# 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 plt
import numpy as np
import tensorflow as tf
import requests
from tensorflow.python.framework import ops
import os.path
import csvops.reset_default_graph()# Create graph
sess = tf.Session()###
# Obtain and prepare data for modeling
#### Set 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', newline='') 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%
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 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
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
loss_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 time
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()