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대 집합 적 여과를 위해 더 낮게 정의 된 siamese 모델을 만들고 싶습니다. 첫 번째는 사용자의 삽입을 만들고 두 번째는 항목의 삽입을 만듭니다.Keras. 레이어를 연결합니다. TypeError
import keras
from keras import backend as K
from keras.layers import Input, Embedding, Dense, Flatten, concatenate
from keras.models import Model
n_users, n_items = 100, 3000
users_input = Input(shape=(n_users,), dtype='int32', name='users')
users_embedding = Embedding(output_dim=6, input_dim=n_users, input_length=1)(users_input)
users_flatten = Flatten()(users_embedding)
items_input = Input(shape=(n_items,), dtype='int32', name='items')
items_embedding = Embedding(output_dim=6, input_dim=n_items, input_length=1)(items_input)
items_flatten = Flatten()(items_embedding)
layer_0 = concatenate([users_flatten, items_flatten])
layer_1 = Dense(8, activation='relu')(layer_0)
layer_2 = Dense(1, activation='relu')(layer_1)
model = Model(inputs=[users_input, items_input], outputs=[layer_2])
위와 같이 연결에 문제가 있습니다.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-bf475de5f9cc> in <module>()
----> 1 layer_0 = concatenate([users_flatten, items_flatten])
2 layer_1 = Dense(8, activation='relu')(layer_0)
3 layer_2 = Dense(1, activation='relu')(layer_1)
/home/vladimir/anaconda2/lib/python2.7/site-packages/keras/layers/merge.pyc in concatenate(inputs, axis, **kwargs)
506 A tensor, the concatenation of the inputs alongside axis `axis`.
507 """
--> 508 return Concatenate(axis=axis, **kwargs)(inputs)
509
510
/home/vladimir/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc in __call__(self, inputs, **kwargs)
583
584 # Actually call the layer, collecting output(s), mask(s), and shape(s).
--> 585 output = self.call(inputs, **kwargs)
586 output_mask = self.compute_mask(inputs, previous_mask)
587
/home/vladimir/anaconda2/lib/python2.7/site-packages/keras/layers/merge.pyc in call(self, inputs)
281 raise ValueError('A `Concatenate` layer should be called '
282 'on a list of inputs.')
--> 283 return K.concatenate(inputs, axis=self.axis)
284
285 def compute_output_shape(self, input_shape):
/home/vladimir/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in concatenate(tensors, axis)
1679 return tf.sparse_concat(axis, tensors)
1680 else:
-> 1681 return tf.concat([to_dense(x) for x in tensors], axis)
1682
1683
/home/vladimir/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.pyc in concat(concat_dim, values, name)
998 ops.convert_to_tensor(concat_dim,
999 name="concat_dim",
-> 1000 dtype=dtypes.int32).get_shape(
1001 ).assert_is_compatible_with(tensor_shape.scalar())
1002 return identity(values[0], name=scope)
/home/vladimir/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc 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:
/home/vladimir/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/constant_op.pyc 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
/home/vladimir/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/constant_op.pyc 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(
/home/vladimir/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.pyc 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.
/home/vladimir/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.pyc 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.
내가 functional API에 대한 Keras의 documenation를 사용하는 예를 들어 : 여기 내 스택 추적입니다. 백엔드로 TensorFlow를 사용합니다.