Keras TypeError例外：タイプTensorType（のfloat32、3D）に変換できません（可変サブテンソルの{：のInt64：} 0）

Question

次のコードスニペットは私

はTypeErrorを一部tensorTypeエラーを与える：タイプTensorType（のfloat32、3Dに変換することはできません。 ）（Variable Subtensor {：int64：}。0）をタイプTensorType（float32、（False、False、True））に変換します。 Subtensor {：int64：}。0を手動でTensorType（float32、（False、False、True））に変換することができます。

これは私が読んでいたチュートリアルのウェブサイトからの基本的な例です。 このエラーの理解にご協力いただけますか？私は機械学習とケラスの両方を勉強していません。

import itertools 
import numpy as np 
# put together a model to predict 
from keras.layers import Input, Embedding, merge, Flatten, SimpleRNN 
from keras.models import Model 


sentences = ''' 
sam is red 
hannah not red 
hannah is green 
bob is green 
bob not red 
sam not green 
sarah is red 
sarah not green'''.strip().split('\n') 
is_green = np.asarray([[0, 1, 1, 1, 1, 0, 0, 0]], dtype='int32').T 

lemma = lambda x: x.strip().lower().split(' ') 
sentences_lemmatized = [lemma(sentence) for sentence in sentences] 
words = set(itertools.chain(*sentences_lemmatized)) 
# set(['boy', 'fed', 'ate', 'cat', 'kicked', 'hat']) 

# dictionaries for converting words to integers and vice versa 
word2idx = dict((v, i) for i, v in enumerate(words)) 
idx2word = list(words) 

# convert the sentences a numpy array 
to_idx = lambda x: [word2idx[word] for word in x] 
sentences_idx = [to_idx(sentence) for sentence in sentences_lemmatized] 
sentences_array = np.asarray(sentences_idx, dtype='int32') 

# parameters for the model 
sentence_maxlen = 3 
n_words = len(words) 
n_embed_dims = 5 

input_sentence = Input(shape=(sentence_maxlen,), dtype='int32') 
input_embedding = Embedding(n_words, n_embed_dims)(input_sentence) 
#color_prediction = SimpleRNN(init='uniform',output_dim=1,input_dim=3)(input_embedding) 
#color_prediction = SimpleRNN(output_dim=1,input_dim=5, 
#  init='glorot_uniform', inner_init='orthogonal', activation='sigmoid', weights=None, return_sequences=False)(input_embedding); 

color_prediction = SimpleRNN(1, return_sequences=False, batch_input_shape=(10, 2, 3))(input_embedding); 

predict_green = Model(input=[input_sentence], output=[color_prediction]) 
predict_green.compile(optimizer='sgd', loss='binary_crossentropy') 

# fit the model to predict what color each person is 
predict_green.fit([sentences_array], [is_green], nb_epoch=5000, verbose=1) 
embeddings = predict_green.layers[1].W.get_value() 

# print out the embedding vector associated with each word 
for i in range(n_words): 
    print('{}: {}'.format(idx2word[i], embeddings[i])) 

Answer 1

私は機械学習に新しいですし、あなたの問題にも会いました。 以下のようにコードを変更して実行しましたが、正しいかどうかはわかりません。

import itertools 
import os 
import numpy as np 
from keras.preprocessing import sequence 
from keras.models import Sequential 
from keras.layers import Dense, Dropout, Activation 
from keras.layers import Embedding 
from keras.layers import Input, Embedding, merge, Flatten, SimpleRNN 
sentences = ''' 
sam is red 
hannah not red 
hannah is green 
bob is green 
bob not red 
sam not green 
sarah is red 
sarah not green'''.strip().split('\n') 
is_green = np.asarray([[0, 1, 1, 1, 1, 0, 0, 0]], dtype='int32').T 
lemma = lambda x: x.strip().lower().split(' ') 
sentences_lemmatized = [lemma(sentence) for sentence in sentences] 
words = set(itertools.chain(*sentences_lemmatized)) 
# set(['boy', 'fed', 'ate', 'cat', 'kicked', 'hat']) 
# dictionaries for converting words to integers and vice versa 
word2idx = dict((v, i) for i, v in enumerate(words)) 
idx2word = list(words) 
# convert the sentences a numpy array 
to_idx = lambda x: [word2idx[word] for word in x] 
sentences_idx = [to_idx(sentence) for sentence in sentences_lemmatized] 
sentences_array = np.asarray(sentences_idx, dtype='int32') 
# parameters for the model 
sentence_maxlen = 3 
n_words = len(words) 
n_embed_dims = 3 
model = Sequential() 
model.add(Embedding(n_words, n_embed_dims,input_length=sentence_maxlen)) 
model.add(SimpleRNN(3)) 
model.add(Dense(1)) 
model.add(Activation('sigmoid')) 
model.compile(loss='binary_crossentropy', 
       optimizer='sgd', 
       metrics=['accuracy']) 
model.fit([sentences_array], [is_green], nb_epoch=5000, verbose=1) 
predictions = model.predict(sentences_array) 
print predictions.shape 
embeddings = model.layers[0].W.get_value() 
# print out the embedding vector associated with each word 
for i in range(n_words): 
    print('{}: {}'.format(idx2word[i], embeddings[i])) 

