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diff --git a/Content/posts/2019-12-08-Image-Classifier-Tensorflow.md b/Content/posts/2019-12-08-Image-Classifier-Tensorflow.md new file mode 100644 index 0000000..d120c9b --- /dev/null +++ b/Content/posts/2019-12-08-Image-Classifier-Tensorflow.md @@ -0,0 +1,176 @@ +--- +date: 2019-12-08 14:16 +description: Tutorial on creating an image classifier model using TensorFlow which detects malaria +tags: Tutorial, Tensorflow, Colab +--- + +# Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria + +**Done during Google Code-In. Org: Tensorflow.** + +## Imports + +```python +%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 + +``` + +## Dataset + +### Fetching the Data + +```python +!wget ftp://lhcftp.nlm.nih.gov/Open-Access-Datasets/Malaria/cell_images.zip +!unzip cell_images.zip +``` + +### Processing the Data + +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. + +```python +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("") +``` + +### Splitting Data + +```python +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))] +``` + +``` +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 +``` + +## Model + +### Creating Model + +By creating a sequential model, we create a linear stack of layers. + +*Note: The input shape for the first layer is 50,50 which corresponds with the sizes of the resized images* + +```python +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() +``` + +### Compiling Model + +We use the Adam optimiser as it is an adaptive learning rate optimisation algorithm that's been designed specifically for *training* deep neural networks, which means it changes its learning rate automatically to get the best results + +```python +model.compile(optimizer="adam", + loss="sparse_categorical_crossentropy", + metrics=["accuracy"]) +``` + +### Training Model + +We train the model for 10 epochs on the training data and then validate it using the testing data + +```python +history = model.fit(X_train,y_train, epochs=10, validation_data=(X_test,y_test)) +``` + +```python +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 +``` + +### Results + +```python +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 +) +``` +```python +Accuracy: 98.64532351493835 +Loss: 3.732407123270176 +Validation Accuracy: 100.0 +Validation Loss: 0.0 +``` + +We have achieved 98% Accuracy! + +[Link to Colab Notebook](https://colab.research.google.com/drive/1ZswDsxLwYZEnev89MzlL5Lwt6ut7iwp- "Colab Notebook") |