ValueError：LSTM 模型中的形状不兼容

Question

我正在根据以下参数创建 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

你能帮我解决这个错误吗？

Answer 1

您的 y_train 来自 Pandas 数据框的单列，这是单列。如果您的分类问题是二进制分类 0/1 问题，则此方法适用。那么你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'))