ann.py

import matplotlib.pyplot as plt 
import tensorflow as tf 
import numpy as np 
import pandas as pd 
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import shuffle
#from sklearn.modek_selection import train_test_split
from sklearn.model_selection import train_test_split

def read_dataset():

	df = pd.read_csv('HR_comma_sep.csv')
	
	X = df.drop(['left','sales'],axis=1)
	y = df['left']

	encoder = LabelEncoder()
	encoder.fit(y)
	y = encoder.transform(y)
	Y = one_hot_encode(y)
	print(X.shape)
	return (X,Y)

def one_hot_encode(labels):
	n_labels = len(labels)
	n_unique_labels = len(np.unique(labels))
	one_hot_encode = np.zeros((n_labels,n_unique_labels))
	one_hot_encode[np.arange(n_labels),labels] = 1
	return one_hot_encode

X,Y = read_dataset()

X,Y = shuffle(X,Y,random_state = 1)

train_x,test_x,train_y,test_y = train_test_split(X,Y,test_size = 0.20)

print(train_x.shape)
print(train_y.shape)
print(test_x.shape)

learning_rate = 0.3
training_epochs = 100
cost_history = np.empty(shape=[1],dtype = float)
n_dim = X.shape[1]
print("n_dim",n_dim)
n_class = 2
model_path = "HR_comma_sep"

n_hidden_1 = 10
n_hidden_2 = 10
n_hidden_3 = 10
n_hidden_4 = 10

x = tf.placeholder(tf.float32,[None,n_dim])
W = tf.Variable(tf.zeros([n_dim,n_class]))
b = tf.Variable(tf.zeros([n_class]))
y_ = tf.placeholder(tf.float32,[None,n_class])

def multilayer_perceptron(x,weights,biases):

	layer_1 = tf.add(tf.matmul(x,weights['h1']),biases['b2'])
	layer_1 = tf.nn.sigmoid(layer_1)

	layer_2 = tf.add(tf.matmul(layer_1,weights['h2']),biases['b2'])
	layer_2 = tf.nn.sigmoid(layer_2)

	layer_3 = tf.add(tf.matmul(layer_2,weights['h3']),biases['b3'])
	layer_3 = tf.nn.sigmoid(layer_3)

	layer_4 = tf.add(tf.matmul(layer_3,weights['h4']),biases['b4'])
	layer_4 = tf.nn.sigmoid(layer_4)

	out_layer = tf.matmul(layer_4,weights['out']) + biases['out']
	return out_layer

weights = {
	
	'h1' : tf.Variable(tf.truncated_normal([n_dim,n_hidden_1])),
	'h2' : tf.Variable(tf.truncated_normal([n_hidden_1,n_hidden_2])),
	'h3' : tf.Variable(tf.truncated_normal([n_hidden_2,n_hidden_3])),
	'h4' : tf.Variable(tf.truncated_normal([n_hidden_3,n_hidden_4])),
	'out' : tf.Variable(tf.truncated_normal([n_hidden_4,n_class]))
}


biases = {
	
	'b1' : tf.Variable(tf.truncated_normal([n_hidden_1])),
	'b2' : tf.Variable(tf.truncated_normal([n_hidden_2])),
	'b3' : tf.Variable(tf.truncated_normal([n_hidden_3])),
	'b4' : tf.Variable(tf.truncated_normal([n_hidden_4])),
	'out' : tf.Variable(tf.truncated_normal([n_class]))
}

init = tf.global_variables_initializer()

saver = tf.train.Saver()

y = multilayer_perceptron(x,weights,biases)

cost_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y,labels=y_))
training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

sess = tf.Session()
sess.run(init)

mse_history = []
accuracy_history = []

for epoch in range(training_epochs):
	#sess.run(training_step,feed_dict = {x: train_x, y_ : train_y})
	cost = sess.run(cost_function,feed_dict = {x : train_x,y_ : train_y})
	cost_history = np.append(cost_history,cost)
	correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
	accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

	pred_y = sess.run(y,feed_dict = {x: test_x})
	mse = tf.reduce_mean(tf.square(pred_y - test_y))
	mse_ = sess.run(mse)
	mse_history.append(mse_)
	accuracy = (sess.run(accuracy,feed_dict = {x: train_x, y_: train_y}))
	accuracy_history.append(accuracy)

	print('epoch : ',epoch,' - ','cost: ',cost," -mse: ",mse_history)

#save_path = saver.save(sess,model_path)
#print("Model saved in file : %s" % save_path)

plt.plot(accuracy_history)
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.show()

correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print("Test Accuracy: ",(sess.run(accuracy,feed_dict= {x : test_x, y_: train_y})))

pred_y = sess.run(y,feed_dict = {x : test_x})
mse = tf.reduce_mean(tf.square(pred_y - test_y))
print("MSE : %.4f" % sess.run(mse))