# 使用 Keras 的用于 CIFAR10 的 ConvNets 让我们在 Keras 重复 LeNet CNN 模型构建和 CIFAR10 数据训练。我们保持架构与前面的示例相同，以便轻松解释概念。在 Keras 中，损失层添加如下： ```py model.add(Dropout(0.2)) ``` 用于 CIFAR10 CNN 模型的 Keras 中的完整代码在笔记本 `ch-09b_CNN_CIFAR10_TF_and_Keras` 中提供。 在运行模型时，我们得到以下模型描述： ```py _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 32, 32, 32) 1568 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32) 0 _________________________________________________________________ dropout_1 (Dropout) (None, 16, 16, 32) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 16, 16, 64) 32832 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64) 0 _________________________________________________________________ dropout_2 (Dropout) (None, 8, 8, 64) 0 _________________________________________________________________ flatten_1 (Flatten) (None, 4096) 0 _________________________________________________________________ dense_1 (Dense) (None, 1024) 4195328 _________________________________________________________________ dropout_3 (Dropout) (None, 1024) 0 _________________________________________________________________ dense_2 (Dense) (None, 10) 10250 ================================================================= Total params: 4,239,978 Trainable params: 4,239,978 Non-trainable params: 0 _________________________________________________________________ ``` 我们得到以下训练和评估结果： ```py Epoch 1/10 50000/50000 [====================] - 191s - loss: 1.5847 - acc: 0.4364 Epoch 2/10 50000/50000 [====================] - 202s - loss: 1.1491 - acc: 0.5973 Epoch 3/10 50000/50000 [====================] - 223s - loss: 0.9838 - acc: 0.6582 Epoch 4/10 50000/50000 [====================] - 223s - loss: 0.8612 - acc: 0.7009 Epoch 5/10 50000/50000 [====================] - 224s - loss: 0.7564 - acc: 0.7394 Epoch 6/10 50000/50000 [====================] - 217s - loss: 0.6690 - acc: 0.7710 Epoch 7/10 50000/50000 [====================] - 222s - loss: 0.5925 - acc: 0.7945 Epoch 8/10 50000/50000 [====================] - 221s - loss: 0.5263 - acc: 0.8191 Epoch 9/10 50000/50000 [====================] - 237s - loss: 0.4692 - acc: 0.8387 Epoch 10/10 50000/50000 [====================] - 230s - loss: 0.4320 - acc: 0.8528 Test loss: 0.849927025414 Test accuracy: 0.7414 ``` 再次，我们将其作为挑战，让读者探索并尝试不同的 LeNet 架构和超参数变体，以实现更高的准确性。