CONV1D神经网络形状

Question

model = Sequential()
model.add(Conv1D(filters=4, kernel_size=(1), activation="relu", input_shape=(4,1)))
model.add(MaxPooling1D(pool_size=(1)))
model.add(Dropout(0.25))
model.add(Conv1D(filters=32, kernel_size=(1), activation='relu'))
model.add(MaxPooling1D(pool_size=(1)))
model.add(Dropout(0.25))
model.add(Conv1D(filters=64, kernel_size=(1), activation="relu"))
model.add(MaxPooling1D(pool_size=(1)))
model.add(Dropout(0.25))
model.add(Conv1D(filters=64, kernel_size=(1), activation='relu'))
model.add(MaxPooling1D(pool_size=(1)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(7, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

你好，我刚开始构建神经网络，决定尝试解决一个多标签分类问题。我将四个特征值作为输入，并将结果分类为7个类别中的一个或多个。因此，我决定实现上面看到的神经网络。但是，在拟合模型时

model.fit(X_train, y_train, epochs = 10, validation_data = (X_test,y_test), batch_size = 64)

我收到这个错误：

Error when checking input: expected conv1d_92_input to have 3 dimensions, but got array with shape (415, 4)

为了让神经网络与数据相适应，我对要做的事情感到困惑。特征和标注数据的形状分别为: X_train = (414,4) y_train = (413,7)

Answer 1

我相信你可能会发现之前的这篇堆栈堆栈溢出帖子(它似乎解决了你的问题)很有帮助：Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (339732, 29)