teratail
回答率
85.30%
teratail header banner
teratail header banner
質問するログイン新規登録
トップに関する質問mnist オートエンコーダについて

編集履歴

質問編集履歴

1

Tracebackを追加しました

2021/07/24 17:41

投稿

tkrd
tkrd

スコア5

title CHANGED
File without changes
body CHANGED
@@ -1,5 +1,97 @@
1
1
  閲覧ありがとうございます。kerasでmnistのオートエンコードの多層化を試していたところ、不明なエラーが出てしまったため、質問いたしました。回答いただけると幸いです。
2
2
 
3
+ ```Traceback
4
+ in <module>()
5
+ 27 decoder_layer = autoencoder.layers[-1]
6
+ 28 # encodeされた画像データを再構成する部分
7
+ ---> 29 decoder = Model(encoded_input, decoder_layer(encoded_input))
8
+ 30
9
+ 31 # AdaDeltaで最適化, loss関数はbinary_crossentropy
10
+
11
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
12
+ 968 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
13
+ 969 return self._functional_construction_call(inputs, args, kwargs,
14
+ --> 970 input_list)
15
+ 971
16
+ 972 # Maintains info about the `Layer.call` stack.
17
+
18
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
19
+ 1106 # Check input assumptions set after layer building, e.g. input shape.
20
+ 1107 outputs = self._keras_tensor_symbolic_call(
21
+ -> 1108 inputs, input_masks, args, kwargs)
22
+ 1109
23
+ 1110 if outputs is None:
24
+
25
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
26
+ 838 return nest.map_structure(keras_tensor.KerasTensor, output_signature)
27
+ 839 else:
28
+ --> 840 return self._infer_output_signature(inputs, args, kwargs, input_masks)
29
+ 841
30
+ 842 def _infer_output_signature(self, inputs, args, kwargs, input_masks):
31
+
32
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
33
+ 878 self._maybe_build(inputs)
34
+ 879 inputs = self._maybe_cast_inputs(inputs)
35
+ --> 880 outputs = call_fn(inputs, *args, **kwargs)
36
+ 881
37
+ 882 self._handle_activity_regularization(inputs, outputs)
38
+
39
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/core.py in call(self, inputs)
40
+ 1240 self.kernel, ids, weights, combiner='sum')
41
+ 1241 else:
42
+ -> 1242 outputs = gen_math_ops.MatMul(a=inputs, b=self.kernel)
43
+ 1243 # Broadcast kernel to inputs.
44
+ 1244 else:
45
+
46
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/tf_export.py in wrapper(*args, **kwargs)
47
+ 402 'Please pass these args as kwargs instead.'
48
+ 403 .format(f=f.__name__, kwargs=f_argspec.args))
49
+ --> 404 return f(**kwargs)
50
+ 405
51
+ 406 return tf_decorator.make_decorator(f, wrapper, decorator_argspec=f_argspec)
52
+
53
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_math_ops.py in mat_mul(a, b, transpose_a, transpose_b, name)
54
+ 5716 _, _, _op, _outputs = _op_def_library._apply_op_helper(
55
+ 5717 "MatMul", a=a, b=b, transpose_a=transpose_a, transpose_b=transpose_b,
56
+ -> 5718 name=name)
57
+ 5719 _result = _outputs[:]
58
+ 5720 if _execute.must_record_gradient():
59
+
60
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(op_type_name, name, **keywords)
61
+ 748 op = g._create_op_internal(op_type_name, inputs, dtypes=None,
62
+ 749 name=scope, input_types=input_types,
63
+ --> 750 attrs=attr_protos, op_def=op_def)
64
+ 751
65
+ 752 # `outputs` is returned as a separate return value so that the output
66
+
67
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
68
+ 599 return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
69
+ 600 op_type, captured_inputs, dtypes, input_types, name, attrs, op_def,
70
+ --> 601 compute_device)
71
+ 602
72
+ 603 def capture(self, tensor, name=None, shape=None):
73
+
74
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
75
+ 3563 input_types=input_types,
76
+ 3564 original_op=self._default_original_op,
77
+ -> 3565 op_def=op_def)
78
+ 3566 self._create_op_helper(ret, compute_device=compute_device)
79
+ 3567 return ret
80
+
81
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
82
+ 2040 op_def = self._graph._get_op_def(node_def.op)
83
+ 2041 self._c_op = _create_c_op(self._graph, node_def, inputs,
84
+ -> 2042 control_input_ops, op_def)
85
+ 2043 name = compat.as_str(node_def.name)
86
+ 2044
87
+
88
+ /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def)
89
+ 1881 except errors.InvalidArgumentError as e:
90
+ 1882 # Convert to ValueError for backwards compatibility.
91
+ -> 1883 raise ValueError(str(e))
92
+ 1884
93
+ 1885 return c_op
94
+ ```
3
95
  ```Error
4
96
  ValueError: Dimensions must be equal, but are 32 and 128 for '{{node dense_5/MatMul}} = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false](Placeholder, dense_5/MatMul/ReadVariableOp)' with input shapes: [?,32], [128,784].
5
97
  ```