处理 1D CNN 中的批量大小和时间步长

Question

I have a batch generator which gives me data in the shape of (500, 1, 12) (ie corresponding to (batch size, time steps, features) ).

def batch_generator(batch_size, gen_x,gen_y): 
    batch_features = np.zeros((batch_size,1, 12))
    batch_labels = np.zeros((batch_size,9))
    while True:
        for i in range(batch_size):
            batch_features[i] = next(gen_x)
            batch_labels[i] = next(gen_y)
        yield batch_features, batch_labels

def generate_X():
    while True:
        with open("/my_path/my_data.csv") as f:
            for line in f:
                currentline = line.rstrip('\n').split(",")
                currentline = np.asarray(currentline)
                currentline = currentline.reshape(1,1,12)
                yield currentline

def generate_y():
    while True:
        for i in range(len(y_train)):
            y= y_train[i]
            yield y

I then try to feed this into a 1D-CNN:

model = Sequential()
model.add(Conv1D(filters=100, kernel_size=1, activation='relu', input_shape=(1,12), data_format="channels_last"))

But now I am not able to use a kernel size of more than 1 (ie kernel_size = 1 ). This is probably because my time step is equal to 1.

How can I use the whole batch size as input to the 1D-CNN and increase the kernel_size ?

Answer 1

Keep in mind that 1D-convolution is used when each of our input samples is a sequence, ie data in which the order of values are important/specified, like stock market values over a week or the weather temperature values over a period of month or a sequence of genomes or words. With that said, considering your data, there are three different scenarios:

• If each line in your csv file is a sequence of length 12, then you are dealing with samples of shape (12,1) , ie in each sample there are 12 timesteps where each timestep has only on feature. So you should reshape it accordingly (ie to (12,1) and not to (1,12) ).

• However, if each line is not a sequence by itself, but a group of consecutive lines form a sequence, then you must generate your data accordingly: each sample would consists of multiple consecutive lines, eg if we consider the number of timesteps to be 10 then lines #1 to #10 would be a sample, lines #2 to #12 would be another sample, and so on. And in this case each sample would have a shape of (number_of_timesteps, 12) (in the example I mentioned it would be (10,12) ). Now you can create and generate these samples by writing a custom function, or alternatively you could load all of the data as a numpy array and then use TimeseriesGenerator to do it for you.

• If none of the two cases above apply, then it's very likely that your data is not a sequential at all and therefore using 1D-CNN (or any other sequence processing model like RNNs) does not make sense for this data. Instead, you should use other suitable architectures.