質問編集履歴
5
間違ったエラー部分を削除しました。
test
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File without changes
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test
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@@ -44,12 +44,7 @@
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44
44
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() -> handle
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45
45
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46
46
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47
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-
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47
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+
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48
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-
```python
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49
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-
weights=[embeddings],
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50
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-
name='embedding')
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51
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-
self.rnn = SimpleRNN(hid_dim, name='rnn')
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52
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-
```
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53
48
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54
49
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ライブラリのtensorflow付近のリストは以下のようになっています。
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55
50
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4
Tracebackの内容を追記しました。
test
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File without changes
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test
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@@ -8,6 +8,41 @@
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8
8
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9
9
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TypeError: Unable to convert function return value to a Python type! The signature was
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10
10
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() -> handle
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11
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+
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12
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+
また、Tracebackの内容は以下です。
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13
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+
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14
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+
Traceback (most recent call last):
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15
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File "c:\Users\K21060066\Desktop\Python学習ファイル\自然言語処理入門\chapter09\models.py", line 1, in <module>
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16
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+
from tensorflow.keras.models import Model
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17
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\__init__.py", line 37, in <module>
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18
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+
from tensorflow.python.tools import module_util as _module_util
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19
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\__init__.py", line 42, in <module>
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20
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+
from tensorflow.python import data
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21
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\data\__init__.py", line 21, in <module>
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22
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+
from tensorflow.python.data import experimental
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23
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\data\experimental\__init__.py", line 95, in <module>
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24
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+
from tensorflow.python.data.experimental import service
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25
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\data\experimental\service\__init__.py", line 387, in <module>
|
|
26
|
+
from tensorflow.python.data.experimental.ops.data_service_ops import distribute
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27
