卷积神经网络与深度学习的崛起

### 卷积神经网络与深度学习的崛起 #### 1. 卷积神经网络基础 卷积神经网络（CNN）在处理图像等多维数据方面表现出色。以特定的卷积层为例，它有16个过滤器，每个过滤器是3×3的正方形。Keras会自动初始化过滤器中的值，就像初始化全连接网络的权重一样，该层的激活函数通常是ReLU。 下面是构建一个用于CIFAR - 10数据集的卷积神经网络的代码： ```python import numpy as np from keras.models import Sequential from keras.layers import Conv2D, Dropout, Dense from keras.layers import BatchNormalization, Flatten from keras.optimizers import Adam from keras.utils import to_categorical from keras.datasets import cifar10 (X_train_raw, Y_train_raw), (X_test_raw, Y_test_raw) = cifar10.load_data() X_train = X_train_raw / 255 X_test_all = X_test_raw / 255 X_validation, X_test = np.split(X_test_all, 2) Y_train = to_categorical(Y_train_raw) Y_validation, Y_test = np.split(to_categorical(Y_test_raw), 2) model = Sequential() model.add(Conv2D(16, (3, 3), activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.5)) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(1000, activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.5)) model.add(Dense(512, activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy']) history = model.fit(X_train, Y_train, validation_data=(X_validat ```