cats_vs_dogs_challenge.py

#!/usr/bin/env python
# coding: utf-8

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# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense


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# Initialising the CNN
classifier = Sequential()


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# Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))


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


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# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))


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# Step 3 - Flattening
classifier.add(Flatten())


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# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))


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


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# Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator


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train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)


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test_datagen = ImageDataGenerator(rescale = 1./255)


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training_data_path = '....../dataset/training_set'


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training_data_path = '....../dataset/training_set'training_set = train_datagen.flow_from_directory(training_data_path,
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')


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test_data_path = '....../dataset/test_set'


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


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


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import numpy as np
from keras.preprocessing import image
 
test_image = image.load_img('....../dataset/Dog or Cat ? Predict/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
if result[0][0] == 1:
    prediction = 'dog'
else:
    prediction = 'cat'


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prediction


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from IPython.display import Image
Image(filename='....../dataset/Dog or Cat ? Predict/cat_or_dog_1.jpg') 


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