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Dogbreed_cnn_model.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 23 15:37:58 2018
@author: alok
"""
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 22 18:57:07 2018
@author: alok
"""
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 21 22:26:13 2018
@author: alok
"""
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 20 09:40:13 2018
@author: alok
"""
import pandas as pd
import tensorflow as tf
import cv2
import numpy as np
import matplotlib.pyplot as plt
import math
import time
from datetime import timedelta
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from sklearn.model_selection import train_test_split
def conv2d(x, W, b, strides=1,padding='VALID'):
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='VALID')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x,f = 3, k=2,padding = 'VALID'):
return tf.nn.max_pool(x, ksize=[1, f, f, 1], strides=[1, k, k, 1],padding='VALID')
def conv_net(x, weights, biases, dropout):
conv1 = conv2d(x, weights['wc1'], biases['bc1'],strides=2)
conv1 = tf.nn.relu(conv1)
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'],strides=1)
conv2 = tf.nn.relu(conv2)
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'],strides=1,padding='SAME')
conv3 = maxpool2d(conv3,f=3, k=2)
conv3 = tf.nn.relu(conv3)
conv4 = conv2d(conv3, weights['wc4'], biases['bc4'],strides=1)
conv4 = tf.nn.relu(conv4)
print(conv4.shape)
fc1 = tf.reshape(conv4, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
fc2 = tf.add(tf.matmul(fc1, weights['wd2']), biases['bd2'])
fc2 = tf.nn.relu(fc2)
fc2 = tf.nn.dropout(fc2, dropout)
out = tf.add(tf.matmul(fc2, weights['out']), biases['out'])
return out
def random_mini_batches(X, Y, mini_batch_size ):
m = X.shape[0]
mini_batches = []
# Shuffle (X, Y)
permutation = list(np.random.permutation(m))
shuffled_X = X[permutation,:]
shuffled_Y = Y[permutation,:]
# Partition
num_complete_minibatches = int(math.floor(m/mini_batch_size) )
for k in range(0, num_complete_minibatches):
mini_batch_X = shuffled_X[ k * mini_batch_size : k * mini_batch_size + mini_batch_size,:]
mini_batch_Y = shuffled_Y[ k * mini_batch_size : k * mini_batch_size + mini_batch_size,:]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
# Handling the end case (last mini-batch < mini_batch_size)
if m % mini_batch_size != 0:
mini_batch_X = shuffled_X[num_complete_minibatches * mini_batch_size : m,:]
mini_batch_Y = shuffled_Y[num_complete_minibatches * mini_batch_size : m,:]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
return mini_batches
def main_func(x,y,learning_rate = 0.005, num_epochs = 5,minibatch_size = 1000):
num_classes = 120
height = 81
width = 81
weights = { 'wc1': tf.Variable(tf.random_normal([3, 3, 3, 32])),
'wc2': tf.Variable(tf.random_normal([3, 3, 32, 32])),
'wc3': tf.Variable(tf.random_normal([3, 3, 32, 64])),
'wc4': tf.Variable(tf.random_normal([3, 3, 64, 128])),
'wd1': tf.Variable(tf.random_normal([15*15*128, 256])),
'wd2': tf.Variable(tf.random_normal([256, 512])),
'out': tf.Variable(tf.random_normal([512, num_classes]))}
biases = { 'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([32])),
'bc3': tf.Variable(tf.random_normal([64])),
'bc4': tf.Variable(tf.random_normal([128])),
'bd1': tf.Variable(tf.random_normal([256])),
'bd2': tf.Variable(tf.random_normal([512])),
'out': tf.Variable(tf.random_normal([num_classes]))}
acc = []
costs = []
test = []
num_classes = 120
train_x,test_x,train_y,test_y=train_test_split(x,y,test_size = 0.20)
X = tf.placeholder(tf.float32, [None, height,width,3])
Y = tf.placeholder(tf.float32, [None, num_classes])
keep_prob = tf.placeholder(tf.float32)
logits = conv_net(X, weights, biases, keep_prob)
prediction = tf.nn.softmax(logits)
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss_op)
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
# saver = tf.train.Saver(max_to_keep = 1)#
# writer = tf.summary.FileWriter('/home/alok/spyder/tensorboard/breed/1/',tf.get_default_graph())
# tf.summary.scalar('accuracy',accuracy)
# tf.summary.scalar('cost',loss_op)
# merged = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(init)
for epoch in range(num_epochs):
start_time = time.time()
epoch_cost = 0.
epoch_accuracy = 0.
epoch_test_accuracy = 0.
num_minibatches = int(10222 / minibatch_size)
minibatches = random_mini_batches(dogs, breed, minibatch_size)
#running loop over different minibatches
for minibatch in minibatches:
(batch_x, batch_y) = minibatch
#cost calculation
_, minibatch_cost = sess.run([optimizer,loss_op],feed_dict={X:batch_x, Y:batch_y, keep_prob:0.8})
# _, minibatch_cost,summ = sess.run([optimizer,loss_op,merged],feed_dict={X:batch_x, Y:batch_y, keep_prob:0.8})
epoch_cost += 0.001*minibatch_cost / num_minibatches
# writer.add_summary(summ,epoch)
minibatch_acc = sess.run(accuracy, feed_dict={X: batch_x,Y:batch_y,keep_prob: 1.0})
epoch_accuracy += minibatch_acc / num_minibatches
#test accuracy
test_acc = sess.run(accuracy, feed_dict={X: test_x,Y:test_y,keep_prob: 1.0})
epoch_test_accuracy += test_acc / num_minibatches
print('cost'+str(minibatch_cost)+'train_acc'+str(minibatch_acc)+'test_acc'+str(test_acc))
costs.append(epoch_cost)
acc.append(epoch_accuracy)
test.append(epoch_test_accuracy)
print(str(epoch)+' epoch,' +' epoch_cost: '+ str(epoch_cost)+', epoch_accuracy: '+str(epoch_accuracy)+'test_accuray: '+str(epoch_test_accuracy))
end_time = time.time()
time_dif = end_time - start_time
print("Time usage: " + str(timedelta(seconds=int(round(time_dif)))))
# save after every 5th iteration
# if(epoch %5 == 0):
# saver.save(sess,save_path='/home/alok/spyder/checkpoints/breed/1/',global_step=epoch)
plt.plot(np.arange(len(costs)),costs)
plt.plot(np.arange(len(acc)),acc)
plt.plot(np.arange(len(test)),test)
# writer.close()
return costs,acc,test
data = pd.read_csv('labels.csv')
names = data.id
dogs = []
breed = []
#reading images
for i in range(len(names)):
img = cv2.imread(names[i]+'.jpg')
img = cv2.resize(img,(81,81), interpolation = cv2.INTER_CUBIC)
dogs.append(img/255.0)
breed.append(data.breed[i])
dogs = np.array(dogs)
breed = np.array(breed)
#encoding breed names
enc = LabelEncoder()
breed = enc.fit_transform(breed)
breed = breed.reshape(10222,1)
henc = OneHotEncoder()
breed = henc.fit_transform(breed).toarray()
#get cost , train and test accuracy
costs,acc,test= main_func(dogs,breed,learning_rate = 0.005, num_epochs = 50,minibatch_size = 1000)