Keras 中顺序网络的输出形状错误

Question

我有一个顺序网络，它接受长度为 20 个单词的向量句子，旨在根据标签对句子进行分类。每个词有 300 个维度。因此每个句子都有一个形状 (20, 300)。该数据集目前有 11 个样本，因此完整 x_train 的形状为 (11, 20, 300)

下面是我的网络的代码：

nnmodel = keras.Sequential()
nnmodel.add(keras.layers.InputLayer(input_shape = (20, 300)))
nnmodel.add(keras.layers.Dense(units = 300, activation = "relu"))
nnmodel.add(keras.layers.Dense(units = 20, activation = "relu"))
nnmodel.add(keras.layers.Dense(units = 1, activation = "sigmoid"))

nnmodel.compile(optimizer='adam', 
              loss='SparseCategoricalCrossentropy', 
              metrics=['accuracy'])
nnmodel.fit(x_train, y_train, epochs=10, batch_size = 1)
for layer in nnmodel.layers:
    print(layer.output_shape)

这给出：

Epoch 1/10
11/11 [==============================] - 0s 1ms/step - loss: 2.9727 - accuracy: 0.0455
Epoch 2/10
11/11 [==============================] - 0s 1ms/step - loss: 2.7716 - accuracy: 0.0682
Epoch 3/10
11/11 [==============================] - 0s 1ms/step - loss: 2.6279 - accuracy: 0.0682
Epoch 4/10
11/11 [==============================] - 0s 1ms/step - loss: 2.4878 - accuracy: 0.0682
Epoch 5/10
11/11 [==============================] - 0s 1ms/step - loss: 2.3145 - accuracy: 0.0545
Epoch 6/10
11/11 [==============================] - 0s 1ms/step - loss: 2.0505 - accuracy: 0.0545
Epoch 7/10
11/11 [==============================] - 0s 1ms/step - loss: 1.7010 - accuracy: 0.0545
Epoch 8/10
11/11 [==============================] - 0s 992us/step - loss: 1.2874 - accuracy: 0.0545
Epoch 9/10
11/11 [==============================] - 0s 891us/step - loss: 0.9628 - accuracy: 0.0545
Epoch 10/10
11/11 [==============================] - 0s 794us/step - loss: 0.7960 - accuracy: 0.0545
(None, 20, 300)
(None, 20, 20)
(None, 20, 1)

为什么我的输出层返回 (20,1)？它的形状必须是 (1)，因为我的标签只是一个整数。我很困惑，不确定如果形状错误它是如何计算损失的。

任何帮助将不胜感激/ 谢谢

Answer 1

使用当前代码，这是预期的输出。为多维输入添加一个简单的密集层只会改变最后一个维度的大小。如果你注意到，在 CNN 中，出于同样的原因，我们通常会在卷积层之后添加一个 Flatten。 Flatten 层本质上是重塑输入数组以删除额外的维度（每个样本现在都是一维的）。更新后的代码应该是：

nnmodel = keras.Sequential()
nnmodel.add(keras.layers.InputLayer(input_shape = (20, 300)))
nnmodel.add(keras.layers.Flatten()) #This is the code change
nnmodel.add(keras.layers.Dense(units = 300, activation = "relu"))
nnmodel.add(keras.layers.Dense(units = 20, activation = "relu"))
nnmodel.add(keras.layers.Dense(units = 1, activation = "sigmoid"))

nnmodel.compile(optimizer='adam', 
              loss='SparseCategoricalCrossentropy', 
              metrics=['accuracy'])
nnmodel.fit(x_train, y_train, epochs=10, batch_size = 1)
for layer in nnmodel.layers:
    print(layer.output_shape)

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