Python 稀疏矩阵长度不明确

我对机器学习非常陌生，所以这个问题听起来可能很愚蠢。 我正在遵循一个错误，但我面临一个错误，我不知道如何解决

这是我的代码（基本上就是教程中的代码）

当我到达最后一行时，我得到一个错误

“类型错误：稀疏矩阵长度不明确；请使用getnnz（）或形状[0]”

我想我必须对正在使用的数据执行某种转换，或者我应该尝试以不同的方式加载这些数据。我已经试着在Stackoverflow上搜索了，但是由于对这一切都不熟悉，我找不到任何有用的东西

我该怎么做？理想情况下，我不仅希望得到解决方案，还希望得到一个关于错误发生原因的简要解释，以及解决方案是如何解决的

---

谢谢

您面临此困难的原因是您的

X\u列车

和

X\u测试

属于

类型，而您的模型希望它是一个numpy阵列

试着将它们浇铸到稠密状态，你就可以开始了：

X_train = X_train.todense()
X_test = X_test.todense()

---

不确定，为什么此脚本出现错误

以下脚本运行良好；即使是稀疏矩阵。可以在你的机器上试一试

sentences = ['i want to test this','let us try this',
             'would this work','how about this',
             'even this','this should not work']
y= [0,0,0,0,0,1]
from sklearn.model_selection import train_test_split
sentences_train, sentences_test, y_train, y_test = train_test_split(sentences, y, test_size=0.25, random_state=1000)


from sklearn.feature_extraction.text import CountVectorizer


vectorizer = CountVectorizer()
vectorizer.fit(sentences_train)

X_train = vectorizer.transform(sentences_train)
X_test  = vectorizer.transform(sentences_test)

from keras.models import Sequential
from keras import layers

input_dim = X_train.shape[1] 

model = Sequential()
model.add(layers.Dense(10, input_dim=input_dim, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', 
            optimizer='adam', 
            metrics=['accuracy'])
model.summary()

model.fit(X_train, y_train,
                        epochs=2,
                        verbose=True,
                        validation_data=(X_test, y_test),
                        batch_size=2)

#
Layer (type)                 Output Shape              Param #   
=================================================================
dense_5 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_6 (Dense)              (None, 1)                 11        
=================================================================
Total params: 121
Trainable params: 121
Non-trainable params: 0
_________________________________________________________________
Train on 4 samples, validate on 2 samples
Epoch 1/2
4/4 [==============================] - 1s 169ms/step - loss: 0.7570 - acc: 0.2500 - val_loss: 0.6358 - val_acc: 1.0000
Epoch 2/2
4/4 [==============================] - 0s 3ms/step - loss: 0.7509 - acc: 0.2500 - val_loss: 0.6328 - val_acc: 1.0000

---

哪一行给出了错误？类型（X\U列车）、类型（y\U列车）的输出是什么？@SergeyBushmanov类型（X\U列车）：；键入（y\u-train）：你能试着像

X\u-train.todense（）

一样将稀疏矩阵转换为稠密矩阵，并将结果传递给

model.fit（）

？@FrancoPiccolo最后一个历史=model.fit（X\u-train，y\u-train，nb\u-epoch=100，verbose=False，validation\u-data=（X\u-test，y\u-test），batch\u-size=10），我也这么认为。Todense是一项耗资巨大的业务。更新包可能是更好的解决方案。

sentences = ['i want to test this','let us try this',
             'would this work','how about this',
             'even this','this should not work']
y= [0,0,0,0,0,1]
from sklearn.model_selection import train_test_split
sentences_train, sentences_test, y_train, y_test = train_test_split(sentences, y, test_size=0.25, random_state=1000)


from sklearn.feature_extraction.text import CountVectorizer


vectorizer = CountVectorizer()
vectorizer.fit(sentences_train)

X_train = vectorizer.transform(sentences_train)
X_test  = vectorizer.transform(sentences_test)

from keras.models import Sequential
from keras import layers

input_dim = X_train.shape[1] 

model = Sequential()
model.add(layers.Dense(10, input_dim=input_dim, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', 
            optimizer='adam', 
            metrics=['accuracy'])
model.summary()

model.fit(X_train, y_train,
                        epochs=2,
                        verbose=True,
                        validation_data=(X_test, y_test),
                        batch_size=2)

#
Layer (type)                 Output Shape              Param #   
=================================================================
dense_5 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_6 (Dense)              (None, 1)                 11        
=================================================================
Total params: 121
Trainable params: 121
Non-trainable params: 0
_________________________________________________________________
Train on 4 samples, validate on 2 samples
Epoch 1/2
4/4 [==============================] - 1s 169ms/step - loss: 0.7570 - acc: 0.2500 - val_loss: 0.6358 - val_acc: 1.0000
Epoch 2/2
4/4 [==============================] - 0s 3ms/step - loss: 0.7509 - acc: 0.2500 - val_loss: 0.6328 - val_acc: 1.0000