CIFAR10-ResNet18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 from resnet import resnet18 tf.random.set_seed(2345) /* 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 */ def preprocess(x, y): # [-1~1] x = tf.cast(x, dtype=tf.float32) / 255. - 0.5 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(512) test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test)) test_db = test_db.map(preprocess).batch(512) 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] model = resnet18() model.build(input_shape=(None, 32, 32, 3)) model.summary() optimizer = optimizers.Adam(lr=1e-3) for epoch in range(500): for step, (x,y) in enumerate(train_db): with tf.GradientTape() as tape: # [b, 32, 32, 3] => [b, 100] logits = model(x,training=True) # [b] => [b, 100] y_onehot = tf.one_hot(y, depth=100) # compute loss loss = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True) loss = tf.reduce_mean(loss) grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) if step %50 == 0: print(epoch, step, 'loss:', float(loss)) total_num = 0 total_correct = 0 for x,y in test_db: logits = model(x,training=False) 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()