brain.py

from keras.preprocessing import image
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
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'])


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(
    'training_data',
    target_size=(64, 64),
    batch_size=32,
    class_mode='binary'
)

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

print("training")
classifier.fit_generator(
    training_set,
    steps_per_epoch=1000,
    epochs=10,
    validation_data=test_set,
    validation_steps=100
)

print("test")

test_image = image.load_img(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] >= 0.5:
    print("closed")
else:
    print("open")