def train_mnist_model(): with tf.Session(config=session_config) as sess: mnist = create_inputs.read_data_sets(dataset_path, one_hot=True) # 计算有多少批次 n_batch = mnist.train.num_examples // batch_size # 构建网络 x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, class_num]) prediction, _ = model(x, input_shape, class_num) # 求loss loss = dk.cross_entropy_loss(prediction, y) # 设置优化器 global_step, train_step = dk.set_optimizer( lr_range, num_batches_per_epoch=n_batch, loss=loss) # 求acc accuracy = dk.get_acc(prediction, y) # 初始化变量 coord, threads = dk.init_variables_and_start_thread(sess) # 恢复model saver, start_epoch = dk.restore_model(sess, ckpt, restore_model=False) # 设置训练日志 summary_dict = {'loss': loss, 'accuracy': accuracy} summary_writer, summary_op = dk.set_summary(sess, logdir, summary_dict) # epoch=50 for epoch_th in range(epoch): for bat in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # 训练一个step _, loss_value, acc_value, summary_str, step = sess.run( [train_step, loss, accuracy, summary_op, global_step], feed_dict={ x: batch_xs, y: batch_ys }) # 显示结果 dk.print_message(epoch_th, bat, n_batch, step, loss_value, acc_value) # 保存summary if (step + 1) % 20 == 0: summary_writer.add_summary(summary_str, step) # 保存model if (((epoch_th + 1) % 50)) == 0: print('saving movdel.......') saver.save(sess, os.path.join(ckpt, 'model_{}.ckpt'.format(epoch_th)), global_step=global_step) dk.stop_threads(coord, threads)
def train_cifar10_model(): with tf.Session(config=session_config) as sess: #入口 train_x, train_y = create_inputs(is_train) x = tf.placeholder(tf.float32, shape=input_shape) y = tf.placeholder(tf.float32, shape=label_shape) # 构建网络 prediction, _ = model(x, input_shape, class_num) # 求loss loss = dk.cross_entropy_loss(prediction, y) # 设置优化器 global_step, train_step = dk.set_optimizer( lr_range, num_batches_per_epoch=n_batch_train, loss=loss) # 求acc accuracy = dk.get_acc(prediction, y) # 初始化变量 coord, threads = dk.init_variables_and_start_thread(sess) # 设置训练日志 summary_dict = {'loss': loss, 'accuracy': accuracy} summary_writer, summary_op = dk.set_summary(sess, logdir, summary_dict) # 恢复model saver, start_epoch = dk.restore_model(sess, ckpt, restore_model=False) # 显示参数量 dk.show_parament_numbers() start_epoch = 0 if restore_model: step = sess.run(global_step) start_epoch = int( step / n_batch_train / save_epoch_n) * save_epoch_n # 训练loop total_step = n_batch_train * epoch for epoch_n in range(epoch): pre_index = 0 since = time.time() acc_value_list = [] for n_batch in range(n_batch_train): if pre_index + batch_size < 50000: batch_x = train_x[pre_index:pre_index + batch_size] batch_y = train_y[pre_index:pre_index + batch_size] else: batch_x = train_x[pre_index:] batch_y = train_y[pre_index:] # 训练一个step _, loss_value, acc_value, summary_str, step = sess.run( [train_step, loss, accuracy, summary_op, global_step], feed_dict={ x: batch_x, y: batch_y }) # 显示结果 dk.print_message(epoch_n, n_batch, n_batch_train, step, loss_value, acc_value) # 保存summary if (step + 1) % 20 == 0: summary_writer.add_summary(summary_str, step) pre_index += batch_size # 保存结果 acc_value_list.append(acc_value) # 显示进度、耗时、最小最大平均值 seconds_mean = (time.time() - since) / n_batch_train dk.print_progress_and_time_massge(seconds_mean, step, total_step, acc_value_list) # 保存model if (((epoch_n + 1) % save_epoch_n)) == 0: print('epoch_n :{} saving movdel.......'.format(epoch_n)) saver.save(sess, os.path.join(ckpt, 'model_{}.ckpt'.format(epoch_n)), global_step=global_step) dk.stop_threads(coord, threads)
n_batch_train = int(train_number //batch_size) n_batch_test = int(test_number //batch_size) os.makedirs(ckpt,exist_ok=True) session_config = dk.set_gpu() with tf.Session(config = session_config) as sess: #入口 train_x, train_y = create_inputs(is_train) train_y = tf.one_hot(train_y, depth=class_num, axis=1, dtype=tf.float32) x = tf.placeholder(tf.float32, shape=input_shape) y = tf.placeholder(tf.float32, shape=label_shape) # 构建网络 prediction = model(x, input_shape,class_num,keep_prob=0.8) # 求loss loss = dk.cross_entropy_loss(prediction, y) # 设置优化器 global_step, train_step = dk.set_optimizer(num_batches_per_epoch=n_batch_train, loss=loss) # 求acc accuracy = dk.get_acc(prediction, y) # 初始化变量 coord, threads = dk.init_variables_and_start_thread(sess) # 设置训练日志 summary_dict = {'loss':loss,'accuracy':accuracy} summary_writer, summary_op = dk.set_summary(sess,logdir,summary_dict) # 恢复model saver = dk.restore_model(sess,ckpt,restore_model =restore_model) # 显示参数量 dk.show_parament_numbers() # 若恢复model,则重新计算start_epoch继续 start_epoch = 0