如何在keras中对矩阵的每一行应用不同的密集层答案

【问题标题】：How to apply a different dense layer to each row of a matrix in keras如何在keras中对矩阵的每一行应用不同的密集层
【发布时间】：2026-01-07 18:50:02
【问题描述】：

我上一层的输出具有形状 (None, 30, 600)。我想将此矩阵的每一行乘以 不同的 (600, 600) 矩阵或等效地将此矩阵乘以 3D 权重矩阵。这可以通过对每一行应用不同的密集层来实现。我尝试使用 TimeDistributed Wrapper，但这会将 same 密集层应用于每一行。我也尝试过像这样使用 lambda 层：

Lambda(lambda x: tf.stack(x, axis=1))(
    Lambda(lambda x: [Dense(600)(each) for each in tf.unstack(x, axis=1)])(prev_layer_output)
)

这似乎解决了问题，我能够正确训练模型。但我注意到 model.summary() 不能识别这些密集层，它们也没有反映在总可训练参数的计数中。此外，当我加载模型时，我无法恢复它们的权重，因此整个训练都被浪费了。我该如何解决这个问题？如何对矩阵的每一行应用不同的密集层？

【问题讨论】：

标签： deep-learning keras keras-layer

【解决方案1】：

您可以使用多个层，而不是将所有内容包装到单个 Lambda 层中。

x = Input((30, 600))
unstacked = Lambda(lambda x: K.tf.unstack(x, axis=1))(x)
dense_outputs = [Dense(600)(x) for x in unstacked]
merged = Lambda(lambda x: K.stack(x, axis=1))(dense_outputs)
model = Model(x, merged)

现在您可以在model.summary() 中看到 30 个Dense(600) 层。

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
input_1 (InputLayer)            (None, 30, 600)      0
__________________________________________________________________________________________________
lambda_1 (Lambda)               [(None, 600), (None, 0           input_1[0][0]
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 600)          360600      lambda_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 600)          360600      lambda_1[0][1]
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 600)          360600      lambda_1[0][2]
__________________________________________________________________________________________________
dense_4 (Dense)                 (None, 600)          360600      lambda_1[0][3]
__________________________________________________________________________________________________
dense_5 (Dense)                 (None, 600)          360600      lambda_1[0][4]
__________________________________________________________________________________________________
dense_6 (Dense)                 (None, 600)          360600      lambda_1[0][5]
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, 600)          360600      lambda_1[0][6]
__________________________________________________________________________________________________
dense_8 (Dense)                 (None, 600)          360600      lambda_1[0][7]
__________________________________________________________________________________________________
dense_9 (Dense)                 (None, 600)          360600      lambda_1[0][8]
__________________________________________________________________________________________________
dense_10 (Dense)                (None, 600)          360600      lambda_1[0][9]
__________________________________________________________________________________________________
dense_11 (Dense)                (None, 600)          360600      lambda_1[0][10]
__________________________________________________________________________________________________
dense_12 (Dense)                (None, 600)          360600      lambda_1[0][11]
__________________________________________________________________________________________________
dense_13 (Dense)                (None, 600)          360600      lambda_1[0][12]
__________________________________________________________________________________________________
dense_14 (Dense)                (None, 600)          360600      lambda_1[0][13]
__________________________________________________________________________________________________
dense_15 (Dense)                (None, 600)          360600      lambda_1[0][14]
__________________________________________________________________________________________________
dense_16 (Dense)                (None, 600)          360600      lambda_1[0][15]
__________________________________________________________________________________________________
dense_17 (Dense)                (None, 600)          360600      lambda_1[0][16]
__________________________________________________________________________________________________
dense_18 (Dense)                (None, 600)          360600      lambda_1[0][17]
__________________________________________________________________________________________________
dense_19 (Dense)                (None, 600)          360600      lambda_1[0][18]
__________________________________________________________________________________________________
dense_20 (Dense)                (None, 600)          360600      lambda_1[0][19]
__________________________________________________________________________________________________
dense_21 (Dense)                (None, 600)          360600      lambda_1[0][20]
__________________________________________________________________________________________________
dense_22 (Dense)                (None, 600)          360600      lambda_1[0][21]
__________________________________________________________________________________________________
dense_23 (Dense)                (None, 600)          360600      lambda_1[0][22]
__________________________________________________________________________________________________
dense_24 (Dense)                (None, 600)          360600      lambda_1[0][23]
__________________________________________________________________________________________________
dense_25 (Dense)                (None, 600)          360600      lambda_1[0][24]
__________________________________________________________________________________________________
dense_26 (Dense)                (None, 600)          360600      lambda_1[0][25]
__________________________________________________________________________________________________
dense_27 (Dense)                (None, 600)          360600      lambda_1[0][26]
__________________________________________________________________________________________________
dense_28 (Dense)                (None, 600)          360600      lambda_1[0][27]
__________________________________________________________________________________________________
dense_29 (Dense)                (None, 600)          360600      lambda_1[0][28]
__________________________________________________________________________________________________
dense_30 (Dense)                (None, 600)          360600      lambda_1[0][29]
__________________________________________________________________________________________________
lambda_2 (Lambda)               (None, 30, 600)      0           dense_1[0][0]
                                                                 dense_2[0][0]
                                                                 dense_3[0][0]
                                                                 dense_4[0][0]
                                                                 dense_5[0][0]
                                                                 dense_6[0][0]
                                                                 dense_7[0][0]
                                                                 dense_8[0][0]
                                                                 dense_9[0][0]
                                                                 dense_10[0][0]
                                                                 dense_11[0][0]
                                                                 dense_12[0][0]
                                                                 dense_13[0][0]
                                                                 dense_14[0][0]
                                                                 dense_15[0][0]
                                                                 dense_16[0][0]
                                                                 dense_17[0][0]
                                                                 dense_18[0][0]
                                                                 dense_19[0][0]
                                                                 dense_20[0][0]
                                                                 dense_21[0][0]
                                                                 dense_22[0][0]
                                                                 dense_23[0][0]
                                                                 dense_24[0][0]
                                                                 dense_25[0][0]
                                                                 dense_26[0][0]
                                                                 dense_27[0][0]
                                                                 dense_28[0][0]
                                                                 dense_29[0][0]
                                                                 dense_30[0][0]
==================================================================================================
Total params: 10,818,000
Trainable params: 10,818,000
Non-trainable params: 0
__________________________________________________________________________________________________

