实现的是预测 低 出生 体重 的 概率。

尼克·麦克卢尔（Nick McClure）. TensorFlow机器学习实战指南 (智能系统与技术丛书) (Kindle 位置 1060-1061). Kindle 版本.

# Logistic Regression#----------------------------------## 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 dataimport matplotlib.pyplot as pltimport numpy as npimport tensorflow as tfimport requestsfrom tensorflow.python.framework import opsimport os.pathimport csvops.reset_default_graph()# Create graphsess = tf.Session()#### Obtain and prepare data for modeling#### Set name of data filebirth_weight_file = 'birth_weight.csv'# Download data and create data file if file does not exist in current directoryif 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', newline='') as f:        writer = csv.writer(f)        writer.writerow(birth_header)        writer.writerows(birth_data)        f.close()# Read birth weight data into memorybirth_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 variabley_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 resultsseed = 99np.random.seed(seed)tf.set_random_seed(seed)# Split data into train/test = 80%/20%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)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 sizebatch_size = 25# Initialize placeholdersx_data = tf.placeholder(shape=[None, 7], dtype=tf.float32)y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)# Create variables for linear regressionA = tf.Variable(tf.random_normal(shape=[7,1]))b = tf.Variable(tf.random_normal(shape=[1,1]))# Declare model operationsmodel_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 optimizermy_opt = tf.train.GradientDescentOptimizer(0.01)train_step = my_opt.minimize(loss)#### Train model#### Initialize variablesinit = tf.global_variables_initializer()sess.run(init)# Actual Predictionprediction = tf.round(tf.sigmoid(model_output))predictions_correct = tf.cast(tf.equal(prediction, y_target), tf.float32)accuracy = tf.reduce_mean(predictions_correct)# Training looploss_vec = []train_acc = []test_acc = []for i in range(15000):    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#### Plot loss over timeplt.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 accuracyplt.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()