Multiclass Classification with CNN-2D

Multiclass Classification with CNN-2D

Multiclass Classification with CNN-2D. I know there are many blogs about CNN and multi-class classification, but maybe this blog wouldn't be that similar to the other blogs.

In Multiclass classification, the instances can be classified into one of three or more classes. Here, the Dataset contains image data of Natural Scenes around the world that are distributed into 6 different categories. {‘buildings’- 0, ‘forest’- 1, ‘glacier’- 2, ‘mountain’- 3, ‘sea’ - 4, ‘street’ - 5 }

There are 14034 images in Train data and 3000 images in Test data.

Data Source:

Import Libraries

import tensorflow
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.utils import to_categorical
print('Tensorflow Version: ',tensorflow.__version__)

Image for post

CNN-2D Model

## ImageDataGenerator generate batches of tensor image data with real-time data augmentation.
from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set= train_datagen.flow_from_directory('dataset/seg_train',
                                  target_size = (64, 64),
                                  batch_size = 32,
test_set = test_datagen.flow_from_directory('dataset/seg_test',
                                            target_size = (64, 64),
                                            batch_size = 32,

multiclass-classification tensorflow convolution-neural-net

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