编辑：验证此模型是否正在学习：

model.compile(loss='mse', optimizer='adam')
w0 = model.get_weights()
model.fit(np.random.rand(100,30,600), np.random.rand(100,30,600), epochs=10)

你应该可以看到损失在减少：

Epoch 1/10
100/100 [==============================] - 1s 15ms/step - loss: 0.4725
Epoch 2/10
100/100 [==============================] - 0s 1ms/step - loss: 0.2211
Epoch 3/10
100/100 [==============================] - 0s 1ms/step - loss: 0.2405
Epoch 4/10
100/100 [==============================] - 0s 1ms/step - loss: 0.2013
Epoch 5/10
100/100 [==============================] - 0s 1ms/step - loss: 0.1771
Epoch 6/10
100/100 [==============================] - 0s 1ms/step - loss: 0.1676
Epoch 7/10
100/100 [==============================] - 0s 1ms/step - loss: 0.1568
Epoch 8/10
100/100 [==============================] - 0s 1ms/step - loss: 0.1473
Epoch 9/10
100/100 [==============================] - 0s 1ms/step - loss: 0.1400
Epoch 10/10
100/100 [==============================] - 0s 1ms/step - loss: 0.1343

此外，您可以通过比较模型拟合前后的值来验证权重是否确实得到了更新：

w0 = model.get_weights()
model.fit(np.random.rand(100,30,600), np.random.rand(100,30,600), epochs=10)

w1 = model.get_weights()
print(not any(np.allclose(x0, x1) for x0, x1 in zip(w0, w1)))
# => True

【讨论】：

我试过这样做。它允许模型编译，但不会应用密集层的权重更新（在反向传播期间计算）。因此，模型没有学习。
@hal9000 我试着用一些随机数据编译和拟合这个模型。由于损失在每个时期都在减少，因此该模型似乎正在学习。您能否详细说明您的尝试？也许您的代码中还有其他问题。
查看我的编辑以验证模型是否正在学习。也许您还可以检查是否有任何区别。
是的，你是对的，权重确实会更新。感谢您的帮助！

【解决方案2】：

您可以将 (30,600) 矩阵与 (600,30,600) 相乘，这样您将获得 (600,30,600)，如果您对最后一个维度求和，您应该得到您想要转置的结果。我在 numpy 中测试了这个，而不是在 tensorflow 中，但它应该是一样的

【讨论】：