TensorFlow2_200729系列---23、卷积神经网络实例
一、总结
一句话总结：
A、layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
B、layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
C、layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
二、卷积神经网络实例
博客对应课程的视频位置：
import os os.environ['TF_CPP_MIN_LOG_LEVEL']='2' import tensorflow as tf from tensorflow.keras import layers, optimizers, datasets, Sequential tf.random.set_seed(2345) # 卷积层 conv_layers = [ # 5 units of conv + max pooling # 5个单元 # unit 1 layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 2 layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 3 layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 4 layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 5 layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same') ] # 归一化处理 def preprocess(x, y): # [0~1] x = tf.cast(x, dtype=tf.float32) / 255. y = tf.cast(y, dtype=tf.int32) return x,y # 加载数据 (x,y), (x_test, y_test) = datasets.cifar100.load_data() # 挤压 y = tf.squeeze(y, axis=1) y_test = tf.squeeze(y_test, axis=1) print(x.shape, y.shape, x_test.shape, y_test.shape) # 训练数据 train_db = tf.data.Dataset.from_tensor_slices((x,y)) train_db = train_db.shuffle(1000).map(preprocess).batch(128) # 测试数据 test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test)) test_db = test_db.map(preprocess).batch(64) # 数据样例 sample = next(iter(train_db)) print('sample:', sample[0].shape, sample[1].shape, tf.reduce_min(sample[0]), tf.reduce_max(sample[0])) def main(): # 卷积层 # [b, 32, 32, 3] => [b, 1, 1, 512] conv_net = Sequential(conv_layers) # 全连接层 fc_net = Sequential([ layers.Dense(256, activation=tf.nn.relu), layers.Dense(128, activation=tf.nn.relu), # 100个分类，输出 layers.Dense(100, activation=None), ]) # 卷积层网络构建 conv_net.build(input_shape=[None, 32, 32, 3]) # 全连接网络构建 fc_net.build(input_shape=[None, 512]) # 优化函数 optimizer = optimizers.Adam(lr=1e-4) # [1, 2] + [3, 4] => [1, 2, 3, 4] # 所有的参数就是 卷积层的参数 和 全连接层的参数 variables = conv_net.trainable_variables + fc_net.trainable_variables for epoch in range(50): for step, (x,y) in enumerate(train_db): with tf.GradientTape() as tape: # 卷积层 # [b, 32, 32, 3] => [b, 1, 1, 512] out = conv_net(x) # reshape方便全连接层用 # flatten, => [b, 512] out = tf.reshape(out, [-1, 512]) # 全连接层 # [b, 512] => [b, 100] logits = fc_net(out) # [b] => [b, 100] y_onehot = tf.one_hot(y, depth=100) # compute loss：crossentropy loss = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True) loss = tf.reduce_mean(loss) grads = tape.gradient(loss, variables) optimizer.apply_gradients(zip(grads, variables)) if step %100 == 0: print(epoch, step, 'loss:', float(loss)) total_num = 0 total_correct = 0 for x,y in test_db: out = conv_net(x) out = tf.reshape(out, [-1, 512]) logits = fc_net(out) prob = tf.nn.softmax(logits, axis=1) pred = tf.argmax(prob, axis=1) pred = tf.cast(pred, dtype=tf.int32) correct = tf.cast(tf.equal(pred, y), dtype=tf.int32) correct = tf.reduce_sum(correct) total_num += x.shape[0] total_correct += int(correct) acc = total_correct / total_num print(epoch, 'acc:', acc) if __name__ == '__main__': main()