# **卷积神经网络** 也叫**convnet**，它是计算机视觉应用几乎都在使用的一种深度学习模型。 ## **简单的卷积神经网络** `Conv2D`层和`MaxPooling2D`层的堆叠 ~~~ model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) ~~~ ~~~ input_shape= (image_height, image_width, image_channels) ~~~ ~~~ _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 26, 26, 32) 320 _________________________________________________________________ max_pooling2d_1 (MaxPooling2D) (None, 13, 13, 32) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 11, 11, 64) 18496 _________________________________________________________________ max_pooling2d_2 (MaxPooling2D) (None, 5, 5, 64) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 3, 3, 64) 36928 ================================================================= ~~~ * [ ] 3D 输出展平为 1D ~~~ model.add(layers.Flatten()) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(10, activation='softmax')) ~~~ ~~~ _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 26, 26, 32) 320 _________________________________________________________________ max_pooling2d_1 (MaxPooling2D) (None, 13, 13, 32) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 11, 11, 64) 18496 _________________________________________________________________ max_pooling2d_2 (MaxPooling2D) (None, 5, 5, 64) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 3, 3, 64) 36928 _________________________________________________________________ flatten_1 (Flatten) (None, 576) 0 _________________________________________________________________ dense_1 (Dense) (None, 64) 36928 _________________________________________________________________ dense_2 (Dense) (None, 10) 650 ================================================================= ~~~ * [ ] 在 MNIST 图像上训练卷积神经网络 ~~~ test_acc = 0.99080000000000001 ~~~