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<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/SwiftWebsite/tutorials/custom-image-classifier-keras-tensorflow"/><meta name="twitter:url" content="https://navanchauhan.github.io/SwiftWebsite/tutorials/custom-image-classifier-keras-tensorflow"/><meta name="og:url" content="https://navanchauhan.github.io/SwiftWebsite/tutorials/custom-image-classifier-keras-tensorflow"/><title>Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan</title><meta name="twitter:title" content="Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan"/><meta name="og:title" content="Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan Chauhan"/><meta name="description" content="Short tutorial for creating a custom image classifier using TF 2.0"/><meta name="twitter:description" content="Short tutorial for creating a custom image classifier using TF 2.0"/><meta name="og:description" content="Short tutorial for creating a custom image classifier using TF 2.0"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/SwiftWebsite/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/SwiftWebsite/images/logo.png"/></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/posts">Posts</a></li><li><a class="selected" href="/tutorials">Tutorials</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><h1>Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</h1><p><strong>Done during Google Code-In. Org: Tensorflow.</strong></p><h2>Imports</h2><pre><code>%tensorflow_version 2.x #This is for telling Colab that you want to use TF 2.0, ignore if running on local machine
from PIL import Image # We use the PIL Library to resize images
import numpy as np
import os
import cv2
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout
</code></pre><h2>Dataset</h2><h3>Fetching the Data</h3><pre><code>!wget ftp://lhcftp.nlm.nih.gov/Open-Access-Datasets/Malaria/cell_images.zip
!unzip cell_images.zip
</code></pre><h3>Processing the Data</h3><p>We resize all the images as 50x50 and add the numpy array of that image as well as their label names (Infected or Not) to common arrays.</p><pre><code>data = []
labels = []
Parasitized = os.listdir("./cell_images/Parasitized/")
for parasite in Parasitized:
try:
image=cv2.imread("./cell_images/Parasitized/"+parasite)
image_from_array = Image.fromarray(image, 'RGB')
size_image = image_from_array.resize((50, 50))
data.append(np.array(size_image))
labels.append(0)
except AttributeError:
print("")
Uninfected = os.listdir("./cell_images/Uninfected/")
for uninfect in Uninfected:
try:
image=cv2.imread("./cell_images/Uninfected/"+uninfect)
image_from_array = Image.fromarray(image, 'RGB')
size_image = image_from_array.resize((50, 50))
data.append(np.array(size_image))
labels.append(1)
except AttributeError:
print("")
</code></pre><h3>Splitting Data</h3><pre><code>df = np.array(data)
labels = np.array(labels)
(X_train, X_test) = df[(int)(0.1*len(df)):],df[:(int)(0.1*len(df))]
(y_train, y_test) = labels[(int)(0.1*len(labels)):],labels[:(int)(0.1*len(labels))]
</code></pre><pre><code>s=np.arange(X_train.shape[0])
np.random.shuffle(s)
X_train=X_train[s]
y_train=y_train[s]
X_train = X_train/255.0
</code></pre><h2>Model</h2><h3>Creating Model</h3><p>By creating a sequential model, we create a linear stack of layers.</p><p><em>Note: The input shape for the first layer is 50,50 which corresponds with the sizes of the resized images</em></p><pre><code>model = models.Sequential()
model.add(layers.Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(50,50,3)))
model.add(layers.MaxPooling2D(pool_size=2))
model.add(layers.Conv2D(filters=32,kernel_size=2,padding='same',activation='relu'))
model.add(layers.MaxPooling2D(pool_size=2))
model.add(layers.Conv2D(filters=64,kernel_size=2,padding="same",activation="relu"))
model.add(layers.MaxPooling2D(pool_size=2))
model.add(layers.Dropout(0.2))
model.add(layers.Flatten())
model.add(layers.Dense(500,activation="relu"))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(2,activation="softmax"))#2 represent output layer neurons
model.summary()
</code></pre><h3>Compiling Model</h3><p>We use the adam optimiser as it is an adaptive learning rate optimization algorithm that's been designed specifically for <em>training</em> deep neural networks, which means it changes its learning rate automaticaly to get the best results</p><pre><code>model.compile(optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
</code></pre><h3>Training Model</h3><p>We train the model for 10 epochs on the training data and then validate it using the testing data</p><pre><code>history = model.fit(X_train,y_train, epochs=10, validation_data=(X_test,y_test))
</code></pre><pre><code>Train on 24803 samples, validate on 2755 samples
Epoch 1/10
24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0786 - accuracy: 0.9729 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 2/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0746 - accuracy: 0.9731 - val_loss: 0.0290 - val_accuracy: 0.9996
Epoch 3/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0672 - accuracy: 0.9764 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 4/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0601 - accuracy: 0.9789 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 5/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0558 - accuracy: 0.9804 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 6/10
24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0513 - accuracy: 0.9819 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 7/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0452 - accuracy: 0.9849 - val_loss: 0.3190 - val_accuracy: 0.9985
Epoch 8/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0404 - accuracy: 0.9858 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 9/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0352 - accuracy: 0.9878 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
Epoch 10/10
24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0373 - accuracy: 0.9865 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
</code></pre><h3>Results</h3><pre><code>accuracy = history.history['accuracy'][-1]*100
loss = history.history['loss'][-1]*100
val_accuracy = history.history['val_accuracy'][-1]*100
val_loss = history.history['val_loss'][-1]*100
print(
'Accuracy:', accuracy,
'\nLoss:', loss,
'\nValidation Accuracy:', val_accuracy,
'\nValidation Loss:', val_loss
)
</code></pre><pre><code>Accuracy: 98.64532351493835
Loss: 3.732407123270176
Validation Accuracy: 100.0
Validation Loss: 0.0
</code></pre><p>We have achieved 98% Accuracy!</p><p><a href="https://colab.research.google.com/drive/1ZswDsxLwYZEnev89MzlL5Lwt6ut7iwp- "Colab Notebook"">Link to Colab Notebook</a></p></div><span>Tagged with: </span><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li></ul></article></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
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