(train)
# transform test
test = test.reshape(test.shape[0], test.shape[1])
test_scaled = scaler.transform(test)
return scaler, train_scaled, test_scaled
# inverse scaling for a forecasted value
def invert_scale(scaler, X, value):
new_row = [x for x in X] + [value]
array = numpy.array(new_row)
array = array.reshape(1, len(array))
inverted = scaler.inverse_transform(array)
return inverted[0, -1]
# fit an LSTM network to training data
def fit_lstm(train, batch_size, nb_epoch, neurons):
X, y = train[:, 0:-1], train[:, -1]
X = X.reshape(X.shape[0], 1, X.shape[1])
model = Sequential()
model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
for i in range(nb_epoch):
model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)
model.reset_states()
return model
# make a one-step forecast
def forecast_lstm(model, batch_size, X):
X = X.reshape(1, 1, len(X))
yhat = model.predict(X, batch_size=batch_size)
return yhat[0,0]
# load dataset
series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
# transform data to be stationary
raw_values = series.values
diff_values = difference(raw_values, 1)
# transform data to be supervised learning
supervised = timeseries_to_supervised(diff_values, 1)
supervised_values = supervised.values
# split data into train and test-sets
train, test = supervised_values[0:-12], supervised_values[-12:]
# transform the scale of the data
scaler, train_scaled, test_scaled = scale(train, test)
# repeat experiment
repeats = 30
error_scores = list()
for r in range(repeats):
# fit the model
lstm_model = fit_lstm(train_scaled, 1, 3000, 4)
# forecast the entire training dataset to build up state for forecasting
train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)
lstm_model.predict(train_reshaped, batch_size=1)
# walk-forward validation on the test data
predictions = list()
for i in range(len(test_scaled)):
# make one-step forecast
X, y = test_scaled[i, 0:-1], test_scaled[i, -1]
yhat = forecast_lstm(lstm_model, 1, X)
# invert scaling
yhat = invert_scale(scaler, X, yhat)
# invert differencing
yhat = inverse_difference(raw_values, yhat, len(test_scaled)+1-i)
# store forecast
predictions.append(yhat)
# report performance
rmse = sqrt(mean_squared_error(raw_values[-12:], predictions))
print('%d) Test RMSE: %.3f' % (r+1, rmse))
error_scores.append(rmse)
# summarize results
results = DataFrame()
results['rmse'] = error_scores
print(results.describe())
results.boxplot()
pyplot.show()
但是出现了以下错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-9-a64098fe2161> in <module>()
100 for r in range(repeats):
101 # fit the model
--> 102 lstm_model = fit_lstm(train_scaled, 1, 3000, 4)
103 # forecast the entire training dataset to build up state for forecasting
104 train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)
<ipython-input-9-a64098fe2161> in fit_lstm(train, batch_size, nb_epoch, neurons)
64 X = X.reshape(X.shape[0], 1, X.shape[1])
65 model = Sequential()
---> 66 model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))
67 model.add(Dense(1))
68 model.compile(loss='mean_squared_error', optimizer='adam')
/usr/local/lib/python3.4/dist-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:
/usr/local/lib/python3.4/dist-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)):
/usr/local/lib/python3.4/dist-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)
/usr/local/lib/python3.4/dist-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
/usr/local/lib/python3.4/dist-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
/usr/local/lib/python3.4/dist-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))
/usr/local/lib/python3.4/dist-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:
/usr/local/lib/python3.4/dist-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
/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/array_ops.py in concat(concat_dim, values, name)
865 ops.convert_to_tensor(concat_dim,
866 name="concat_dim",
--> 867 dtype=dtypes.int32).get_shape(
868 ).assert_is_compatible_with(tensor_shape.scalar())
869 return identity(values[0], name=scope)
/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
655
656 if ret is None:
--> 657 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
658
659 if ret is NotImplemented:
/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
178 as_ref=False):
179 _ = as_ref
--> 180 return constant(v, dtype=dtype, name=name)
181
182
/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
161 tensor_value = attr_value_pb2.AttrValue()
162 tensor_value.tensor.CopyFrom(
--> 163 tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
164 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
165 const_tensor = g.create_op(
/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape)
351 nparray = np.empty(shape, dtype=np_dt)
352 else:
--> 353 _AssertCompatible(values, dtype)
354 nparray = np.array(values, dtype=np_dt)
355 # check to them.
/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/tensor_util.py in _AssertCompatible(values, dtype)
288 else:
289 raise TypeError("Expected %s, got %s of type '%s' instead." %
--> 290 (dtype.name, repr(mismatch), type(mismatch).__name__))
291
292
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
我看过网站讨论，知道某些旧版本的keras有这样的问题，所以我更新到Keras == 2.0.6但仍然有同样的问题......
知道可以采取哪些措施来解决此错误？谢谢！
答案 0 :(得分：1)
你的TensorFlow太旧了，你至少应该试试TensorFlow 1.1。我相信Keras 2.0至少需要TensorFlow 1.0。
答案 1 :(得分：-1)
我这样解决了这个问题：
我只在连接（张量，轴）中使用tensorflow_backend.py
更改此内容：
return tf.concat([to_dense(x) for x in tensors], axis)
对此：
return tf.concat(axis,[to_dense(x) for x in tensors])