TensorFlow (三):逻辑回归

素颜马尾好姑娘i 2022-03-14 11:04 195阅读 0赞

### 逻辑回归 ###

*  代码
 *  结果

# 代码 #

import tensorflow as tf 
    import numpy as np 
    import matplotlib.pyplot as plt 
    
    # generate data
    
    data_size = 500
    x_data = np.random.rand(data_size, 2)
    noise = np.random.rand(data_size)
    noise = (noise - np.mean(noise)) * 0.5
    y_data = ((1 - x_data[:, 1]) > x_data[:, 0]).astype(np.float32)
    
    boundary = int(0.7 * data_size)
    x_train = x_data[:boundary]
    y_train = y_data[:boundary]
    x_test = x_data[boundary:]
    y_test = y_data[boundary:]
    
    x_true = x_data[np.where(y_data)]
    x_false = x_data[np.where(y_data == 0)]
    
    # modeling
    
    def sigmoid(x):
    	return 1.0 / (1 + tf.exp(-x))
    
    def accuracy(threshold = 0.5):
    	return tf.reduce_mean(tf.cast(tf.equal(tf.cast(y > threshold, tf.float32), y_[:, None]), tf.float32))
    
    my_w = 0
    my_b = 0
    learning_rate = 1.0
    nb_epoch = 100
    with tf.Graph().as_default():
    	x = tf.placeholder(dtype = tf.float32, shape = [None, 2])
    	w = tf.Variable(tf.random_uniform([2, 1]), dtype = tf.float32)
    	b = tf.Variable(tf.random_uniform([]), dtype = tf.float32)
    	y = sigmoid(tf.matmul(x, w) + b)
    	y_ = tf.placeholder(dtype = tf.float32, shape = [None])
    
    	cost = -tf.reduce_mean(y_[:, None] * tf.log(y) + (1 - y_[:, None]) * tf.log(1 - y))
    	train = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
    
    	train_cost_history = []
    	train_acc_history = []
    	test_cost_history = []
    	test_acc_history = []
    	accs = []
    	with tf.Session() as sess:
    		sess.run(tf.global_variables_initializer())
    		for i in range(nb_epoch):
    			# print("epoch %d" % i)
    			_, c, acc = sess.run([train, cost, accuracy()], feed_dict={ x:x_train, y_:y_train})
    			train_cost_history.append(c)
    			train_acc_history.append(acc)
    			c, acc = sess.run([cost, accuracy()], feed_dict={ x:x_test, y_:y_test})
    			test_cost_history.append(c)
    			test_acc_history.append(acc)
    		my_w = w.eval()
    		my_b = b.eval()
    
    
    # visualize
    
    plt.figure(figsize=(10, 4))
    plt.subplot(1, 2, 1)
    plt.title('Data')
    plt.scatter(x_true[:, 0], x_true[:, 1], 15.0, 'b', marker = 'x')
    plt.scatter(x_false[:, 0], x_false[:, 1], 15.0, 'g', marker = 'o')
    
    k = -my_w[0, 0] / my_w[1, 0]
    b = -my_b / my_w[1, 0]
    plt.plot(np.linspace(0, 1, 20), np.linspace(0, 1, 20) * k + b, 'r--')
    
    plt.subplot(1, 2, 2)
    plt.title('Loss')
    plt.plot(train_cost_history, '--')
    plt.plot(train_acc_history, '-')
    plt.plot(test_cost_history, '--')
    plt.plot(test_acc_history, '-')
    plt.legend(['train cost', 'train acc', 'test cost', 'test acc'])
    
    plt.show()

# 结果 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ZpbmNlZWVfXw_size_16_color_FFFFFF_t_70]

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