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import tensorflow as tf
import matplotlib.pyplot as plt
import csv
import os
import os.path
import random
import numpy as np
import requests
from tensorflow.python.framework import ops
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# name of data file# name
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.writerows([birth_header])
writer.writerows(birth_data)
f.close()
# read birth weight data into memory
birth_data = []
with open(birth_weight_file, 'r') 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]
# Extract y-target (birth weight)
y_vals = np.array([x[8] for x in birth_data])
# Filter for features of interest
cols_of_interest = ['AGE', 'LWT', 'RACE', 'SMOKE', 'PTL', 'HT', 'UI']
x_vals = np.array([[x[ix] for ix, feature in enumerate(birth_header) if feature in cols_of_interest] for x in birth_data])
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ops.reset_default_graph()
sess = tf.Session()
batch_size = 100
seed = 3
np.random.seed(seed)
tf.set_random_seed(seed)
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]
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))
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def init_weight(shape, st_dev):
weight = tf.Variable(tf.random_normal(shape, stddev=st_dev))
return (weight)
def init_bias(shape, st_dev):
bias = tf.Variable(tf.random_normal(shape, stddev=st_dev))
return (bias)
x_data = tf.placeholder(shape=[None, 7], dtype=tf.float32)
y_target= tf.placeholder(shape=[None, 1], dtype=tf.float32)
def fully_connected(input_layer, weight, biases):
layer = tf.add(tf.matmul(input_layer, weight), biases)
return (tf.nn.relu(layer))
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#----- layer 1-----
weight_1 = init_weight(shape=[7, 25], st_dev=10.0)
bias_1 = init_bias(shape=[25], st_dev=10.0)
layer_1 = fully_connected(x_data, weight_1, bias_1)
#----- layer 2-----
weight_2 = init_weight(shape=[25, 10], st_dev=10.0)
bias_2 = init_bias(shape=[10], st_dev=10.0)
layer_2 = fully_connected(layer_1, weight_2, bias_2)
#----- layer 3-----
weight_3 = init_weight(shape=[10, 3], st_dev=10.0)
bias_3 = init_bias(shape=[3], st_dev=10.0)
layer_3 = fully_connected(layer_2, weight_3, bias_3)
#----- output layer-----
weight_4 = init_weight(shape=[3, 1], st_dev=10.0)
bias_4 = init_bias(shape=[1], st_dev=10.0)
final_output = fully_connected(layer_3, weight_4, bias_4)
loss = tf.reduce_mean(tf.abs(y_target - final_output))
my_opt = tf.train.AdadeltaOptimizer(0.05)
train_step = my_opt.minimize(loss)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
loss_vec = []
test_loss = []
for i in range(200):
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)
test_temp_loss = sess.run(loss, feed_dict={x_data: x_vals_test, y_target: np.transpose([y_vals_test])})
test_loss.append(test_temp_loss)
if (i + 1) % 25 == 0:
print('Generation. ' + str(i + 1) + '. Loss = ' + str(temp_loss))
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%matplotlib inline
# plot loss(MSE) over time
plt.plot(loss_vec, 'k-', label='Train Loss')
plt.plot(test_loss, 'r--', label='Test Loss')
plt.title('Loss (MSE) per Generation')
plt.legend(loc='upper right')
plt.xlabel('Generation')
plt.ylabel('Loss')
plt.show()
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