rockmine.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.model_selection import train_test_split

# Reading Dataset
def read_dataset():
	df = pd.read_csv('sonar-all-data.csv')
	print(df.shape)
	X = df[df.columns[0:60]].values
	y = df[df.columns[60]]

	#Encode th dependent Variables
	encoder = LabelEncoder()
	encoder.fit(y)
	y = encoder.transform(y)
	Y = one_hot_encode(y)
	#print(Y)
	print(X.shape)
	return(X, Y)

# Define the encoder function
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


# Read the Dataset
X, Y = read_dataset()

# Shuffle the dataset to mix rows because in beggining of dataset there are only mines and then rocks...so Shuffle.
X, Y = shuffle(X, Y, random_state = 1)

# Split the dataset into train and test
train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size = 0.20, random_state = 415)

# Check or quick look at the train and test shape
print("\nTrain and Test Shape")
print(train_x.shape)
print(train_y.shape)
print(test_x.shape)

# Define important parameters and variables to work with tensors
learning_rate = 0.3
training_epochs = 1000 #Total Number of iterations will be done to minimize the error
cost_history = np.empty(shape = [1], dtype = float)
n_dim = X.shape[1]
print("n_dim = ", n_dim)
n_class = 2
model_path = "/Users/rudrajikadra/Documents/Machine Learning Works/3) Rock and Mine Identifier/" #Path to store the model


### MULTILAYER PERCEPTRON

# Define number of hidden layers and number of neurons for each layer
n_hidden_1 = 60
n_hidden_2 = 60
n_hidden_3 = 60
n_hidden_4 = 60

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]) #Output of our model

#Define the model
def multilayer_perceptron(x, weights, biases):

	# Hidden Layer 1 with Sigmoid Activation function
	layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
	layer_1 = tf.nn.sigmoid(layer_1)

	# Hidden Layer 2 with Sigmoid Activation function
	layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
	layer_2 = tf.nn.sigmoid(layer_2)

	# Hidden Layer 3 with Sigmoid Activation function
	layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])
	layer_3 = tf.nn.sigmoid(layer_3)

	# Hidden Layer 4 with Relu Activation function
	layer_4 = tf.add(tf.matmul(layer_3, weights['h4']), biases['b4'])
	layer_4 = tf.nn.relu(layer_4)

	# Output layer with linear activation
	out_layer = tf.matmul(layer_4, weights['out']) + biases['out']
	return out_layer


# Define the weights and biases for each layer

weights = {
	'h1' : tf.Variable(tf.truncated_normal([n_dim, n_hidden_1])), #60 x 60
	'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]))
}

# Initialize all the variables
init = tf.global_variables_initializer()

# Saver object in order to save the model
saver = tf.train.Saver() 


# Call the model Defined above
y = multilayer_perceptron(x, weights, biases)


# Define the cost/loss function and optimizer
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)


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


# Calculte the cost and accuracy of each epoch
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)) #Difference between the actual output and model outputs
	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)) #Mean square error
	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_, "- Train Accuracy: ", accuracy)

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


# Plot mse and accuracy grapth
plt.plot(mse_history, 'r')
plt.show()
plt.plot(accuracy_history, 'b')
plt.show()

# Print the final accuracy
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_: test_y})))

# Print the final mean square error
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))