python数字识别

Python数字识别可以使用深度学习框架TensorFlow来实现。以下是一个简单的数字识别的示例代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 加载MNIST数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 定义输入、输出、隐藏层的节点数
input_size = 784
output_size = 10
hidden_layer_size = 50

# 定义输入和输出的占位符
inputs = tf.placeholder(tf.float32, [None, input_size])
targets = tf.placeholder(tf.float32, [None, output_size])

# 定义权重和偏差
weights_1 = tf.get_variable("weights_1", [input_size, hidden_layer_size])
biases_1 = tf.get_variable("biases_1", [hidden_layer_size])
outputs_1 = tf.nn.relu(tf.matmul(inputs, weights_1) + biases_1)

weights_2 = tf.get_variable("weights_2", [hidden_layer_size, output_size])
biases_2 = tf.get_variable("biases_2", [output_size])
outputs = tf.matmul(outputs_1, weights_2) + biases_2

# 定义损失函数和优化器
loss = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets)
mean_loss = tf.reduce_mean(loss)
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(mean_loss)

# 定义精度
correct_predictions = tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))

# 开始训练
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for i in range(10000):
        inputs_batch, targets_batch = mnist.train.next_batch(100)
        _, loss_value = sess.run([optimizer, mean_loss], feed_dict={inputs: inputs_batch, targets: targets_batch})

        if i % 1000 == 0:
            accuracy_value = sess.run(accuracy, feed_dict={inputs: mnist.test.images, targets: mnist.test.labels})
            print("Iteration: {}, Loss: {}, Test Accuracy: {}".format(i, loss_value, accuracy_value))

这个代码使用了一个具有一个隐藏层的简单的神经网络来进行数字识别。训练过程中，我们使用MNIST数据集进行训练和测试。在训练10000次后，模型可以达到大约98%的准确率。你可以尝试调整模型参数以提高准确率。