OUTPUT：このサンプルコードは、2016年4月からhttp://benjaminbolte.com/blog/2016/keras-language-modeling.htmlからとが来て参考

sarah: [-0.51089537 -0.30958903 -0.17312947] sam: [-0.47487321 
-0.33426151 -0.18260512] hannah: [ 0.51548952 0.33343625 0.18121554] is: [ 0.02989657 -0.02573686 0.01081978] green: [ 0.0155487 
-0.02551323 0.00846179] not: [ 0.01339869 -0.02586824 0.01932905] bob: [ 0.47654441 0.37283263 0.17969941] red: [-0.02136148 
0.04420395 -0.03119873] 

Answer 2

、それは時Keras==1.1.1、Theano==0.8.2

の現在のバージョンでは動作しません。ブログの投稿、私もこのコードを取ってLSTMを使用するように修正し、最近はKeras/Theanoの最新版で動作しなくなったことに気付きました。

あなたが必要と思う主なものは、特に新しいKeras-1機能APIを使用すると、末尾にDenseの層がSimpleRNNの後に続きます。

import itertools 
import numpy as np 
# put together a model to predict 
from keras.layers import Input, Embedding, merge, Flatten, Dense, SimpleRNN 
from keras.models import Model 


sentences = ''' 
sam is red 
hannah not red 
hannah is green 
bob is green 
bob not red 
sam not green 
sarah is red 
sarah not green'''.strip().split('\n') 
is_green = np.asarray([[0, 1, 1, 1, 1, 0, 0, 0]], dtype='int32').T 

lemma = lambda x: x.strip().lower().split(' ') 
sentences_lemmatized = [lemma(sentence) for sentence in sentences] 
words = set(itertools.chain(*sentences_lemmatized)) 
# set(['boy', 'fed', 'ate', 'cat', 'kicked', 'hat']) 

# dictionaries for converting words to integers and vice versa 
word2idx = dict((v, i) for i, v in enumerate(words)) 
idx2word = list(words) 

# convert the sentences a numpy array 
to_idx = lambda x: [word2idx[word] for word in x] 
sentences_idx = [to_idx(sentence) for sentence in sentences_lemmatized] 
sentences_array = np.asarray(sentences_idx, dtype='int32') 

# parameters for the model 
sentence_maxlen = 3 
n_words = len(words) 
n_embed_dims = 5 

input_sentence = Input(shape=(sentence_maxlen,), dtype='int32') 
input_embedding = Embedding(n_words, n_embed_dims)(input_sentence) 
#color_prediction = SimpleRNN(init='uniform',output_dim=1,input_dim=3)(input_embedding) 
#color_prediction = SimpleRNN(output_dim=1,input_dim=5, 
#  init='glorot_uniform', inner_init='orthogonal', activation='sigmoid', weights=None, return_sequences=False)(input_embedding); 

color_prediction = SimpleRNN(3, return_sequences=False, batch_input_shape=(10, 2, 3))(input_embedding); 
output = Dense(1, activation='sigmoid')(color_prediction) 

predict_green = Model(input=[input_sentence], output=[output]) 
predict_green.compile(optimizer='sgd', loss='binary_crossentropy') 

# fit the model to predict what color each person is 
predict_green.fit([sentences_array], [is_green], nb_epoch=5000, verbose=1) 
embeddings = predict_green.layers[1].W.get_value() 

# print out the embedding vector associated with each word 
for i in range(n_words): 
    print('{}: {}'.format(idx2word[i], embeddings[i])) 

出力：あなたはまた、あなたのSimpleRNNは、私が働いてあなたのコードを取得するために最低限の修正output_dim 3.

の持っていることを確認する必要があり、ここで実行され、次の出力が得られます更新されたコードは次のようになります。

red: [-0.03866547 -0.04583547 -0.0314577 -0.04576075 0.00064603] 
is: [-0.02093433 -0.00811194 -0.01167872 -0.02813761 -0.02160992] 
bob: [-0.46798751 -0.01344876 0.36456427 -0.04407313 0.22842836] 
sarah: [ 0.45210958 0.02854088 -0.34124625 0.0184452 -0.23039177] 
green: [-0.00628944 0.04167764 -0.02662347 -0.01051781 0.03500782] 
not: [-0.01113868 -0.01490347 -0.00235422 -0.03276222 -0.0263596 ] 
sam: [ 0.45785579 0.04527023 -0.34266111 -0.01125967 -0.25254542] 
hannah: [-0.45697126 -0.00369712 0.37587604 -0.04988953 0.23814654]