我正在与 keras 合作进行文本分类。经过预处理和矢量化后,我的训练和验证数据详细信息如下:
print(X_train.shape, ',', X_train.ndim, ',', type(X_train))
print(y_train.shape, ',', y_train.ndim, ',', type(y_train))
print(X_valid.shape, ',', X_valid.ndim, ',', type(X_valid))
print(y_valid.shape, ',', y_valid.ndim, ',', type(y_valid))
print(data_dim)
输出是:
(14904,) , 1 , <class 'numpy.ndarray'>
(14904,) , 1 , <class 'numpy.ndarray'>
(3725,) , 1 , <class 'numpy.ndarray'>
(3725,) , 1 , <class 'numpy.ndarray'>
15435
那么模型定义是:
model = Sequential()
model.add(LSTM(100, input_shape=(data_dim,1 ), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(200))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics = ['accuracy'])
model.summary()
型号总结:
模型拟合:
model.fit(X_train,y_train, validation_data = (X_valid, y_valid),
batch_size=batch_size, epochs=epochs)
为什么会出现这个错误?
----> 1 model.fit(X_train,y_train, validation_data = (X_valid, y_valid),
2 batch_size=batch_size, epochs=epochs)
...
...
ValueError: Input 0 of layer sequential is incompatible with the layer:
expected ndim=3, found ndim=2. Full shape received: [None, 1]