By Mirza Yusuf
A classifier that identifies dogs and cats in Python using TensorFlow, making layers from scratch
In this project we will make a dogs and cat identifier. Using TensorFlow which is a library in Python.
Since the dataset is moderately big we don't need to use transfer learning. We will be building models from scratch .
The dataset we looking at already has divided the data into training and testing data so we don't need to worry about splitting it. Furthermore it around 5,000 images which is a moderate number when it comes to making a classifier. You can find the kaggle dataset here dogs & cats.
Before importing libraries we need to install the dependencies.
pip install tensorflow pip install numpy pip install pandas pip install cv2
import tensorflow as tf import numpy as np import pandas as pd import matplotlib.pyplot as plt import os from os import getcwd from os import listdir from matplotlib.image import imread import cv2 from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten import random
Set path and along with that check if the path is working by printing out one image.
path = '../input/dogs-cats-images/dataset/training_set/cats/cat.1.jpg' cath = cv2.imread(path) plt.imshow(cath, cmap='binary', interpolation='nearest')
Let's define a function that will preprocess the data as in resize the images and convert them to lists for easier conversion. Here we also convert the coloured images to grayscale so that identifying features becomes easier.
def preproces(path,path_test, label): train_image =  train_label =  test_image =  test_label =  for i in os.listdir(path): img = cv2.imread(path + '/' + i) res = cv2.resize(img, dsize=(128,128),interpolation=cv2.INTER_CUBIC) gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY) # Converting to grayscale train_image.append(gray) train_label.append(label) for j in os.listdir(path_test): img2 = cv2.imread(path_test + '/' + j) res2 = cv2.resize(img, dsize = (128,128), interpolation = cv2.INTER_CUBIC) gray2 = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY) test_image.append(gray2) test_label.append(label) return train_image, train_label, test_image, test_label
Now let's use the function to get both the images in a list and converge those lists together, keeping all training data for dogs and cats together and all test data along with both of their labels together. After that we need to convert it into a numpy array so that the data fits in the compiler.
train =  test =  trainlabel =  testlabel =  train_image, train_label, test_image, test_label = preproces('../input/dogs-cats-images/dataset/training_set/cats','../input/dogs-cats-images/dataset/test_set/cats', 0) train.extend(train_image) trainlabel.extend(train_label) test.extend(test_image) testlabel.extend(test_label) #dogs train_image, train_label, test_image, test_label = preproces('../input/dogs-cats-images/dataset/training_set/dogs','../input/dogs-cats-images/dataset/test_set/dogs', 1) train.extend(train_image) trainlabel.extend(train_label) test.extend(test_image) testlabel.extend(test_label)
train = np.array(train)
trainlabel = np.array(trainlabel)
test = np.array(test)
testlabel = np.array(testlabel)
Reshaping and shuffling the data for better accuracy
train = train.reshape(train.shape, 128,128,1).astype('float32') test = test.reshape(test.shape, 128, 128, 1).astype('float32') train = train/255.0 test = test/255.0 temp = list(zip(train,trainlabel)) random.shuffle(temp) train_x,train_l = zip(*temp) train_x = np.array(train_x) train_l = np.array(train_l)
Now we define the neural nets for this, in this model we will be using a series of Conv2D layers along with MaxPooling along with dropouts occasionaly to determine the proper features as both of them resemble each other closely.
model = Sequential() model.add(Conv2D(16, (3,3), padding = 'same', activation = 'relu', input_shape = (128,128,1))) model.add(Conv2D(16, (3,3), activation = 'relu')) model.add(MaxPooling2D(pool_size= (2,2))) model.add(Conv2D(32, (3,3), padding = 'same', activation = 'relu')) model.add(MaxPooling2D(pool_size = (2,2))) model.add(Dropout(0.2)) model.add(Conv2D(64, (3,3), padding = 'same', activation = 'relu')) model.add(Conv2D(64, (3,3), activation = 'relu')) model.add(MaxPooling2D(pool_size = (2,2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(1024, activation = 'relu')) model.add(Dense(512, activation = 'relu')) model.add(Dropout(0.5)) model.add(Dense(128, activation = 'relu')) model.add(Dense(64, activation = 'relu')) model.add(Dense(1, activation = 'sigmoid'))
COMPILING AND FITTING
We will be using Adam optimizer for the code along with the binary crossentropy for loss as there are only 2 classifications to be done.
model.compile(optimizer = 'adam', loss ='binary_crossentropy', metrics =['accuracy'] )
Fit the model on the shuffled data
history = model.fit(train_x, train_l, epochs = 30, validation_data = (test, testlabel), verbose =1)
In 30 epochs we reach an accuracy of around 98% on training and 95% on testing which is a pretty good classification.
Submitted by Mirza Yusuf (Yusuf)
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