嗨，任何人都可以帮我解决这个错误，我似乎在搜索文档但是无济于事。
目的是预测时间序列。我使用了伪数据df = df.reset_index()
df.drop('index', axis = 1, inplace=True)
index = df.index[df["Country"] == "Republic of Korea"]
df.set_value(index, "Country", "South Korea")
df = df.set_index("Country")
df["Country"] = df.index
。我希望shape = (N, timesteps, features)从predict x_2 x_1, x_3 x_2来x_11 x_10 LSTM使用import numpy as np
N = 13*12;
T = 10;
F = 3;
X = np.random.rand(N, T, F);
Y = np.random.rand(N, 1, F);
Y = np.concatenate((X[:,1:T,:], Y), axis=1);
import keras
from keras.models import Model
from keras.layers import Dense, Input, LSTM, Lambda, concatenate, Dropout
from keras.optimizers import Adam, SGD
from keras import regularizers
from keras.metrics import categorical_accuracy
from keras.models import load_model
input_ = Input(shape = (T, F), name ='input');
x = Dense(15, activation='sigmoid', name='fc1')(input_);
x = LSTM(25, return_sequences=True, activation='tanh', name='lstm')(x);
x = Dense(F, activation='sigmoid', name='fc2')(x);
model = Model(input_, x, name='dummy');
model.compile(optimizer='rmsprop', loss='mse', metrics=['accuracy']);
print(model.input_shape); print(X.shape);
print(model.output_shape); print(Y.shape);
print(model.summary());
model.fit(X, Y, batch_size = 13, epochs=30, validation_split=0.20, shuffle=False);
Using Theano backend.
(None, 10, 3)
(156, 10, 3)
(None, 10, 3)
(156, 10, 3)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) (None, 10, 3) 0
_________________________________________________________________
fc1 (Dense) (None, 10, 15) 60
_________________________________________________________________
lstm (LSTM) (None, 10, 25) 4100
_________________________________________________________________
fc2 (Dense) (None, 10, 3) 78
=================================================================
Total params: 4,238
Trainable params: 4,238
Non-trainable params: 0
_________________________________________________________________
None
Train on 124 samples, validate on 32 samples
Epoch 1/30
Traceback (most recent call last):
File "C:\Anaconda3\lib\site-packages\theano\compile\function_module.py", line 903, in __call__
self.fn() if output_subset is None else\
ValueError: Input dimension mis-match. (input[0].shape[1] = 10, input[1].shape[1] = 15)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "b.py", line 34, in <module>
model.fit(X, Y, batch_size = 13, epochs=30, validation_split=0.20, shuffle=False);
File "C:\Anaconda3\lib\site-packages\keras\engine\training.py", line 1498, in fit
initial_epoch=initial_epoch)
File "C:\Anaconda3\lib\site-packages\keras\engine\training.py", line 1152, in _fit_loop
outs = f(ins_batch)
File "C:\Anaconda3\lib\site-packages\keras\backend\theano_backend.py", line 1158, in __call__
return self.function(*inputs)
File "C:\Anaconda3\lib\site-packages\theano\compile\function_module.py", line 917, in __call__
storage_map=getattr(self.fn, 'storage_map', None))
File "C:\Anaconda3\lib\site-packages\theano\gof\link.py", line 325, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "C:\Anaconda3\lib\site-packages\six.py", line 692, in reraise
raise value.with_traceback(tb)
File "C:\Anaconda3\lib\site-packages\theano\compile\function_module.py", line 903, in __call__
self.fn() if output_subset is None else\
ValueError: Input dimension mis-match. (input[0].shape[1] = 10, input[1].shape[1] = 15)
Apply node that caused the error: Elemwise{Add}[(0, 0)](Reshape{3}.0, InplaceDimShuffle{x,0,x}.0)
Toposort index: 98
Inputs types: [TensorType(float32, 3D), TensorType(float32, (True, False, True))]
Inputs shapes: [(13, 10, 15), (1, 15, 1)]
Inputs strides: [(600, 60, 4), (60, 4, 4)]
Inputs values: ['not shown', 'not shown']
Outputs clients: [[Reshape{2}(Elemwise{Add}[(0, 0)].0, TensorConstant{[-1 15]}), Elemwise{Composite{((i0 + i1 + i2
+ i3) * scalar_sigmoid(i4) * (i5 - scalar_sigmoid(i4)))}}[(0, 0)](Reshape{3}.0, Reshape{3}.0, Reshape{3}.0, Reshape
{3}.0, Elemwise{Add}[(0, 0)].0, TensorConstant{(1, 1, 1) of 1.0})]]
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created.
This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizati
ons can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.
（欢迎提出更好的建议）。输出（下图）显示了看似正确的预期输出形状。但是，该错误提到输入维度不匹配。根据文档，我似乎无法找到问题。
new错误来自
original我无法理解为什么输入形状在错误中是（1,15,1）的错误以及theano提到的2个输入是什么？
我使用的theano版本是0.9.0，keras版本是2.0.4。如果我不使用任何功能（F），代码将顺利运行。
编辑1 ：批量大小为13，只是为了清除错误日志。删除它也会产生完全相同的错误。