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本文将实现逻辑回归算法,预测低出生体重的概率。
# Logistic Regression # 逻辑回归 #---------------------------------- # # This function shows how to use TensorFlow to # solve logistic regression. # y = sigmoid(Ax + b) # # We will use the low birth weight data, specifically: # y = 0 or 1 = low birth weight # x = demographic and medical history data import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import requests from tensorflow.python.framework import ops import os.path import csv ops.reset_default_graph() # Create graph sess = tf.Session() ### # Obtain and prepare data for modeling ### # name of data file birth_weight_file = 'birth_weight.csv' # download data and create data file if file does not exist in current directory if not os.path.exists(birth_weight_file): birthdata_url = 'https://github.com/nfmcclure/tensorflow_cookbook/raw/master/01_Introduction/07_Working_with_Data_Sources/birthweight_data/birthweight.dat' birth_file = requests.get(birthdata_url) birth_data = birth_file.text.split('\r\n') birth_header = birth_data[0].split('\t') birth_data = [[float(x) for x in y.split('\t') if len(x)>=1] for y in birth_data[1:] if len(y)>=1] with open(birth_weight_file, "w") as f: writer = csv.writer(f) writer.writerow(birth_header) writer.writerows(birth_data) f.close() # read birth weight data into memory birth_data = [] with open(birth_weight_file, newline='') as csvfile: csv_reader = csv.reader(csvfile) birth_header = next(csv_reader) for row in csv_reader: birth_data.append(row) birth_data = [[float(x) for x in row] for row in birth_data] # Pull out target variable y_vals = np.array([x[0] for x in birth_data]) # Pull out predictor variables (not id, not target, and not birthweight) x_vals = np.array([x[1:8] for x in birth_data]) # set for reproducible results seed = 99 np.random.seed(seed) tf.set_random_seed(seed) # Split data into train/test = 80%/20% # 分割数据集为测试集和训练集 train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False) test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices))) x_vals_train = x_vals[train_indices] x_vals_test = x_vals[test_indices] y_vals_train = y_vals[train_indices] y_vals_test = y_vals[test_indices] # Normalize by column (min-max norm) # 将所有特征缩放到0和1区间(min-max缩放),逻辑回归收敛的效果更好 # 归一化特征 def normalize_cols(m): col_max = m.max(axis=0) col_min = m.min(axis=0) return (m-col_min) / (col_max - col_min) x_vals_train = np.nan_to_num(normalize_cols(x_vals_train)) x_vals_test = np.nan_to_num(normalize_cols(x_vals_test)) ### # Define Tensorflow computational graph¶ ### # Declare batch size batch_size = 25 # Initialize placeholders x_data = tf.placeholder(shape=[None, 7], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32) # Create variables for linear regression A = tf.Variable(tf.random_normal(shape=[7,1])) b = tf.Variable(tf.random_normal(shape=[1,1])) # Declare model operations model_output = tf.add(tf.matmul(x_data, A), b) # Declare loss function (Cross Entropy loss) loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=model_output, labels=y_target)) # Declare optimizer my_opt = tf.train.GradientDescentOptimizer(0.01) train_step = my_opt.minimize(loss) ### # Train model ### # Initialize variables init = tf.global_variables_initializer() sess.run(init) # Actual Prediction # 除记录损失函数外,也需要记录分类器在训练集和测试集上的准确度。 # 所以创建一个返回准确度的预测函数 prediction = tf.round(tf.sigmoid(model_output)) predictions_correct = tf.cast(tf.equal(prediction, y_target), tf.float32) accuracy = tf.reduce_mean(predictions_correct) # Training loop # 开始遍历迭代训练,记录损失值和准确度 loss_vec = [] train_acc = [] test_acc = [] for i in range(1500): rand_index = np.random.choice(len(x_vals_train), size=batch_size) rand_x = x_vals_train[rand_index] rand_y = np.transpose([y_vals_train[rand_index]]) sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y}) temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y}) loss_vec.append(temp_loss) temp_acc_train = sess.run(accuracy, feed_dict={x_data: x_vals_train, y_target: np.transpose([y_vals_train])}) train_acc.append(temp_acc_train) temp_acc_test = sess.run(accuracy, feed_dict={x_data: x_vals_test, y_target: np.transpose([y_vals_test])}) test_acc.append(temp_acc_test) if (i+1)%300==0: print('Loss = ' + str(temp_loss)) ### # Display model performance ### # 绘制损失和准确度 plt.plot(loss_vec, 'k-') plt.title('Cross Entropy Loss per Generation') plt.xlabel('Generation') plt.ylabel('Cross Entropy Loss') plt.show() # Plot train and test accuracy plt.plot(train_acc, 'k-', label='Train Set Accuracy') plt.plot(test_acc, 'r--', label='Test Set Accuracy') plt.title('Train and Test Accuracy') plt.xlabel('Generation') plt.ylabel('Accuracy') plt.legend(loc='lower right') plt.show()
数据结果:
Loss = 0.845124
Loss = 0.658061
Loss = 0.471852
Loss = 0.643469
Loss = 0.672077
迭代1500次的交叉熵损失图
迭代1500次的测试集和训练集的准确度图
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持。
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稳了!魔兽国服回归的3条重磅消息!官宣时间再确认!
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