ML-tutorial/cn.py at master · aprameyabharadwaj/ML-tutorial · GitHub

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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

classifier  = Sequential()
classifier.add(Convolution2D(32,3,3 , input_shape =(64,64,3), activation = 'relu'))

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

classifier.add(Flatten())

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'])

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_2.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:
    pred = 'dog'
else:
    pred = 'cat'