python 定义一个数组 accs

以下是定义一个数组`accs`的示例代码：

accs = []

这段代码创建了一个空的数组`accs`，可以用来存储一系列的准确率值。你可以根据需要向这个数组中添加元素，例如：

accs.append(0.85)
accs.append(0.92)

这样就可以将准确率值0.85和0.92添加到数组`accs`中了。

相关问题

iter1 = iter(accs)

This line of code creates an iterator called "iter1" using the built-in function "iter()" and passing in the list "accs" as an argument. The iterator will allow us to loop over the elements in the list one at a time.

帮我接入飞桨写一个手写字体识别python程序

好的，我可以回答这个问题。以下是一个使用飞桨实现手写数字识别的 Python 程序：

import paddle
import paddle.fluid as fluid
import numpy as np

# 定义网络结构
def convolutional_neural_network(img):
    # 第一层卷积层
    conv1 = fluid.layers.conv2d(input=img, num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
    # 第一层池化层
    pool1 = fluid.layers.pool2d(input=conv1, pool_size=2, pool_type='max', pool_stride=2)
    # 第二层卷积层
    conv2 = fluid.layers.conv2d(input=pool1, num_filters=64, filter_size=5, stride=1, padding=2, act='relu')
    # 第二层池化层
    pool2 = fluid.layers.pool2d(input=conv2, pool_size=2, pool_type='max', pool_stride=2)
    # 第三层卷积层
    conv3 = fluid.layers.conv2d(input=pool2, num_filters=64, filter_size=5, stride=1, padding=2, act='relu')
    # 第三层池化层
    pool3 = fluid.layers.pool2d(input=conv3, pool_size=2, pool_type='max', pool_stride=2)
    # 全连接层
    fc = fluid.layers.fc(input=pool3, size=512, act='relu')
    # 输出层
    out = fluid.layers.fc(input=fc, size=10, act='softmax')
    return out

# 定义输入数据
image = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')

# 获取网络输出结果
predict = convolutional_neural_network(image)

# 定义损失函数和优化器
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=.001)
opts = optimizer.minimize(avg_cost)

# 定义训练程序
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# 定义数据读取器
train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500), batch_size=128)
test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)

# 开始训练
for pass_id in range(10):
    # 训练
    for batch_id, data in enumerate(train_reader()):
        train_cost, train_acc = exe.run(program=fluid.default_main_program(),
                                        feed={'image': np.array([x[] for x in data]).astype('float32'),
                                              'label': np.array([x[1] for x in data]).astype('int64')},
                                        fetch_list=[avg_cost, predict])
        if batch_id % 100 == :
            print("Pass %d, Batch %d, Cost %f, Acc %f" % (pass_id, batch_id, train_cost[], train_acc[]))

    # 测试
    test_accs = []
    test_costs = []
    for batch_id, data in enumerate(test_reader()):
        test_cost, test_acc = exe.run(program=fluid.default_main_program(),
                                      feed={'image': np.array([x[] for x in data]).astype('float32'),
                                            'label': np.array([x[1] for x in data]).astype('int64')},
                                      fetch_list=[avg_cost, predict])
        test_accs.append(test_acc[])
        test_costs.append(test_cost[])
    test_cost = (sum(test_costs) / len(test_costs))
    test_acc = (sum(test_accs) / len(test_accs))
    print("Test: Cost %f, Acc %f" % (test_cost, test_acc))

这个程序使用了飞桨框架，实现了一个卷积神经网络，用于手写数字识别。训练数据使用了 MNIST 数据集，训练过程中会输出训练和测试的损失和准确率。