Netception
Description
Netception is a neural network inception library developed in Python 3 and used with Keras.
How to Install
pip install netception
Dependencies
Here are Netception's required dependencies.
numpykeras
Here are some optional dependencies one might need.
-
h5py(to load and save Keras models stored in files) -
pillow(to manipulate images)
Also note that Keras requires a backend library like TensorFlow to operate.
pip install tensorflow
If GPU support is desired, it is also possible to use the GPU version of TensorFlow.
pip install tensorflow-gpu
An Exhaustively Commented Example to Get You Started
import os
from PIL import Image
from keras import applications, backend
from netception.inceptor import Inceptor
from netception.utils.visualization_util import VisualizationUtil
if __name__ == "__main__":
# Load the model to incept
# (Here, we load the pretrained VGG16 model from Keras)
model = applications.VGG16()
# Print the model's summary to see its layers
model.summary()
# Determine the target to incept within the model
# (Here, we choose to incept the output of the 455th filter of the
# convolutional layer "block5_conv3")
target = model.get_layer("block5_conv3").output[:, :, :, 455]
# Create an inceptor and configure it
# (Here, we create an inceptor with our model and target. We also set an
# inception rate of 0.25, a maximal number of steps of 50, and parameters
# for early stopping if the inception score stops improving enough)
inceptor = Inceptor(
model=model,
target=target,
inception_rate=0.5,
max_steps=200,
improvement_check_interval=5,
improvement_threshold=0.05
)
# Run the inceptor
inception, score = inceptor.incept()
# Convert the resulting inception into image data
image_data = VisualizationUtil.inception_to_bytes(
inception=inception,
colorfulness=0.15
)
# Create an image from the image data, and resize the image
image = Image.fromarray(image_data).resize((512, 512), Image.BICUBIC)
# Show the image
image.show()
# Save the image
script_dir = os.path.dirname(os.path.realpath(__file__))
image.save(os.path.join(script_dir, "inception.png"))
# Clear the backend session
backend.clear_session()
This is what the result looks like.
The Same Example In Compact Form For a Quick Copy & Paste
import os
from PIL import Image
from keras import applications, backend
from netception.inceptor import Inceptor
from netception.utils.visualization_util import VisualizationUtil
if __name__ == "__main__":
model = applications.VGG16()
model.summary()
target = model.get_layer("block5_conv3").output[:, :, :, 455]
inceptor = Inceptor(model, target, 0.5, 200, 5, 0.05)
inception, score = inceptor.incept()
image_data = VisualizationUtil.inception_to_bytes(inception, 0.15)
image = Image.fromarray(image_data).resize((512, 512), Image.BICUBIC)
image.show()
script_dir = os.path.dirname(os.path.realpath(__file__))
image.save(os.path.join(script_dir, "inception.png"))
backend.clear_session()
