image_classifier_model

Image Classification with CIFAR-10 dataset

In this notebook, I am going to classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples.

Model Architecture

The entire model consists of 14 layers in total. In addition to layers below lists what techniques are applied to build the model.

Convolution with 64 different filters in size of (3x3)
Max Pooling by 2

ReLU activation function
Batch Normalization

Convolution with 128 different filters in size of (3x3)
Max Pooling by 2

ReLU activation function
Batch Normalization

Convolution with 256 different filters in size of (3x3)
Max Pooling by 2

ReLU activation function
Batch Normalization

Convolution with 512 different filters in size of (3x3)
Max Pooling by 2

ReLU activation function
Batch Normalization

Flattening the 3-D output of the last convolutional operations.
Fully Connected Layer with 128 units

Dropout
Batch Normalization

Fully Connected Layer with 256 units

Dropout
Batch Normalization

Fully Connected Layer with 512 units

Dropout
Batch Normalization

Fully Connected Layer with 1024 units

Dropout
Batch Normalization

Fully Connected Layer with 10 units (number of image classes)

the image below decribes how the conceptual convolving operation differs from the tensorflow implementation when you use [Channel x Width x Height] tensor format.