在飞浆平台使用vgg16实现图片识别代码

以下是在飞桨平台上使用VGG16实现图像识别的示例代码：

import paddle
import paddle.fluid as fluid

# 定义VGG16网络结构
def vgg16(input):
    def conv_block(input, num_filter, groups, num_channels=None):
        return fluid.nets.img_conv_group(
            input=input,
            num_filters=num_filter,
            conv_padding=1,
            conv_num_filter_groups=groups,
            conv_num_channels=num_channels,
            pool_stride=2,
            pool_padding=0,
            pool_size=2,
            pool_type='max')
    
    conv1 = conv_block(input, 64, 1)
    conv2 = conv_block(conv1, 128, 2)
    conv3 = conv_block(conv2, 256, 4)
    conv4 = conv_block(conv3, 512, 8)
    conv5 = conv_block(conv4, 512, 8)
    
    fc1 = fluid.layers.fc(input=conv5, size=4096, act='relu')
    fc2 = fluid.layers.fc(input=fc1, size=4096, act='relu')
    fc3 = fluid.layers.fc(input=fc2, size=1000, act='softmax')
    
    return fc3

# 定义输入层
input_shape = [None, 3, 224, 224]
input = fluid.layers.data(name='input', shape=input_shape, dtype='float32')

# 定义VGG16网络模型
output = vgg16(input)

# 定义损失函数和优化器
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=output, label=label)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
optimizer.minimize(avg_cost)

# 定义预测程序
test_program = fluid.default_main_program().clone(for_test=True)
predict = fluid.layers.argmax(output, axis=1)

# 定义训练和测试数据读取器
train_reader = paddle.batch(
    paddle.reader.shuffle(
        paddle.dataset.flowers.train(), buf_size=100),
    batch_size=32)
test_reader = paddle.batch(
    paddle.reader.shuffle(
        paddle.dataset.flowers.test(), buf_size=100),
    batch_size=32)

# 定义执行器
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# 开始训练
for pass_id in range(10):
    # 训练
    for batch_id, data in enumerate(train_reader()):
        train_cost, _ = exe.run(
            program=fluid.default_main_program(),
            feed={
                'input': fluid.dygraph.to_variable(
                    [x[0] for x in data]),
                'label': fluid.dygraph.to_variable(
                    [x[1] for x in data])
            },
            fetch_list=[avg_cost])
        
        if batch_id % 10 == 0:
            print("Pass %d, Batch %d, Cost %f" % 
                  (pass_id, batch_id, train_cost[0]))

    # 测试
    test_accs = []
    test_costs = []
    for batch_id, data in enumerate(test_reader()):
        test_cost, test_acc = exe.run(
            program=test_program,
            feed={
                'input': fluid.dygraph.to_variable(
                    [x[0] for x in data]),
                'label': fluid.dygraph.to_variable(
                    [x[1] for x in data])
            },
            fetch_list=[avg_cost, predict])
        
        test_accs.append(test_acc)
        test_costs.append(test_cost)
    
    test_acc = sum(test_accs) / len(test_accs)
    test_cost = sum(test_costs) / len(test_costs)
    print("Pass %d, Test Cost %f, Test Acc %f" % 
          (pass_id, test_cost[0], test_acc[0]))

在训练和测试数据读取器中，我们使用了PaddlePaddle自带的鲜花分类数据集。如果需要使用其他数据集进行训练，可以自己编写数据读取器。

在以上代码中，我们使用了fluid.dygraph.to_variable()函数将numpy数组转换为飞桨可识别的张量，使用exe.run()函数执行训练和测试程序。最后，我们输出了每个pass的训练损失和测试准确率。