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\data\experimental\ops\data_service_ops.py", line 23, in <module>
|
|
28
|
+
from tensorflow.python.data.experimental.ops import compression_ops
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29
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\data\experimental\ops\compression_ops.py", line 16, in <module>
|
|
30
|
+
from tensorflow.python.data.util import structure
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31
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\data\util\structure.py", line 22, in <module>
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32
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+
from tensorflow.python.data.util import nest
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33
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\data\util\nest.py", line 36, in <module>
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34
|
+
from tensorflow.python.framework import sparse_tensor as _sparse_tensor
|
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35
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\sparse_tensor.py", line 24, in <module>
|
|
36
|
+
from tensorflow.python.framework import constant_op
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37
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\constant_op.py", line 25, in <module>
|
|
38
|
+
from tensorflow.python.eager import execute
|
|
39
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\eager\execute.py", line 23, in <module>
|
|
40
|
+
from tensorflow.python.framework import dtypes
|
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41
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+
File "C:\Users\K21060066\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\dtypes.py", line 29, in <module>
|
|
42
|
+
_np_bfloat16 = _pywrap_bfloat16.TF_bfloat16_type()
|
|
43
|
+
TypeError: Unable to convert function return value to a Python type! The signature was
|
|
44
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+
() -> handle
|
|
45
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+
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11
46
|
|
|
12
47
|
エラーが生じているのは以下の部分です
|
|
13
48
|
```python
|
3
失礼いたしました。エラーが発生する部分を追記しました。
test
CHANGED
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File without changes
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test
CHANGED
|
@@ -8,6 +8,13 @@
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8
8
|
|
|
9
9
|
TypeError: Unable to convert function return value to a Python type! The signature was
|
|
10
10
|
() -> handle
|
|
11
|
+
|
|
12
|
+
エラーが生じているのは以下の部分です
|
|
13
|
+
```python
|
|
14
|
+
weights=[embeddings],
|
|
15
|
+
name='embedding')
|
|
16
|
+
self.rnn = SimpleRNN(hid_dim, name='rnn')
|
|
17
|
+
```
|
|
11
18
|
|
|
12
19
|
ライブラリのtensorflow付近のリストは以下のようになっています。
|
|
13
20
|
|
2
１枚目の画像の変更です。
test
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|
File without changes
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test
CHANGED
|
@@ -1,7 +1,7 @@
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|
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1
1
|
Pythonの「tensorflow」がうまく利用できません。
|
|
2
2
|
pip install tensorflowなどを行い、pathを通したはずですがうまくいきません。
|
|
3
3
|
数日前までは、添付画像のようにモジュール警告の線が引かれているのにも関わらず実行できました。
|
|
4
|
-
|
|
5
5
|
|
|
6
6
|
しかし、現在は実行を行っても、エラーになります。
|
|
7
7
|
エラー文は以下となります。
|
1
コードを追加します。失礼いたします。
test
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File without changes
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test
CHANGED
|
@@ -15,3 +15,146 @@
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|
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15
15
|
もし、この辺に精通している方がいらしましたら、ぜひご教授ください。
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|
16
16
|
また、現在はanacondaの利用はできないのでご容赦ください。
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17
17
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|
18
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+
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19
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+
|
|
20
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+
```python
|
|
21
|
+
#models.py
|
|
22
|
+
from tensorflow.keras.models import Model
|
|
23
|
+
from tensorflow.keras.layers import Dense, Input, Embedding, SimpleRNN, LSTM, Conv1D, GlobalMaxPooling1D
|
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24
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+
|
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25
|
+
|
|
26
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+
class RNNModel:
|
|
27
|
+
|
|
28
|
+
def __init__(self, input_dim, output_dim,
