Question

我有这样的网络架构：

model = Sequential()
model.add(Embedding(9761, 100, input_length=longest_period))
model.add(LSTM(30, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

当我尝试拟合模型时：

res = model.fit(X_train_lsmt, np.array(y_train_lsmt), validation_split=0.25, epochs=2, batch_size=128, verbose=0)

我收到此错误：

ValueError: Error when checking model input: expected
embedding_3_input to have shape (None, 217) but got array 
with shape (3133, 1)

我认为错误可能是关于经过一次热编码的y_train_lsmt，形状为(3133,3)

[[ 0. 1. 0.] [ 0. 1. 0.] [ 0. 0. 1.] ..., [ 1. 0. 0.] [ 1. 0. 0.] [ 0. 1. 0.]]

但是我不确定。

更新：

我已经部分解决了添加Flatten()层的问题：

model = Sequential()
model.add(Embedding(9761, 100, input_length=stringa_piu_lunga))
model.add(LSTM(units=10, return_sequences=True))
model.add(Flatten())
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

但是现在我在模型评估期间遇到了相同的错误：

score = model.evaluate(X_test_lsmt, y_train_lsmt, verbose=0)

Answer 1

您的代码似乎正常。将您的y_train_lstm更改为分类：

y_train_lstm = keras.utils.to_categorical(y_train_lstm)

或将损失更改为sparse_categorical_entropy：

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

已编辑：根据您的github存储库，该评估无法正常工作，因为您没有预处理x_test_lstm。试试：

X_test_lstm = sequence.pad_sequences(X_test_lstm, maxlen=longest_string)

LSTM嵌入层形状错误

1 个答案: