我有顺序数据，其中每个元素都是一个向量，如下所示：
x_i = [ 0. , 0. , 0. , 0.03666667, 0. ,
0. , 0.95666667, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.00666667, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ]
向量表示用户在一组活动上花费的时间分布（例如，超过5分钟的块）。任务是在给定前N个步骤（t-N：t）的情况下预测下一个时间步骤t + 1的任务分布。因此，我的输入形状是：
X.shape =（batch_size，timesteps，input_length），一个例子是（32,10,41），其中我们的批量大小为32，过去10个步骤，每个元素的维数为41
要做到这一点，我使用的是使用Keras构建的LSTM。在将此输入传递给LSTM之前，我想创建类似于嵌入层的东西，将此表示转换为类似于在NLP中完成的密集高维向量，并使用单个热字向量嵌入到嵌入空间中嵌入层。然而，Keras中的嵌入层只接受整数输入（或一个热表示），在我的情况下，我想要实现的是输入向量X（由几个x_i组成，因为它代表时间 - 之间的矩阵乘积 - 系列数据）和嵌入矩阵V.举例说明：
X.shape =（10,41） 嵌入矩阵形状=（41,100）
这个角色是通过矩阵乘法将X中的每个元素从它的41维稀疏表示转换为100维，这应该对批输入中的所有元素进行。
为此，我做了以下
class EmbeddingMatrix(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(EmbeddingMatrix, self).__init__(**kwargs)
def build(self, input_shape):
# Create a trainable weight variable for this layer.
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[2], self.output_dim),
initializer='uniform',
trainable=True)
super(EmbeddingMatrix, self).build(input_shape) # Be sure to call this somewhere!
def call(self, x, mask=None):
return K.dot(x, self.kernel)
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1], self.output_dim)
我正在使用的LSTM网络如下：
inputs = Input(shape=(FLAGS.look_back, FLAGS.inputlength))
inputs_embedded = EmbeddingMatrix(N_EMBEDDING)(inputs)
encoded = LSTM(N_HIDDEN, dropout=0.2, recurrent_dropout=0.2)(inputs_embedded)
dense = TimeDistributed(Dense(N_DENSE, activation='sigmoid'))(dropout)
dense_output = TimeDistributed(Dense(FLAGS.inputlength, activation='softmax'))(dense)
embedder = Model(inputs, inputs_embedded)
model = Model(inputs, dense_output)
model.compile(loss='mean_squared_error', optimizer = RMSprop(lr=LEARNING_RATE, clipnorm=5))
但是，在运行时我收到以下错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-24-5a28b4f3b6b9> in <module>()
5 inputs_embedded = EmbeddingMatrix(N_EMBEDDING)(inputs)
6
----> 7 encoded = LSTM(N_HIDDEN, dropout=0.2, recurrent_dropout=0.2)(inputs_embedded)
8
9 dense = TimeDistributed(Dense(N_DENSE, activation='sigmoid'))(dropout)
/Users/asturkmani/anaconda3/lib/python3.5/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)):
/Users/asturkmani/anaconda3/lib/python3.5/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)
/Users/asturkmani/anaconda3/lib/python3.5/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
/Users/asturkmani/anaconda3/lib/python3.5/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
/Users/asturkmani/anaconda3/lib/python3.5/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))
/Users/asturkmani/anaconda3/lib/python3.5/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:
/Users/asturkmani/anaconda3/lib/python3.5/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
/Users/asturkmani/anaconda3/lib/python3.5/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)
/Users/asturkmani/anaconda3/lib/python3.5/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:
/Users/asturkmani/anaconda3/lib/python3.5/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
/Users/asturkmani/anaconda3/lib/python3.5/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(
/Users/asturkmani/anaconda3/lib/python3.5/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.
/Users/asturkmani/anaconda3/lib/python3.5/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.
可能导致这种情况的原因以及实现这种加权嵌入矩阵的最佳方法是什么？