CNN Feature Visualization with Activation Maximization in Keras (VGG16)

### Ensure the visualization toolkit and TensorFlow are available and up to date.
# pip install --upgrade tf-keras-vis tensorflow

### Import NumPy for array handling.
import numpy as np

### Import TensorFlow for deep learning operations.
import tensorflow as tf

### Import the pre-trained VGG16 model and alias for readability.
from tensorflow.keras.applications.vgg16 import VGG16 as Vgg16Model

### Load VGG16 with ImageNet weights and keep the classification head for class scoring.
model = Vgg16Model(weights='imagenet', include_top=True)

### Print the model summary to confirm architecture and parameter counts.
print (model.summary())

### Prepare a function that modifies the model before visualization.
### We change the final softmax to linear to expose raw logits for maximization.
# define a function to modify the model
# we will change the softmax to a linear function
def model_modifier(modl):
    ### Set the last layer activation to linear to make optimization well behaved.
    modl.layers[-1].activation = tf.keras.activations.linear  # All the layers except the last one will have activation=linear

### Ensure the visualization toolkit and TensorFlow are available and up to date. # pip install --upgrade tf-keras-vis tensorflow  ### Import NumPy for array handling. import numpy as np  ### Import TensorFlow for deep learning operations. import tensorflow as tf  ### Import the pre-trained VGG16 model and alias for readability. from tensorflow.keras.applications.vgg16 import VGG16 as Vgg16Model  ### Load VGG16 with ImageNet weights and keep the classification head for class scoring. model = Vgg16Model(weights='imagenet', include_top=True)  ### Print the model summary to confirm architecture and parameter counts. print (model.summary())  ### Prepare a function that modifies the model before visualization. ### We change the final softmax to linear to expose raw logits for maximization. # define a function to modify the model # we will change the softmax to a linear function def model_modifier(modl):     ### Set the last layer activation to linear to make optimization well behaved.     modl.layers[-1].activation = tf.keras.activations.linear  # All the layers except the last one will have activation=linear 

### Import the activation maximization utility that performs gradient-based optimization on the input.
# create an object instance of Acticvation maximization class
from tf_keras_vis.activation_maximization import ActivationMaximization

### Instantiate the visualizer with our model and the modifier.
### clone=True duplicates the model so the original instance remains unchanged.
activation_maximization = ActivationMaximization(model,
                                                model_modifier,
                                                clone=True)  # clone means , duplicate the model and not update the current one

### Define a loss that returns the logit for the target ImageNet class.
### We will maximize the score for the Persian cat class.
# Now , We will define a loss function that maximize a specific class.
# leats maximize the class of "Persian cat" , class no. 283
# https://deeplearning.cms.waikato.ac.nz/user-guide/class-maps/IMAGENET/
def loss(output):
    ### Return the score for class index 283 which corresponds to Persian cat.
    return output[:, 283]

### Import a callback to print progress at intervals.
from tf_keras_vis.utils.callbacks import Print

### Run activation maximization to synthesize an image that maximizes the target class score.
### The callback prints status every 50 iterations for transparency.
# visual the class
activation = activation_maximization(loss,
                                    callbacks=[Print(interval=50)]    )

### Retrieve the synthesized image from the result list and convert to 8-bit for rendering.
#lets grab the image after running the process
image = activation[0].astype(np.uint8)

### Import the activation maximization utility that performs gradient-based optimization on the input. # create an object instance of Acticvation maximization class from tf_keras_vis.activation_maximization import ActivationMaximization  ### Instantiate the visualizer with our model and the modifier. ### clone=True duplicates the model so the original instance remains unchanged. activation_maximization = ActivationMaximization(model,                                                 model_modifier,                                                 clone=True)  # clone means , duplicate the model and not update the current one  ### Define a loss that returns the logit for the target ImageNet class. ### We will maximize the score for the Persian cat class. # Now , We will define a loss function that maximize a specific class. # leats maximize the class of "Persian cat" , class no. 283 # https://deeplearning.cms.waikato.ac.nz/user-guide/class-maps/IMAGENET/ def loss(output):     ### Return the score for class index 283 which corresponds to Persian cat.     return output[:, 283]  ### Import a callback to print progress at intervals. from tf_keras_vis.utils.callbacks import Print  ### Run activation maximization to synthesize an image that maximizes the target class score. ### The callback prints status every 50 iterations for transparency. # visual the class activation = activation_maximization(loss,                                     callbacks=[Print(interval=50)]    )  ### Retrieve the synthesized image from the result list and convert to 8-bit for rendering. #lets grab the image after running the process image = activation[0].astype(np.uint8) 

### Import OpenCV for color conversion, resizing, and display.
# show the image using OpenCv
import cv2

### Convert the image from RGB to BGR because OpenCV expects BGR by default.
# change the image from RGB to BGR
imageCV = cv2.cvtColor(image , cv2.COLOR_RGB2BGR)

### Define the upscaling percentage to view more detail comfortably.
# enlarge the image
scale_percent = 200

### Compute the new width using the chosen scale.
w = int(imageCV.shape[1]* scale_percent / 100)

### Compute the new height using the chosen scale.
h = int(imageCV.shape[0]* scale_percent / 100)

### Package the target dimensions for the resize function.
dim = (w, h)

### Resize the image using an area-based interpolator for smooth scaling.
resized = cv2.resize(imageCV, dim , interpolation=cv2.INTER_AREA)

### Display the synthesized class visualization in a window titled "Persian Cat".
cv2.imshow("Persian Cat",resized )

### Keep the window open until a key is pressed.
cv2.waitKey(0)

### Import OpenCV for color conversion, resizing, and display. # show the image using OpenCv import cv2  ### Convert the image from RGB to BGR because OpenCV expects BGR by default. # change the image from RGB to BGR imageCV = cv2.cvtColor(image , cv2.COLOR_RGB2BGR)  ### Define the upscaling percentage to view more detail comfortably. # enlarge the image scale_percent = 200  ### Compute the new width using the chosen scale. w = int(imageCV.shape[1]* scale_percent / 100)  ### Compute the new height using the chosen scale. h = int(imageCV.shape[0]* scale_percent / 100)  ### Package the target dimensions for the resize function. dim = (w, h)  ### Resize the image using an area-based interpolator for smooth scaling. resized = cv2.resize(imageCV, dim , interpolation=cv2.INTER_AREA)  ### Display the synthesized class visualization in a window titled "Persian Cat". cv2.imshow("Persian Cat",resized )  ### Keep the window open until a key is pressed. cv2.waitKey(0)