我曾经在2D卷积上遇到过同样的问题，问题是当你应用卷积层时，你所应用的内核大小不是(kernel_size, 1)，而是实际上的(kernel_size, input_dim)。
如果你不这样想的话，kernel_size = 1的1D卷积层将不会对它接收到的输入做任何事情。
而是计算每个时间步长的输入要素的加权平均值，每个时间步长使用相同的权重（虽然每个滤波器使用不同的一组权重）。我认为将input_dim可视化为图像的2D卷积中channels的数量是有帮助的，其中应用相同的推理（在该情况下是“丢失”并转换成滤波器数目的channels）。
为了让自己相信这一点，您可以使用kernel_size=(1D_kernel_size, input_dim)和相同数量的过滤器，用2D卷积层再现1D卷积。

from keras.layers import Conv1D, Conv2D
import keras.backend as K
import numpy as np

# create an input with 4 steps and 5 channels/input_dim
channels = 5
steps = 4
filters = 3
val = np.array([list(range(i * channels, (i + 1) * channels)) for i in range(1, steps + 1)])
val = np.expand_dims(val, axis=0)
x = K.variable(value=val)

# 1D convolution. Initialize the kernels to ones so that it's easier to compute the result by hand

conv1d = Conv1D(filters=filters, kernel_size=1, kernel_initializer='ones')(x)

# 2D convolution that replicates the 1D one

# need to add a dimension to your input since conv2d expects 4D inputs. I add it at axis 4 since my keras is setup with `channel_last`
val1 = np.expand_dims(val, axis=3)
x1 = K.variable(value=val1)

conv2d = Conv2D(filters=filters, kernel_size=(1, 5), kernel_initializer='ones')(x1)

# evaluate and print the outputs

print(K.eval(conv1d))
print(K.eval(conv2d))

正如我所说，我也花了一段时间才理解这一点，我想主要是因为没有教程解释清楚

谢谢，这很有用.
这里，相同的代码使用最新版本的tensorflow +角速度进行调整，并在轴0上叠加以构建4D

# %%
from tensorflow.keras.layers import Conv1D, Conv2D
from tensorflow.keras.backend import eval
import tensorflow as tf
import numpy as np

# %%
# create an 3D input with format BLC (Batch, Layer, Channel)
batch = 10
layers = 3
channels = 5
kernel = 2

val3D = np.random.randint(0, 100, size=(batch, layers, channels))
x = tf.Variable(val3D.astype('float32'))

# %%
# 1D convolution. Initialize the kernels to ones so that it's easier to compute the result by hand / compare
conv1d = Conv1D(filters=layers, kernel_size=kernel, kernel_initializer='ones')(x)

# %%
# 2D convolution that replicates the 1D one

# need to add a dimension to your input since conv2d expects 4D inputs. I add it at axis 0 since my keras is setup with `channel_last`
# stack 3 time the same
val4D = np.stack([val3D,val3D,val3D], axis=0)
x1 = tf.Variable(val4D.astype('float32'))

# %%
# 2D convolution. Initialize the kernel_size to one for the 1st kernel size so that replicate the conv1D
conv2d = Conv2D(filters=layers, kernel_size=(1, kernel), kernel_initializer='ones')(x1)

# %%
# evaluate and print the outputs

print(eval(conv1d))
print('---------------------------------------------')
# display only one of the stacked
print(eval(conv2d)[0])