Image_Classifier.py

# import statements
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
import numpy as np

# loading training data
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
        '/kaggle/input/intel-image-classification/seg_train/seg_train',
        target_size=(64, 64),
        batch_size=32,
        class_mode='categorical')

# loading testing data
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = train_datagen.flow_from_directory(
        '/kaggle/input/intel-image-classification/seg_test/seg_test',
        target_size=(64, 64),
        batch_size=32,
        class_mode='categorical')

# initialising sequential model and adding layers to it
cnn = tf.keras.models.Sequential()
cnn.add(tf.keras.layers.Conv2D(filters=48, kernel_size=3, activation='relu', input_shape=[64, 64, 3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Conv2D(filters=48, kernel_size=3, activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Flatten())
cnn.add(tf.keras.layers.Dense(128, activation='relu'))
cnn.add(tf.keras.layers.Dense(64, activation='relu'))
cnn.add(tf.keras.layers.Dense(6, activation='softmax'))

# finally compile and train the cnn
cnn.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
cnn.fit(x=train_generator, validation_data=test_generator, epochs=30)