87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
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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# TensorFlow 2.x 使用 Keras API
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from keras.optimizers import SGD
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# 确保我们正在使用 TensorFlow 2.x
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print("TensorFlow version:", tf.__version__)
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# 生成训练数据
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x_train = np.float32(np.random.rand(2, 100))
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y_train = np.dot([5.000, 7.000], x_train) + 2.000
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# 构建线性模型
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class LinearModel(tf.keras.Model):
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def __init__(self):
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super(LinearModel, self).__init__()
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self.W = tf.Variable(tf.zeros([1, 2]), name='weight')
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self.b = tf.Variable(tf.zeros([1]), name='bias')
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def call(self, inputs):
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return tf.matmul(self.W, inputs) + self.b
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model = LinearModel()
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# 定义损失函数和优化器
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loss_object = tf.keras.losses.MeanSquaredError()
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optimizer = SGD(learning_rate=0.5)
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# 训练步骤封装
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@tf.function
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def train_step(inputs, outputs):
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with tf.GradientTape() as tape:
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predictions = model(inputs)
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loss = loss_object(outputs, predictions)
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gradients = tape.gradient(loss, model.trainable_variables)
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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return loss
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# 训练模型
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m = 101
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W_temp, b_temp = [], []
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for step in range(m):
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loss = train_step(x_train, y_train)
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if step % 20 == 0:
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print("Step {:02d}: Loss {:.3f}, W {}, b {}".format(
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step, loss, model.W.numpy(), model.b.numpy()))
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W_temp.append(model.W.numpy()[0])
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b_temp.append(model.b.numpy()[0])
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# 参数可视化
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fig, ax = plt.subplots()
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step = np.arange(0, m, 20)
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ax.plot(step, [w[0] for w in W_temp], marker='o', label='W1')
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ax.plot(step, [w[1] for w in W_temp], marker='*', label='W2')
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ax.plot(step, b_temp, marker='^', label='b')
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ax.legend()
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ax.set_xlabel('step')
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ax.set_ylabel('value')
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ax.set_title('Parameter')
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# 生成测试数据
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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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# 测试模型
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y_pred = model(x_test)
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y_diff = abs(y_pred - y_test)
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y_diff = np.where(y_diff > 0.01, y_diff, 1)
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accuracy = np.sum(y_diff == 1) / len(y_diff[0])
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print('accuracy:', accuracy)
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# 测试结果可视化
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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xs, ys = x_test[0], x_test[1]
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zs1, zs2 = y_test, y_pred.numpy()[0]
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ax.scatter(xs, ys, zs1, c='r', marker='v') # 真实值
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ax.scatter(xs, ys, zs2, c='b', marker='^') # 预测值
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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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# 显示图表
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plt.show() |