CIFAR10-VGG13深度学习卷积神经网络框架图形分类识别

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) conv_layers = [ # 5 units of conv + max pooling # 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') ] def preprocess(x, y): # [0~1] x = tf.cast(x, dtype=tf.float32) / 255. y = tf.cast(y, dtype=tf.int32) return x,y (x,y), (x_test, y_test) = datasets.cifar100.load_data() 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) train_db = tf.data.Dataset.from_tensor_slices((x,y)) train_db = train_db.shuffle(1000).map(preprocess).batch(128) test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test)) test_db = test_db.map(preprocess).batch(64) 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(): # [b, 32, 32, 3] => [b, 1, 1, 512] conv_net = Sequential(conv_layers) fc_net = Sequential([ layers.Dense(256, activation=tf.nn.relu), layers.Dense(128, activation=tf.nn.relu), layers.Dense(100, activation=None), ]) /* 开发不易，整理也不易，如需要详细的说明文档和程序，以及完整的数据集，训练好的模型，或者进一步开发， 可加作者新联系方式咨询，WX：Q3101759565，QQ：3101759565 */ 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()