TensorFlow模型的火车准确性保持为零

Question

我构建基于此 Architection 进行二进制分类[“ 0” 对于“延迟不敏感” ，“ 1” for “ Interactive” ]使用 5功能 。目标列为 vmcategory 。当我训练模型时，精度保持在零。

您可以检查 norefollow noreferrer“

Epoch 1/100
1/1 [==============================] - 29s 29s/step - loss: 0.6931 - accuracy: 0.0000e+00
Epoch 2/100
1/1 [==============================] - 7s 7s/step - loss: 0.6893 - accuracy: 0.0000e+00
Epoch 3/100
1/1 [==============================] - 7s 7s/step - loss: 0.6808 - accuracy: 0.0000e+00
Epoch 4/100
1/1 [==============================] - 7s 7s/step - loss: 0.6571 - accuracy: 0.0000e+00
Epoch 5/100
1/1 [==============================] - 7s 7s/step - loss: 0.5957 - accuracy: 0.0000e+00
Epoch 6/100
1/1 [==============================] - 7s 7s/step - loss: 0.5372 - accuracy: 0.0000e+00
Epoch 7/100
1/1 [==============================] - 7s 7s/step - loss: 0.3760 - accuracy: 0.0000e+00
Epoch 8/100
1/1 [==============================] - 7s 7s/step - loss: 0.2411 - accuracy: 0.0000e+00
Epoch 9/100
1/1 [==============================] - 7s 7s/step - loss: 0.1913 - accuracy: 0.0000e+00
Epoch 10/100
1/1 [==============================] - 7s 7s/step - loss: 0.0571 - accuracy: 0.0000e+00
Epoch 11/100
1/1 [==============================] - 7s 7s/step - loss: 0.0483 - accuracy: 0.0000e+00
Epoch 12/100
1/1 [==============================] - 7s 7s/step - loss: 0.0088 - accuracy: 0.0000e+00
Epoch 13/100
1/1 [==============================] - 7s 7s/step - loss: 6.1697e-04 - accuracy: 0.0000e+00
Epoch 14/100
1/1 [==============================] - 6s 6s/step - loss: 3.2386e-04 - accuracy: 0.0000e+00
Epoch 15/100
1/1 [==============================] - 6s 6s/step - loss: 6.8086e-06 - accuracy: 0.0000e+00
Epoch 16/100
1/1 [==============================] - 6s 6s/step - loss: 7.7796e-05 - accuracy: 0.0000e+00
Epoch 17/100
1/1 [==============================] - 7s 7s/step - loss: 1.1021e-06 - accuracy: 0.0000e+00
Epoch 18/100
1/1 [==============================] - 6s 6s/step - loss: 2.7273e-07 - accuracy: 0.0000e+00
Epoch 87/100
1/1 [==============================] - 6s 6s/step - loss: 1.0003e-13 - accuracy: 0.0000e+00
Epoch 88/100
1/1 [==============================] - 6s 6s/step - loss: 2.6685e-14 - accuracy: 0.0000e+00
Epoch 89/100
1/1 [==============================] - 7s 7s/step - loss: 2.4792e-12 - accuracy: 0.0000e+00
Epoch 90/100
1/1 [==============================] - 7s 7s/step - loss: 1.2417e-13 - accuracy: 0.0000e+00
Epoch 91/100
1/1 [==============================] - 7s 7s/step - loss: 1.4707e-11 - accuracy: 0.0000e+00
Epoch 92/100
1/1 [==============================] - 7s 7s/step - loss: 4.9625e-14 - accuracy: 0.0000e+00
Epoch 93/100
1/1 [==============================] - 7s 7s/step - loss: 3.7239e-13 - accuracy: 0.0000e+00
Epoch 94/100
1/1 [==============================] - 7s 7s/step - loss: 6.0243e-13 - accuracy: 0.0000e+00
Epoch 95/100
1/1 [==============================] - 6s 6s/step - loss: 1.4047e-11 - accuracy: 0.0000e+00
Epoch 96/100
1/1 [==============================] - 7s 7s/step - loss: 1.0687e-14 - accuracy: 0.0000e+00
Epoch 97/100
1/1 [==============================] - 7s 7s/step - loss: 3.4614e-16 - accuracy: 0.0000e+00
Epoch 98/100
1/1 [==============================] - 7s 7s/step - loss: 4.5617e-11 - accuracy: 0.0000e+00
Epoch 99/100
1/1 [==============================] - 7s 7s/step - loss: 1.5913e-14 - accuracy: 0.0000e+00
Epoch 100/100
1/1 [==============================] - 7s 7s/step - loss: 3.0236e-10 - accuracy: 0.0000e+00

Answer 1

您正在使用准确性作为指标，该指标期望类标签是输入，但您正在提供类logits（或置信度）作为输入。请替换精度 tf.keras.metrics.categoricalaccuracy（）

- edit---

因此，我刚刚注意到的问题有不同的问题。您有223461，因此输入功能是5个长度的向量，目的是进行二进制分类。

您将输入样本视为特征向量，并且由于相同，您正在尝试预测223461类。为了解决此问题，您需要进行以下更改

，可以在体系结构中进行以下更改，

model.add(Dense(128, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(n_outputs, activation='sigmoid')) # here n_outputs should be 1

• 删除Conv1D和GRU层，您的输入功能是表格的，并且不需要卷积操作。
• 进行二进制分类时，用Sigmoid替换SoftMax功能。
• 用“准确性”替换分类ic的表格
• 确保您的数据的形状[x_batch，5]
• 确保您的y形状[x_batch，1]

在此处，x_batch可能具有形状[223461，5]，在复杂的模型中，您可能不会处理您整个数据中的一个循环，将使用小批量大小。