我正在设计一个LSTM自动编码器。您可能会看到下面的编码层是一个LSTM架构，一旦我有一个嵌入式矢量，我将使用DNN层对其进行解码。在解码嵌入式矢量之前，我想使用一个丢失层来防止过度拟合。我按照Keras文档中的说明操作，但收到了TypeError: Failed to convert object of type <type 'tuple'> to Tensor. Contents: (None, 1, 128). Consider casting elements to a supported type.
def LSTMautoencoder_with_node_features(dataset, adj, feats):
aug_adj = sp.hstack([adj, feats])
input_dim = aug_adj.shape[1]
timesteps = 8
latent_dim = 128
data = Input(shape=(timesteps, input_dim))
encoded = LSTM(latent_dim,
name='encoded1',
dropout=0.5,
recurrent_dropout=0.5,
return_sequences=True)(data)
encoder = Model([data], encoded)
#### The error happens here. ####
decoded = Dropout(0.5, noise_shape=(None, 1, latent_dim))(encoded)
################################
decoded = TimeDistributed(Dense(input_dim))(encoded)
...
错误消息发布在下面：
In [5]: encoder, ae = LSTMautoencoder_with_node_features(dataset, adj, feats)
...:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-84133d8a0b2d> in <module>()
----> 1 encoder, ae = LSTMautoencoder_with_node_features(dataset, adj, feats)
<ipython-input-1-ba7fb4525efe> in LSTMautoencoder_with_node_features(dataset, adj, feats)
286 encoder = Model([data], encoded)
287
--> 288 decoded = Dropout(0.5, noise_shape=(None, 1, latent_dim))(encoded)
289 decoded = TimeDistributed(Dense(input_dim))(encoded)
290
/home/qy/anaconda2/lib/python2.7/sitepackages/keras/engine/topology.pyc in __call__(self, inputs, **kwargs)
594
595 # Actually call the layer, collecting output(s), mask(s), and shape(s).
--> 596 output = self.call(inputs, **kwargs)
597 output_mask =self.compute_mask(inputs,previous_mask)
598
/home/qy/anaconda2/lib/python2.7/site-packages/keras/layers/core.pyc in call(self, inputs, training)
109 seed=self.seed)
110 return K.in_train_phase(dropped_inputs, inputs,
--> 111 training=training)
112 return inputs
113
/home/qy/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in in_train_phase(x, alt, training)
2618
2619 # else: assume learning phase is a placeholder tensor.
-> 2620 x = switch(training, x, alt)
2621 if uses_learning_phase:
2622 x._uses_learning_phase = True
/home/qy/anaconda2/lib/python2.7/sitepackages/keras/backend/tensorflow_backend.pyc in switch(condition, then_expression, else_expression)
2577 x = tf.cond(condition,
2578 then_expression_fn,
-> 2579 else_expression_fn)
2580 return x
2581
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/util/deprecation.pyc in new_func(*args, **kwargs)
287 'in a future version' if date is None else ('after %s' % date),
288 instructions)
--> 289 return func(*args, **kwargs)
290 return tf_decorator.make_decorator(func, new_func, 'deprecated',
291 _add_deprecated_arg_notice_to_docstring(
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/ops/control_flow_ops.pyc in cond(pred, true_fn, false_fn, strict, name, fn1, fn2)
1812 context_t = CondContext(pred, pivot_1, branch=1)
1813 context_t.Enter()
-> 1814 orig_res_t, res_t = context_t.BuildCondBranch(true_fn)
1815 if orig_res_t is None:
1816 raise ValueError("true_fn must have a return value.")
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/ops/control_fl$w_ops.pyc in BuildCondBranch(self, fn)
1687 def BuildCondBranch(self, fn):
1688 """Add the subgraph defined by fn() to the graph."""
-> 1689 original_result = fn()
1690 if original_result is None:
1691 return None, None
/home/qy/anaconda2/lib/python2.7/site-packages/keras/layers/core.pyc in dropped$inputs()
107 def dropped_inputs():
108 return K.dropout(inputs, self.rate, noise_shape,
--> 109 seed=self.seed)
110 return K.in_train_phase(dropped_inputs, inputs,
111 training=training)
/home/qy/anaconda2/lib/python2.7/sitepackages/keras/backend/tensorflow_backend$pyc in dropout(x, level, noise_shape, seed)
2878 # the dummy 1. works around a TF bug
2879 # (float32_ref vs. float32 incompatibility)
-> 2880 return tf.nn.dropout(x * 1., retain_prob, noise_shape, seed=seed)
2881
2882
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/ops/nn_ops.pyc in dropout(x, keep_prob, noise_shape, seed, name)
1910 random_tensor += random_ops.random_uniform(noise_shape,
1911 seed=seed,
-> 1912 dtype=x.dtype)
1913 # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
1914 binary_tensor = math_ops.floor(random_tensor)
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/ops/random_ops$pyc in random_uniform(shape, minval, maxval, dtype, seed, name)
225 maxval = 1
226 with ops.name_scope(name, "random_uniform", [shape, minval, maxval]) $s name:
--> 227 shape = _ShapeTensor(shape)
228 minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
229 maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/ops/random_ops$pyc in _ShapeTensor(shape)
40 else:
41 dtype = None
---> 42 return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
43
44
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/framework/ops.$yc in convert_to_tensor(value, dtype, name, preferred_dtype)
674 name=name,
675 preferred_dtype=preferred_dtype,
--> 676 as_ref=False)
677
678
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/framework/ops.pyc in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
739
740 if ret is None:
--> 741 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
742
743 if ret is NotImplemented:
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/framework/constant_op.pyc in _constant_tensor_conversion_function(v, dtype, name, as_ref)
111 as_ref=False):
112 _ = as_ref
--> 113 return constant(v, dtype=dtype, name=name)
114
115
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/framework/constant_op.pyc in constant(value, dtype, shape, name, verify_shape)
100 tensor_value = attr_value_pb2.AttrValue()
101 tensor_value.tensor.CopyFrom(
--> 102 tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
103 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
104 const_tensor = g.create_op(
/home/qy/anaconda2/lib/python2.7/sitepackages/tensorflow/python/framework/tensor_util.pyc in make_tensor_proto(values, dtype, shape, verify_shape)
460 raise TypeError("Failed to convert object of type %s to Tensor. "
461 "Contents: %s. Consider casting elements to a "
--> 462 "supported type." % (type(values), values))
463 tensor_proto.string_val.extend(str_values)
464 return tensor_proto
TypeError: Failed to convert object of type <type 'tuple'> to Tensor. Contents: (None, 1, 128). Consider casting elements to a supported type.
答案 0 :(得分：0)
辍学噪音形状不能有无尺寸。