83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
#====================================================================
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# 使用 Python API , 拟合一个平面，训练、测试，并画图显示
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# 麻雀虽小五脏俱全!
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#====================================================================
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import tensorflow as tf
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import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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# 训练数据
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x_train = np.float32(np.random.rand(2, 100)) # 随机输入
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y_train = np.dot([5.000, 7.000], x_train) + 2.000
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# 构造一个线性模型
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b = tf.Variable(tf.zeros([1]))
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W = tf.Variable(tf.zeros([1,2]))
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y = tf.matmul(W, x_train) + b
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# 最小化方差
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loss = tf.reduce_mean(tf.square(y - y_train))
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optimizer = tf.train.GradientDescentOptimizer(0.5)
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train = optimizer.minimize(loss)
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# 启动图 (graph)
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sess = tf.Session()
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sess.run(tf.global_variables_initializer())
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# 训练：拟合平面
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m = 101
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n = 0
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W_temp = np.zeros([m // 20 + 1, 2])
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b_temp = np.zeros([1, m // 20 + 1])
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for step in range(0, m):
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sess.run(train)
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if step % 20 == 0:
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temp = sess.run(W)
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W_temp[n] = temp[0] # 注意：列表和数组属于不同类型数据，否则赋值报错！！
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b_temp[0][n] = sess.run(b)
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print(step, sess.run(W), sess.run(b))
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n = n + 1
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W_temp = np.transpose(W_temp)
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# 参数画图
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fig = plt.figure()
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step=np.arange(0,m,20)
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plt.plot(step,W_temp[0], marker='o', mec='b', mfc='b',label=u'W1')
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plt.plot(step,W_temp[1], marker='*', ms=10,label=u'W2')
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plt.plot(step,b_temp[0], marker='^', ms=10,label=u'b')
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plt.legend()
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plt.xlabel('step')
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plt.ylabel('value')
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plt.title('Parameter')
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# 测试数据.
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x_test = np.float32(np.random.rand(2, 100))
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y_test = np.dot([5.000, 7.000], x_test) + 2.000
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# 测试：Test trained model
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y_=sess.run(tf.matmul(W, x_test) + b)
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y = abs(y_-y_test)
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y=np.where(y>0.01,y,1) # 数组y_中，如果元素大于一个给定的数0.001，则值保留，否则元素被1替代。
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accuracy=np.sum(y==1)/len(y[0,:])
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print('accuracy:',accuracy)
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sess.close()
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# 测试结果数据画图对比
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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xs = x_test[0]
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ys = x_test[1]
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zs1 = y_test
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zs2 = y_
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ax.scatter(xs, ys, zs1, c='r', marker='v')
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ax.scatter(xs, ys, zs2, c='b', marker='^')
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.set_zlabel('z')
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# 显示以上画的两个图
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plt.show() |