Keras example with helper functions

comparison.py

# Original
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))


# With helpers
myLayers = [
	convPool(32, input_shape=(150,150,3)),
	convPool(64),
	convPool(128),
	convPool(128),
	layers.Flatten(),
	layers.Dropout(0.5),
	layers.Dense(512, activation='relu'),
	layers.Dense(1, activation='sigmoid')
]

original.py

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

with-helpers.py

maxPool = layers.MaxPooling2D((2,2))

def conv(nFilters, **kwargs): 
	return layers.Conv2D(nFilters, (3,3), activation = 'relu', **kwargs)

def convPool(nFilters, **kwargs):
	return [conv(nFilters, **kwargs), maxPool]

def squash(xs):
	"""Flatten an irrelegular list of lists (some items aren't lists)"""
	newList = []
    for x in xs:
        if isinstance(x, list):
            for sub in squash(x):
                newList.append(sub)
        else:
            newList.append(x)
    return squash

def createModel(modelLayers): 
	model = models.Sequential()
	for layer in squash(modelLayers):
		model.add(layer)
	return model	

myLayers = [
	convPool(32, input_shape=(150,150,3)),
	convPool(64),
	convPool(128),
	convPool(128),
	layers.Flatten(),
	layers.Dropout(0.5),
	layers.Dense(512, activation='relu'),
	layers.Dense(1, activation='sigmoid')
]

model = createModel(mylayers)