prototype.py

import tensorflow as tf
from tensorflow import keras
from keras.layers import *
from keras.models import *
from keras.preprocessing import image

model = Sequential()

model.add(Conv2D(32, kernel_size=3, activation='relu', input_shape=(224,224,3)))
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))

model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))

model.add(Conv2D(128, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss=keras.losses.binary_crossentropy, optimizer='adam', metrics=['accuracy'])

model.summary()

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

test_datagen = image.ImageDataGenerator(rescale = 1./255)

train_generator = train_datagen.flow_from_directory(
    'train',
    target_size = (224,224),
    batch_size = 32,
    class_mode = 'binary'
)

validation_generator = test_datagen.flow_from_directory(
    'test',
    target_size = (224,224),
    batch_size = 32,
    class_mode = 'binary'
)

hist = model.fit_generator(
    train_generator,
    steps_per_epoch = 8,
    epochs = 100,
    validation_data = validation_generator,
    validation_steps = 2,
)