4 minute readCreated on December 8, 2019Last modified on January 18, 2020
Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria

Done during Google Code-In. Org: Tensorflow.
Imports

%tensorflow_version 2.x #This is for telling Colab that you want to use TF 2.0, ignore if running on local machine
+Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria | Navan ChauhanNavan ChauhanAbout Me
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4 minute readCreated on December 8, 2019Last modified on May 24, 2020Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria
Done during Google Code-In. Org: Tensorflow.
Imports
%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 numpy as np
 import os
 import cv2
-import tensorflow as tf
+import tensorflow as tf
 from tensorflow.keras import datasets, layers, models
-import pandas as pd
-import matplotlib.pyplot as plt
+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
!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.
data = []
+
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.
data = []
 labels = []
 
 Parasitized = os.listdir("./cell_images/Parasitized/")
@@ -26,7 +24,7 @@
         data.append(np.array(size_image))
         labels.append(0)
     except AttributeError:
-        print("")
+        print("")
 
 Uninfected = os.listdir("./cell_images/Uninfected/")
 for uninfect in Uninfected:
@@ -37,23 +35,17 @@
         data.append(np.array(size_image))
         labels.append(1)
     except AttributeError:
-        print("")
-
-
-
Splitting Data
df = np.array(data)
+        print("")
+
Splitting Data
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
model = models.Sequential()
+
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
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'))
@@ -66,17 +58,11 @@
 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 optimization algorithm that's been designed specifically for training deep neural networks, which means it changes its learning rate automaticaly to get the best results
model.compile(optimizer="adam",
+
Compiling Model
We use the adam optimiser as it is an adaptive learning rate optimization algorithm that's been designed specifically for training deep neural networks, which means it changes its learning rate automaticaly to get the best results
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
history = model.fit(X_train,y_train, epochs=10, validation_data=(X_test,y_test))
-
-
-
Train on 24803 samples, validate on 2755 samples
+
Training Model
We train the model for 10 epochs on the training data and then validate it using the testing data
history = model.fit(X_train,y_train, epochs=10, validation_data=(X_test,y_test))
+
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
@@ -97,25 +83,19 @@
 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
accuracy = history.history['accuracy'][-1]*100
+
Results
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(
+print(
     'Accuracy:', accuracy,
     '\nLoss:', loss,
     '\nValidation Accuracy:', val_accuracy,
     '\nValidation Loss:', val_loss
 )
-
-
-
Accuracy: 98.64532351493835 
+
Accuracy: 98.64532351493835 
 Loss: 3.732407123270176 
 Validation Accuracy: 100.0 
 Validation Loss: 0.0
-
-
-
We have achieved 98% Accuracy!
Link to Colab Notebook
Tagged with: tutorial
tensorflow
colab
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+
We have achieved 98% Accuracy!
Link to Colab Notebook
Tagged with: