rom_cnn.py

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

#initializing the CNN

classifier = Sequential()

#1 Convolution 
classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation= 'relu'))
#2 pooling 
classifier.add(MaxPooling2D(pool_size = (2,2)))
#3 flatten 
classifier.add(Flatten())

# Full Connection
classifier.add(Dense(output_dim = 128, activation = 'relu'))
classifier.add(Dense(output_dim = 1, activation = 'sigmoid'))

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
#fitting the CNN to the image

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('dataset/training_set',
                                                  target_size=(64, 64),
                                                  batch_size=32,
                                                  class_mode='binary')
        

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

classifier.fit_generator(training_set,
                         steps_per_epoch=8000,
                         epochs=25, 
                         validation_data=test_set,
                         validation_steps=2000)

import numpy as np 
from keras.preprocessing import image

test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size=(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices