David Molina virtualdvid

## table_4.csv

          
            
             Keras: Flow from directory with Augmentation
             Gap: Flow from directory with Augmentation

            
              PRE-TIME
               0.20845842361450195

            
              TIME
               143.19440412521362
               10.20444941520691

            
              Memory Used:
               0.13 GB
               0.01 GB

## code_11.py
print("Gap: Flow from directory with Augmentation")

dataset = Images(augment=['rotate=-30,30'], config=['stream'])
dataset.load('flowers')
dataset.minibatch = 32
train_generator = dataset.minibatch
start_mem = psutil.virtual_memory().used

start_time = time.time()

## code_10.py
print("Keras: Flow from directory with Augmentation")
start_mem = psutil.virtual_memory().used

start_time = time.time()

datagen = ImageDataGenerator(rotation_range=30, rescale=1./255)
train_generator = datagen.flow_from_directory('flowers', target_size=(128,128), batch_size=32)

print("PRE-TIME", time.time() - start_time)

## table_3.csv

          
            
             Keras: Flow with Augmentation
             Gap: Flow with Augmentation

            
              PRE-TIME
               0.4257802963256836
               0.0036149024963378906

            
              TIME
               54.004902839660645
               6.712508916854858

            
              Memory Used:
               -0.00 GB
               0.00 GB

## code_9.py
print("Gap: Flow with Augmentation")

dataset = Images(augment=['rotate=-30,30'])
dataset.load('flowers')
start_mem = psutil.virtual_memory().used

start_time = time.time()

dataset.minibatch = 32
train_generator = dataset.minibatch

## code_8.py
print("Keras: Flow with Augmentation")

datagen = ImageDataGenerator(rotation_range=30)

start_time = time.time()

datagen.fit(X_train)
train_generator = datagen.flow(X_train, Y_train, batch_size=32)

print("PRE-TIME (fit)", time.time() - start_time)

## table_2.csv

          
            
             Keras: flow_from_directory
             Gap: flow from directory

            
              PRE-TIME
               0.20801329612731934
               0.01245427131652832

            
              TIME
               89.3897156715393
               6.902476787567139

            
              Memory Used:
               0.00 GB
               -0.00 GB

## code_7.py
print("Gap: flow from directory")

start_mem = psutil.virtual_memory().used
start_time = time.time()

dataset = Images(config=['stream'])
dataset.load('flowers')
dataset.minibatch = 32
train_generator = dataset.minibatch

## code_6.py
print("Keras: flow_from_directory")

start_mem = psutil.virtual_memory().used
start_time = time.time()

datagen = ImageDataGenerator(rescale=1./255)
train_generator = datagen.flow_from_directory('flowers', target_size=(128,128), batch_size=32)
print("PRE-TIME", time.time() - start_time)

start_time = time.time()

## table_1.csv

          
            
             Keras: Flow
             Gap: Flow

            
              PRE-TIME
               0.38779211044311523
               1.8835067749023438e-05

            
              TIME
               1.3482673168182373
               0.7792487144470215

            
              Memory Used:
               1.79 GB
               -0.00 GB
	Keras: Flow from directory with Augmentation	Gap: Flow from directory with Augmentation
PRE-TIME	0.20845842361450195
TIME	143.19440412521362	10.20444941520691
Memory Used:	0.13 GB	0.01 GB
	print("Gap: Flow from directory with Augmentation")

	dataset = Images(augment=['rotate=-30,30'], config=['stream'])
	dataset.load('flowers')
	dataset.minibatch = 32
	train_generator = dataset.minibatch
	start_mem = psutil.virtual_memory().used

	start_time = time.time()
	print("Keras: Flow from directory with Augmentation")
	start_mem = psutil.virtual_memory().used

	start_time = time.time()

	datagen = ImageDataGenerator(rotation_range=30, rescale=1./255)
	train_generator = datagen.flow_from_directory('flowers', target_size=(128,128), batch_size=32)

	print("PRE-TIME", time.time() - start_time)
	Keras: Flow with Augmentation	Gap: Flow with Augmentation
PRE-TIME	0.4257802963256836	0.0036149024963378906
TIME	54.004902839660645	6.712508916854858
Memory Used:	-0.00 GB	0.00 GB
	print("Gap: Flow with Augmentation")

	dataset = Images(augment=['rotate=-30,30'])
	dataset.load('flowers')
	start_mem = psutil.virtual_memory().used

	start_time = time.time()

	dataset.minibatch = 32
	train_generator = dataset.minibatch
	print("Keras: Flow with Augmentation")

	datagen = ImageDataGenerator(rotation_range=30)

	start_time = time.time()

	datagen.fit(X_train)
	train_generator = datagen.flow(X_train, Y_train, batch_size=32)

	print("PRE-TIME (fit)", time.time() - start_time)
	Keras: flow_from_directory	Gap: flow from directory
PRE-TIME	0.20801329612731934	0.01245427131652832
TIME	89.3897156715393	6.902476787567139
Memory Used:	0.00 GB	-0.00 GB
	print("Gap: flow from directory")

	start_mem = psutil.virtual_memory().used
	start_time = time.time()

	dataset = Images(config=['stream'])
	dataset.load('flowers')
	dataset.minibatch = 32
	train_generator = dataset.minibatch
	print("Keras: flow_from_directory")

	start_mem = psutil.virtual_memory().used
	start_time = time.time()

	datagen = ImageDataGenerator(rescale=1./255)
	train_generator = datagen.flow_from_directory('flowers', target_size=(128,128), batch_size=32)
	print("PRE-TIME", time.time() - start_time)

	start_time = time.time()
	Keras: Flow	Gap: Flow
PRE-TIME	0.38779211044311523	1.8835067749023438e-05
TIME	1.3482673168182373	0.7792487144470215
Memory Used:	1.79 GB	-0.00 GB