summaryrefslogtreecommitdiff
path: root/tutorials/custom-image-classifier-keras-tensorflow/index.html
diff options
context:
space:
mode:
authorNavan Chauhan <navanchauhan@gmail.com>2020-01-14 23:03:43 +0530
committerNavan Chauhan <navanchauhan@gmail.com>2020-01-14 23:03:43 +0530
commit81ae1cfaf44f9f188e35936a660e82a4e9af238e (patch)
tree81e272000149357b93b8f0e7af809375979642af /tutorials/custom-image-classifier-keras-tensorflow/index.html
parent562c929cd8b75d08d8ca368e0400c70188cd35a4 (diff)
Publish deploy 2020-01-14 23:03
Diffstat (limited to 'tutorials/custom-image-classifier-keras-tensorflow/index.html')
-rw-r--r--tutorials/custom-image-classifier-keras-tensorflow/index.html101
1 files changed, 0 insertions, 101 deletions
diff --git a/tutorials/custom-image-classifier-keras-tensorflow/index.html b/tutorials/custom-image-classifier-keras-tensorflow/index.html
deleted file mode 100644
index c3518fd..0000000
--- a/tutorials/custom-image-classifier-keras-tensorflow/index.html
+++ /dev/null
@@ -1,101 +0,0 @@
-<!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 optimizer 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 automatically 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> \ No newline at end of file
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-10 09:32:32 +0000