Google Earth Images Classification using CNN in Keras.py

# -*- coding: utf-8 -*-
"""
Created on Sat Jan 19 17:53:04 2019
@author: Zaheer Abbas
"""
#     Part 1 :-   Building CNN

# importing Keras libraries and packages

from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# initializing CNN
classifier = Sequential()

# step - 1 Connvolution
classifier.add(Convolution2D(32, 3, 3, input_shape = (64,64,3), activation = 'relu'))

# step - 2 Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding second Connvolution layer and pooling layer
classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# step - 3 Falttening
classifier.add(Flatten())

# step - 4 full connection
classifier.add(Dense(output_dim = 128, activation = 'relu'))
classifier.add(Dense(output_dim = 1, activation = 'sigmoid'))

# compiling
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# fitting CNN to the images
from 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('images_data/training_set',
                                                 target_size=(64, 64),
                                                 batch_size=32,
                                                 class_mode='binary')

test_set = test_datagen.flow_from_directory('images_data/test_set',
                                            target_size=(64, 64),
                                            batch_size=32,
                                            class_mode='binary')

classifier.fit_generator(training_set,
                         steps_per_epoch=700,
                         epochs=15,
                         validation_data=test_set,
                         validation_steps=500)