tensorflow简单二维分类 simple_classification

官方代码： https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/302_simple_classification.py   训练前自己的数据
训练之后的数据（分类之后的数据）  
  代码详解

 """
    Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
    My Youtube Channel: https://www.youtube.com/user/MorvanZhou
    Dependencies:
    tensorflow: 1.1.0
    matplotlib
    numpy
    """
    import tensorflow as tf
    import matplotlib.pyplot as plt
    import numpy as np

    tf.set_random_seed(1)  #设置随机种子点
    np.random.seed(1)

    # fake data
    n_data = np.ones((100, 2))   #设置100行2列的矩阵，矩阵里面全是1
    x0 = np.random.normal(2*n_data, 1)      # class0 x shape=(100, 2)
    y0 = np.zeros(100)                      # class0 y shape=(100, )  #设置100行列数未知的矩阵，矩阵里面全是0
    x1 = np.random.normal(-2*n_data, 1)     # class1 x shape=(100, 2)
    y1 = np.ones(100)                       # class1 y shape=(100, )
    x = np.vstack((x0, x1))  # shape (200, 2) + some noise   #沿着竖直方向将矩阵堆叠起来。 
    y = np.hstack((y0, y1))  # shape (200, )   #np.hstack():在水平方向上平铺

    # plot data
    plt.scatter(x[:, 0], x[:, 1], c=y, s=100, lw=0, cmap='RdYlGn')   #显示散点图
    plt.show()

    tf_x = tf.placeholder(tf.float32, x.shape)     # input x   #占位符 后面必须有sess.run(predcit-y,feed={'xs':imput,'ys':output})
    tf_y = tf.placeholder(tf.int32, y.shape)     # input y

    # neural network layers
    l1 = tf.layers.dense(tf_x, 10, tf.nn.relu)          # hidden layer  添加层，相当与add_layer()
    output = tf.layers.dense(l1, 2)                     # output layer

    #与logits具有相同类型的加权损失Tensor.如果reduction是NONE,它的形状与labels相同；否则,它是标量.
    loss = tf.losses.sparse_softmax_cross_entropy(labels=tf_y, logits=output)           # compute cost

    accuracy = tf.metrics.accuracy(          # return (acc, update_op), and create 2 local variables 评估指标算子
        #argmax请参照  https://blog.csdn.net/qq575379110/article/details/70538051
        labels=tf.squeeze(tf_y), predictions=tf.argmax(output, axis=1),)[1]
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.05)  #优化器，传播误差
    train_op = optimizer.minimize(loss)

    sess = tf.Session()                                                                 # control training and others
    init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())  #对 TensorFlow 的多个操作进行分组
    sess.run(init_op)     # initialize var in graph

    plt.ion()   # something about plotting
    for step in range(100):
        # train and net output
        _, acc, pred = sess.run([train_op, accuracy, output], {tf_x: x, tf_y: y})
        if step % 2 == 0:
            # plot and show learning process
            plt.cla()
            plt.scatter(x[:, 0], x[:, 1], c=pred.argmax(1), s=100, lw=0, cmap='RdYlGn')
            plt.text(1.5, -4, 'Accuracy=%.2f' % acc, fontdict={'size': 20, 'color': 'red'})
            plt.pause(0.1)

    plt.ioff()
    plt.show()