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LeNet-5是Yann LeCun于1998年设计的用于手写数字识别的经典卷积神经网络，被誉为深度学习领域的“Hello world！”。它曾被广泛应用于支票识别领域。近年来，我将LeNet-5迁移到昇腾训练芯片上，并分享了完整的训练实现和代码。以下是实现过程和详细说明。
昇腾910芯片目前支持TensorFlow 1.15版本，开发环境需按照官方文档进行配置。请确保已完成基础镜像的上传和启动。
以下命令用于挂载昇腾910的所有GPU资源，并准备好训练环境：
docker run -it --net=host --device=/dev/davinci0 --device=/dev/davinci1 --device=/dev/davinci2 --device=/dev/davinci3 --device=/dev/davinci4 --device=/dev/davinci5 --device=/dev/davinci6 --device=/dev/davinci7 --device=/dev/davinci_manager --device=/dev/devmm_svm --device=/dev/hisi_hdc -v /var/log/npu/slog/container/docker:/var/log/npu/slog -v /var/log/npu/conf/slog/slog.conf:/var/log/npu/conf/slog/slog.conf -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ -v /usr/local/Ascend/driver/tools/:/usr/local/Ascend/driver/tools/ -v /data/:/data/ -v /home/code:/home/local/code -v ~/context:/cache ubuntu_18.04-docker.arm64v8:v2 /bin/bash
LeNet-5由7层组成（不计输入层），每层都包含可训练的参数。其网络结构如下：
LeNet-5的设计相对简单，但在1990年代奠定了深度学习的基础。
本文使用LeNet-5作为训练案例，基于MNIST手写数字识别数据集。以下是训练脚本的代码：
import osimport numpy as npimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datafrom npu_bridge.estimator import npu_ops# 导入NPU算子库from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfigbatch_size = 100learning_rate = 0.1training_step = 10000model_save_path = "./model/"model_name = "model.ckpt"def train(mnist): x = tf.placeholder(tf.float32, [batch_size, mnist_inference.image_size, mnist_inference.image_size, mnist_inference.num_channels], name='x-input') y_ = tf.placeholder(tf.float32, [batch_size, mnist_inference.num_labels], name='y-input') regularizer = tf.contrib.layers.l2_regularizer(0.001) y = mnist_inference.inference(x, train=True, regularizer=regularizer) global_step = tf.Variable(0, trainable=False) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1)) cross_entropy_mean = tf.reduce_mean(cross_entropy) loss = cross_entropy_mean + tf.add_n(tf.get_collection("loss")) train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) saver = tf.train.Saver() config = tf.ConfigProto( allow_soft_placement=True, log_device_placement=False ) custom_op = config.graph_options.rewrite_options.custom_optimizers.add() custom_op.name = "NpuOptimizer" custom_op.parameter_map["use_off_line"].b = True writer = tf.summary.FileWriter("./log_dir", tf.get_default_graph()) writer.close() with tf.Session(config=config) as sess: tf.global_variables_initializer().run() start_time = time.time() for i in range(training_step): xs, ys = mnist.train.next_batch(batch_size) reshaped_xs = np.reshape(xs, (batch_size, mnist_inference.image_size, mnist_inference.image_size, mnist_inference.num_channels)) _, loss_value, step = sess.run([train_step, loss, global_step], feed_dict={x: reshaped_xs, y_: ys}) if i % 10 == 0: print("After %d training steps, loss on training batch is %g, total time in this 1000 steps is %s." % (step, loss_value, time.time() - start_time)) saver.save(sess, os.path.join(model_save_path, model_name), global_step=global_step) start_time = time.time()def main(): mnist = input_data.read_data_sets('MNIST_DATA/', one_hot=True) train(mnist)if __name__ == "__main__": main() 该代码实现了LeNet-5网络的训练过程。以下是主要部分的说明：
x和标签y_。运行上述代码时，模型将在./model/目录下生成训练结果。训练完成后，可以加载预训练模型进行推理任务。
LeNet-5是深度学习的经典网络之一，其实现和迁移至昇腾训练芯片的过程展示了从经典算法到现代硬件实现的演变。通过以上代码，读者可以轻松在昇腾平台上完成LeNet-5的训练和部署。
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