define_model.py

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.losses import binary_crossentropy
from keras.callbacks import EarlyStopping
from keras.optimizers import RMSprop


model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(128, 128, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

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

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss=binary_crossentropy,
              optimizer=RMSprop(lr=0.0005),  # half of the default lr
              metrics=['accuracy'])