Python ValueError:形状在LSTM模型中不兼容

我正在基于以下参数创建一个LSTM模型

embed_dim = 128
lstm_out = 200
batch_size = 32

model = Sequential()
model.add(Embedding(2500, embed_dim,input_length = X.shape[1]))
model.add(Dropout(0.2))
model.add(LSTM(lstm_out))
model.add(Dense(2,activation='sigmoid'))
model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])
print(model.summary())

Xtrain, Xtest, ytrain, ytest = train_test_split(X, train['target'], test_size = 0.2, shuffle=True)
print(Xtrain.shape, ytrain.shape)
print(Xtest.shape, ytest.shape)

model.fit(Xtrain, ytrain, batch_size =batch_size, epochs = 1,  verbose = 5)

但我收到以下错误

ValueError: Shapes (32, 1) and (32, 2) are incompatible

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你能帮我解决这个错误吗？

你的

y\u序列

来自熊猫数据帧的一列，这是一列。如果您的分类问题是二进制分类0/1问题，则此选项适用。那么在输出层只需要一个神经元

model = Sequential()
model.add(Embedding(2500, embed_dim,input_length = X.shape[1]))
model.add(Dropout(0.2))
model.add(LSTM(lstm_out))
# Only one neuron in the output layer
model.add(Dense(1,activation='sigmoid'))

---

您的

y\u序列

来自熊猫数据帧的一列，这是一列。如果您的分类问题是二进制分类0/1问题，则此选项适用。那么在输出层只需要一个神经元

model = Sequential()
model.add(Embedding(2500, embed_dim,input_length = X.shape[1]))
model.add(Dropout(0.2))
model.add(LSTM(lstm_out))
# Only one neuron in the output layer
model.add(Dense(1,activation='sigmoid'))

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非常感谢你的帮助。我将“评级”列更改为二进制，它成功了！非常感谢你的帮助。我将“评级”列更改为二进制，它成功了！