以下是来自https://machinelearningmastery.com/how-to-develop-rnn-models-for-human-activity-recognition-time-series-classification/的代码，该代码使用 LSTM 和 CNN 以及 TimeDistributed 用于人类活动时间序列：

def evaluate_model(trainX, trainy, testX, testy):
    # define model
    verbose, epochs, batch_size = 0, 25, 64
    n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
    # reshape data into time steps of sub-sequences
    n_steps, n_length = 4, 32
    trainX = trainX.reshape((trainX.shape[0], n_steps, n_length, n_features))
    testX = testX.reshape((testX.shape[0], n_steps, n_length, n_features))
    # define model
    model = Sequential()
    model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu'), input_shape=(None,n_length,n_features)))
    model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu')))
    model.add(TimeDistributed(Dropout(0.5)))
    model.add(TimeDistributed(MaxPooling1D(pool_size=2)))
    model.add(TimeDistributed(Flatten()))
    model.add(LSTM(100))
    model.add(Dropout(0.5))
    model.add(Dense(100, activation='relu'))
    model.add(Dense(n_outputs, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    # fit network
    model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
    # evaluate model
    _, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
    return accuracy

但是，我想为简单的时间序列数据（1000 行和 100 列）修改上面的代码，其中每一行是 100 个值的时间序列（显示为列）。输出是一个类，有 7 种类（因此 n_output=7）。这是一个“序列分类”问题。

为此，我应该对上述功能进行哪些更改？我是否需要 n_steps 和 n_length （因为我的数据比人类活动识别数据更简单）？对于简单的 CNN1D，我必须重塑如下：

trainX = trainX.reshape(nrows, ncols, 1)
# then use (ncols, 1) as input shape

我应该如何在这里重塑？

我尝试删除 n_steps 和 n_length 并将 input_shape 设置为 (100, 1) （在将数据重塑为 (1000,100,1) 之后），但它不起作用。