87 lines
2.3 KiB
Python
87 lines
2.3 KiB
Python
# -*- coding=utf-8 -*-
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import numpy as np
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import tensorflow as tf
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from sklearn.metrics import mean_squared_error, r2_score
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def normalize(x):
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mean = np.mean(x)
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std = np.std(x)
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return (x - mean) / std
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def append_bias_reshape(features, labels):
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m = features.shape[0]
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n = features.shape[1]
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x = np.reshape(np.c_[np.ones(m), features], [m, n + 1])
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y = np.reshape(labels, [m, 1])
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return x, y
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def load_data():
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p = r'/Users/light/workspace/courses/numerical_analysis/8/wine.csv'
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with open(p, encoding='utf-8') as f:
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data = np.loadtxt(f, delimiter=";", skiprows=1).astype('float64')
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return data[:, :11], data[:, 11:]
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x_all, y_all = load_data()
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x_test = x_all[1400:, :11]
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y_test = y_all[1400:]
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y_train = y_all[:1400]
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x_train = normalize(x_all[:1400, 0:1])
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for i in range(1, 11):
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x_train = np.concatenate((x_train, normalize(x_all[:1400, i:i + 1])), 1)
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# x_train, y_train = append_bias_reshape(x_train, y_train)
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# dataset = tfdata.Dataset.from_tensor_slices((x_train, y_train))
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print(x_train.dtype)
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W = tf.Variable(np.random.random([11, 1]))
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b = tf.Variable(np.random.random([1, 1]))
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def linear_regression(x):
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return tf.matmul(x, W) + b
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# 均方差
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def mean_square(y_pred, y_true):
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return tf.reduce_sum(tf.pow(y_pred - y_true, 2)) / (2 * 1400)
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optimizer = tf.optimizers.SGD(0.01)
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# 优化过程
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def run_optimization():
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# 将计算封装在GradientTape中以实现自动微分
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with tf.GradientTape() as g:
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pred = linear_regression(x_train)
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loss = mean_square(pred, y_train)
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# 计算梯度
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gradients = g.gradient(loss, [W, b])
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# 按gradients更新 W 和 b
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optimizer.apply_gradients(zip(gradients, [W, b]))
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training_steps = 1000
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display_step = 50
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# 针对给定训练步骤数开始训练
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for step in range(1, training_steps + 1):
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# 运行优化以更新W和b值
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run_optimization()
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if step % display_step == 0:
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pred = linear_regression(x_train)
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loss = mean_square(pred, y_train)
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print("step: %i, loss: %f, W: %s, b: %s" % (step, loss, W.numpy(), b.numpy()))
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result = linear_regression(x_test).numpy().round()
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delta = result - y_test
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print(f"根均方误差(RMSE)：{np.sqrt(mean_squared_error(result, y_test))}")
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print(f"测试集R^2：{r2_score(y_test, result)}")
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