paddle训练好的数字识别模型

PaddlePaddle可以用来训练数字识别模型。一个常用的数字识别数据集是MNIST，包含了大量手写数字的图片。你可以使用PaddlePaddle的API来加载MNIST数据集，构建卷积神经网络模型，并进行训练和测试。

以下是一个简单的例子，使用PaddlePaddle训练一个数字识别模型：

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

# 定义网络结构
def convolutional_neural_network(input_layer):
    conv1 = fluid.layers.conv2d(input_layer, num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
    pool1 = fluid.layers.pool2d(conv1, pool_size=2, pool_type='max', pool_stride=2)
    conv2 = fluid.layers.conv2d(pool1, num_filters=64, filter_size=5, stride=1, padding=2, act='relu')
    pool2 = fluid.layers.pool2d(conv2, pool_size=2, pool_type='max', pool_stride=2)
    fc1 = fluid.layers.fc(pool2, size=512, act='relu')
    drop1 = fluid.layers.dropout(fc1, dropout_prob=0.5)
    fc2 = fluid.layers.fc(drop1, size=10, act='softmax')
    return fc2

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

# 定义前向计算图
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=0.001)
opts = optimizer.minimize(avg_cost)

# 定义训练和测试程序
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# 加载数据集
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)

# 定义训练过程
for epoch in range(10):
    for batch_id, data in enumerate(train_reader()):
        train_cost, train_acc = exe.run(program=fluid.default_main_program(),
                                        feed={'image': np.array([x[0].reshape(1, 28, 28) for x in data], dtype='float32'),
                                              'label': np.array([x[1] for x in data], dtype='int64')},
                                        fetch_list=[avg_cost, predict.accuracy])
        if batch_id % 100 == 0:
            print('Epoch {}, Batch {}, Cost {}, Accuracy {}'.format(epoch, batch_id, train_cost[0], train_acc[0]))

    # 测试模型
    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[0].reshape(1, 28, 28) for x in data], dtype='float32'),
                                            'label': np.array([x[1] for x in data], dtype='int64')},
                                      fetch_list=[avg_cost, predict.accuracy])
        test_accs.append(test_acc[0])
        test_costs.append(test_cost[0])
    test_cost = sum(test_costs) / len(test_costs)
    test_acc = sum(test_accs) / len(test_accs)
    print('Test Epoch {}, Cost {}, Accuracy {}'.format(epoch, test_cost, test_acc))

# 保存模型
fluid.io.save_inference_model(dirname='./model', feeded_var_names=['image'], target_vars=[predict], executor=exe)

在训练完成后，你可以使用保存的模型来进行数字识别。以下是一个简单的例子，加载训练好的模型，输入一张手写数字的图片，输出识别结果：

import cv2

# 加载模型
infer_exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
    [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(dirname='./model', executor=infer_exe)

# 读取手写数字的图片
img = cv2.imread('test.png', cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (28, 28), interpolation=cv2.INTER_AREA)
img = img.astype(np.float32) / 255.0
img = np.reshape(img, [1, 1, 28, 28])

# 进行数字识别
results = infer_exe.run(program=inference_program,
                        feed={feed_target_names[0]: img},
                        fetch_list=fetch_targets)
print('The digit is classified as:', np.argmax(results[0]))

注意：在使用以上代码时，需要先安装PaddlePaddle和MNIST数据集。