ImageClassification

Image Classification using ANN and CNN.

Using Tensorflow 2, keras, numpy, matplotlib and sklearn python lib.

%tensorflow_version 2.x

import tensorflow as tf
from tensorflow import keras
from keras import datasets, layers, models
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix , classification_report

Loading Cifar10 dataset using load_data() from keras.datasets

(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()

Defining classes and fine tuning training parameters

classes = ["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"]
y_train = y_train.reshape(-1,)
x_train = x_train/255
x_test = x_test/255

Training model(ANN)

ann = keras.Sequential([
              keras.layers.Flatten(input_shape = (32,32,3)),
              keras.layers.Dense(3000, activation = 'relu'),
              keras.layers.Dense(1000, activation = 'relu'),
              keras.layers.Dense(10, activation = 'sigmoid')
])

ann.compile(optimizer='SGD',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

ann.fit(x_train, y_train, epochs = 2)

Checking stats of the network

y_pred = ann.predict(x_test)
y_pred_classes = [np.argmax(element) for element in y_pred]

print("Classification Report: \n", classification_report(y_test, y_pred_classes))

Training model(CNN)

cnn = models.Sequential([
              #cnn
              layers.Conv2D(filters = 32, activation='relu', kernel_size=(3,3), input_shape = (32,32,3)),
              layers.MaxPooling2D((2,2)),

              layers.Conv2D(filters = 32, activation='relu', kernel_size=(3,3), input_shape = (32,32,3)),
              layers.MaxPooling2D((2,2)),

              #dense
              layers.Flatten(),
              layers.Dense(64, activation = 'relu'),
              layers.Dense(10, activation = 'softmax')
])

cnn.compile(optimizer='adam',
            loss='sparse_categorical_crossentropy',
            metrics=['accuracy'])
cnn.fit(x_train, y_train, epochs = 2)