以下代码段给出了一些tensorType错误
TypeError：无法将Type TensorType（float32,3D）（Variable Subtensor {：int64：}。0）转换为Type TensorType（float32，（False，False，True））。您可以尝试手动将Subtensor {：int64：}。0转换为TensorType（float32，（False，False，True））。
这是我正在阅读的其中一个教程网站的基本示例。 你能帮我理解这个错误吗？我是机器学习和keras的新手
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]))
答案 0 :(得分：1)
我是机器学习的新手，也遇到了你的问题。 我改变了下面的代码并运行了，但我不确定它是否正确。
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]))
输出：
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]
答案 1 :(得分：0)
作为参考，此示例代码来自http://benjaminbolte.com/blog/2016/keras-language-modeling.html，虽然从2016年4月起，它不适用于当前版本的Keras==1.1.1，Theano==0.8.2
在博文发布时，我也使用了这段代码，修改了它以使用LSTM，现在也注意到它不再运行最新的Keras / Theano。
我认为您需要的主要内容，尤其是使用新的Keras-1功能API，Dense之后的SimpleRNN层是SimpleRNN。您还需要确保output_dim的{{1}}为3.
我修改了最小值以使代码正常工作，这是运行的更新代码并提供以下输出：
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]))
输出：
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]