|
|
29
|
+
emb_dim=300, hid_dim=100,
|
|
30
|
+
embeddings=None, trainable=True):
|
|
31
|
+
self.input = Input(shape=(None,), name='input')
|
|
32
|
+
if embeddings is None:
|
|
33
|
+
self.embedding = Embedding(input_dim=input_dim,
|
|
34
|
+
output_dim=emb_dim,
|
|
35
|
+
mask_zero=True,
|
|
36
|
+
trainable=trainable,
|
|
37
|
+
name='embedding')
|
|
38
|
+
else:
|
|
39
|
+
self.embedding = Embedding(input_dim=embeddings.shape[0],
|
|
40
|
+
output_dim=embeddings.shape[1],
|
|
41
|
+
mask_zero=True,
|
|
42
|
+
trainable=trainable,
|
|
43
|
+
weights=[embeddings],
|
|
44
|
+
name='embedding')
|
|
45
|
+
self.rnn = SimpleRNN(hid_dim, name='rnn')
|
|
46
|
+
self.fc = Dense(output_dim, activation='softmax')
|
|
47
|
+
|
|
48
|
+
def build(self):
|
|
49
|
+
x = self.input
|
|
50
|
+
embedding = self.embedding(x)
|
|
51
|
+
output = self.rnn(embedding)
|
|
52
|
+
y = self.fc(output)
|
|
53
|
+
return Model(inputs=x, outputs=y)
|
|
54
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+
|
|
55
|
+
|
|
56
|
+
class LSTMModel:
|
|
57
|
+
|
|
58
|
+
def __init__(self, input_dim, output_dim,
|
|
59
|
+
emb_dim=300, hid_dim=100,
|
|
60
|
+
embeddings=None, trainable=True):
|
|
61
|
+
self.input = Input(shape=(None,), name='input')
|
|
62
|
+
if embeddings is None:
|
|
63
|
+
self.embedding = Embedding(input_dim=input_dim,
|
|
64
|
+
output_dim=emb_dim,
|
|
65
|
+
mask_zero=True,
|
|
66
|
+
trainable=trainable,
|
|
67
|
+
name='embedding')
|
|
68
|
+
else:
|
|
69
|
+
self.embedding = Embedding(input_dim=embeddings.shape[0],
|
|
70
|
+
output_dim=embeddings.shape[1],
|
|
71
|
+
mask_zero=True,
|
|
72
|
+
trainable=trainable,
|
|
73
|
+
weights=[embeddings],
|
|
74
|
+
name='embedding')
|
|
75
|
+
self.lstm = LSTM(hid_dim, name='lstm')
|
|
76
|
+
self.fc = Dense(output_dim, activation='softmax')
|
|
77
|
+
|
|
78
|
+
def build(self):
|
|
79
|
+
x = self.input
|
|
80
|
+
embedding = self.embedding(x)
|
|
81
|
+
output = self.lstm(embedding)
|
|
82
|
+
y = self.fc(output)
|
|
83
|
+
return Model(inputs=x, outputs=y)
|
|
84
|
+
|
|
85
|
+
|
|
86
|
+
class CNNModel:
|
|
87
|
+
|
|
88
|
+
def __init__(self, input_dim, output_dim,
|
|
89
|
+
filters=250, kernel_size=3,
|
|
90
|
+
emb_dim=300, embeddings=None, trainable=True):
|
|
91
|
+
self.input = Input(shape=(None,), name='input')
|
|
92
|
+
if embeddings is None:
|
|
93
|
+
self.embedding = Embedding(input_dim=input_dim,
|
|
94
|
+
output_dim=emb_dim,
|
|
95
|
+
trainable=trainable,
|
|
96
|
+
name='embedding')
|
|
97
|
+
else:
|
|
98
|
+
self.embedding = Embedding(input_dim=embeddings.shape[0],
|
|
99
|
+
output_dim=embeddings.shape[1],
|
|
100
|
+
trainable=trainable,
|
|
101
|
+
weights=[embeddings],
|
|
102
|
+
name='embedding')
|
|
103
|
+
self.conv = Conv1D(filters,
|
|
104
|
+
kernel_size,
|
|
105
|
+
padding='valid',
|
|
106
|
+
activation='relu',
|
|
107
|
+
strides=1)
|
|
108
|
+
self.pool = GlobalMaxPooling1D()
|
|
109
|
+
self.fc = Dense(output_dim, activation='softmax')
|
|
110
|
+
|
|
111
|
+
def build(self):
|
|
112
|
+
x = self.input
|
|
113
|
+
embedding = self.embedding(x)
|
|
114
|
+
conv = self.conv(embedding)
|
|
115
|
+
pool = self.pool(conv)
|
|
116
|
+
y = self.fc(pool)
|
|
117
|
+
return Model(inputs=x, outputs=y)
|
|
118
|
+
|
|
119
|
+
|
|
120
|
+
class LSTMCNNModel:
|
|
121
|
+
|
|
122
|
+
def __init__(self, input_dim, output_dim,
|
|
123
|
+
filters=250, kernel_size=3,
|
|
124
|
+
emb_dim=300, hid_dim=100, embeddings=None):
|
|
125
|
+
self.input = Input(shape=(None,), name='input')
|
|
126
|
+
if embeddings is None:
|
|
127
|
+
self.embedding = Embedding(input_dim=input_dim,
|
|
128
|
+
output_dim=emb_dim,
|
|
129
|
+
mask_zero=True,
|
|
130
|
+
name='embedding')
|
|
131
|
+
else:
|
|
132
|
+
self.embedding = Embedding(input_dim=embeddings.shape[0],
|
|
133
|
+
output_dim=embeddings.shape[1],
|
|
134
|
+
mask_zero=True,
|
|
135
|
+
weights=[embeddings],
|
|
136
|
+
name='embedding')
|
|
137
|
+
self.lstm = LSTM(hid_dim, return_sequences=True, name='lstm')
|
|
138
|
+
self.conv = Conv1D(filters,
|
|
139
|
+
kernel_size,
|
|
140
|
+
padding='valid',
|
|
141
|
+
activation='relu',
|
|
142
|
+
strides=1)
|
|
143
|
+
self.pool = GlobalMaxPooling1D()
|
|
144
|
+
self.fc1 = Dense(hid_dim)
|
|
145
|
+
self.fc2 = Dense(output_dim, activation='softmax')
|
|
146
|
+
|
|
147
|
+
def build(self):
|
|
148
|
+
x = self.input
|
|
149
|
+
embedding = self.embedding(x)
|
|
150
|
+
lstm = self.lstm(embedding)
|
|
151
|
+
conv = self.conv(lstm)
|
|
152
|
+
pool = self.pool(conv)
|
|
153
|
+
y = self.fc1(pool)
|
|
154
|
+
y = self.fc2(y)
|
|
155
|
+
return Model(inputs=x, outputs=y)
|
|
156
|
+
|
|
157
|
+
```
|
|
158
|
+
|
|
159
|
+
|
|
160
|
+
|