卷积神经网络demo

 双层卷积

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


def get_weight(shape):
    w = tf.random_normal(shape=shape, mean=1.0, stddev=1.0)
    w = tf.Variable(w)
    return w


def get_bais(shape):
    b = tf.random_normal(shape=shape, mean=1.0, stddev=1.0)
    b = tf.Variable(b)
    return b


def model():
    # 载入数据
    # [None,784]
    mnist = input_data.read_data_sets("./data/mnist/input_data/", one_hot=True)
    # 占位
    with tf.variable_scope("data"):
        x = tf.placeholder(tf.float32, shape=[None, 784])
        y_true = tf.placeholder(tf.int32, shape=[None, 10])
    # 构建第一层卷积
    with tf.variable_scope("first_jiji"):
        # 构建变量
        # 权重  [5,5,1,32]
        weight_1 = get_weight([5, 5, 1, 32])
        bais_1 = get_bais([32])
        x_reshape = tf.reshape(x, shape=[-1, 28, 28, 1])
        # 卷积
        x_conv2d = tf.nn.conv2d(x_reshape, filter=weight_1, strides=[1, 1, 1, 1], padding="SAME") + bais_1
        # 激活
        x_relu_1 = tf.nn.relu(x_conv2d)
        # 池化
        x_pool_1 = tf.nn.max_pool(x_relu_1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
    # 构建第二层卷积
    with tf.variable_scope("second_jiji"):
        # 构建变量
        # 权重  [5,5,1,32]
        weight_2 = get_weight([5, 5, 32, 64])
        bais_2 = get_bais([64])
        # 卷积
        x_conv2d_2 = tf.nn.conv2d(x_pool_1, filter=weight_2, strides=[1, 1, 1, 1], padding="SAME") + bais_2
        # 激活
        x_relu_2 = tf.nn.relu(x_conv2d_2)
        # 池化
        x_pool_2 = tf.nn.max_pool(x_relu_2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
    # 全连接
    with tf.variable_scope("full_connection"):
        w_fc = get_weight([7 * 7 * 64, 10])
        b_fc = get_bais([10])
        x_fc = tf.reshape(x_pool_2, [-1, 7 * 7 * 64])
        y_predict = tf.matmul(x_fc, w_fc) + b_fc
    return x, y_true, y_predict


def start_run():
    mnist = input_data.read_data_sets("./data/mnist/input_data/", one_hot=True)
    x, y_true, y_predict = model()
    # 求损失
    with tf.variable_scope("loss"):
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_predict))
    # 梯度下降
    with tf.variable_scope("optimizer"):
        train_op = tf.train.GradientDescentOptimizer(0.00001).minimize(loss)
    # 求准确率
    with tf.variable_scope("acc"):
        equal_list = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_predict, 1))

        # equal_list  None个样本   [1, 0, 1, 0, 1, 1,..........]
        accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32))

    init_op = tf.global_variables_initializer()
    with tf.variable_scope("merge"):
        tf.summary.scalar("losser", loss)
        meg = tf.summary.merge_all()
    # 开启会话
    with tf.Session() as sess:
        sess.run(init_op)
        file_writer = tf.summary.FileWriter("./tmp/juanji/brand/writer/data", sess.graph)
        for i in range(1000):
            # 得到数据
            mnist_x, mnist_y = mnist.train.next_batch(50)
            # 计算
            sess.run(train_op, feed_dict={x: mnist_x, y_true: mnist_y})
            print("训练第%d步,准确率为:%f" % (i, sess.run(accuracy, feed_dict={x: mnist_x, y_true: mnist_y})))
            summary = sess.run(meg, feed_dict={x: mnist_x, y_true: mnist_y})
            file_writer.add_summary(summary, i)


if __name__ == '__main__':
    start_run()

 简单神经网络

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


def simple():
    train = 0
    model_path = "./tmp/brand/model/data"
    # 加载数据
    mnist = input_data.read_data_sets("./data/mnist/input_data/", one_hot=True)
    # 建立占位符
    with tf.variable_scope("data"):
        x = tf.placeholder(tf.float32, shape=[None, 784])
        y_true = tf.placeholder(tf.int32, shape=[None, 10])
    # 创建模型
    with tf.variable_scope("model"):
        weight = tf.Variable(tf.random_normal(shape=[784, 10], mean=0.1, stddev=1.0))
        bias = tf.Variable(tf.random_normal(shape=[10], mean=0.1, stddev=1.0))

        y_predict = tf.matmul(x, weight) + bias
    # 求损失
    with tf.variable_scope("loss"):
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_predict))
    # 梯度下降
    with tf.variable_scope("optimizer"):
        train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
    # 求准确率
    with tf.variable_scope("acc"):
        equal_list = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_predict, 1))

        # equal_list  None个样本   [1, 0, 1, 0, 1, 1,..........]
        accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32))

    init_op = tf.global_variables_initializer()
    with tf.variable_scope("merge"):
        tf.summary.scalar("losser", loss)
        tf.summary.histogram("my_weight", weight)
        meg = tf.summary.merge_all()
        saver = tf.train.Saver()
    # 开启会话
    with tf.Session() as sess:
        sess.run(init_op)
        file_writer = tf.summary.FileWriter("./tmp/brand/writer/data", sess.graph)
        if os.path.exists("./tmp/brand/model/checkpoint"):
            saver.restore(sess, model_path)
        if train == 1:
            for i in range(1000):
                # 得到数据
                mnist_x, mnist_y = mnist.train.next_batch(50)
                # 计算
                sess.run(train_op, feed_dict={x: mnist_x, y_true: mnist_y})
                print("训练第%d步,准确率为:%f" % (i, sess.run(accuracy, feed_dict={x: mnist_x, y_true: mnist_y})))
                summary = sess.run(meg, feed_dict={x: mnist_x, y_true: mnist_y})
                file_writer.add_summary(summary, i)
            saver.save(sess, model_path)
        else:
            for i in range(40):
                x_test, y_test = mnist.train.next_batch(1)
                y_by_file = tf.arg_max(y_test, 1).eval()
                y_by_computer = tf.arg_max(sess.run(y_predict, feed_dict={x: x_test, y_true: y_test}), 1).eval()
                print("第%d次,真实数据%d,预测数据%d" % (i, y_by_file, y_by_computer))


if __name__ == '__main__':
    simple()