Python program to build deep learning algorithm using a CNNs model to.docx
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Python program to build deep learning algorithm using a CNNs model to detect malware attacks. The code is working, but I faced issues with the last part of the code, and I needed to know precisely where the problem was. I have the dataset of malware and benign in exe, I convert them to grayscale images, and it goes through the filters, and it shows me the output. this is the code import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D import numpy as np from os import listdir from PIL import Image import os.path import numpy as np path = '/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE' h = 256 #height of image w = 256 #width of image # be careful with using this function, it will consume memory, access to disk and time images = [] for f in listdir('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE'): with open(os.path.join('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE', f), 'rb') as img_set: img_arr = img_set.read(h*w) while img_arr: if len(img_arr) == h*w and img_arr not in images: images.append(img_arr) img_arr = img_set.read(h*w) #And you can save them into png files # count = 0 # for img in images: # png = Image.fromarray(np.reshape(list(img), (h,w)).astype('float32'), mode='L') # png.save('image_l%d.png'%count) # count += 1 #reshape images to fit into the CNN model img_list = np.zeros(shape = (len(images), h,w,1), dtype = np.uint8) for j in range(len(images)): img_list[j,:,:,0] = np.reshape(list(images[j]), (h,w)) img_list = img_list.astype('float32') img_list /= 255 # ################################################################ #approca 1 failed go back from scrtuch # implement the architecture of the CNN model = Sequential() #Conv2D Layers model.add(Conv2D(12, (25, 25), padding='same',input_shape=img_list.shape[1:], activation = 'relu')) model.add(Conv2D(12, (25, 25), activation = 'relu')) #Max Pooling Layer model.add(MaxPooling2D(pool_size=(2, 2))) #Conv2D Layer model.add(Conv2D(12, (13, 13), padding='same', activation = 'relu')) model.add(Conv2D(12, (13, 13), activation = 'relu')) #Max Pooling model.add(MaxPooling2D(pool_size=(2, 2))) #Flattening Layer model.add(Flatten()) #Dense Layer model.add(Dense(1024, activation = 'relu')) model.add(Dense(1, activation = 'sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy']) model.summary() ''' FINALLY If you have a set of executable files from both types (benign and malicious) you can try to fit the model. You should prepare a list of images for malwares and a list for benign and here how you fit the model. ''' #Change the parameters to whatever suits you I already have to files that contain benign files and malicious images, but it shows me syntax error! batch_size = 512 epochs = 100 labels = [0 for _ in benign_images ] + [1 for _ in malicious_images ] model.fit( benign_images + maliciou.
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![Python program to build deep learning algorithm using a CNNs model to detect malware attacks.
The code is working, but I faced issues with the last part of the code, and I needed to know
precisely where the problem was.
I have the dataset of malware and benign in exe, I convert them to grayscale images, and it goes
through the filters, and it shows me the output.
this is the code
import keras from keras.models import Sequential from keras.layers import Dense, Dropout,
Activation, Flatten from keras.layers import Conv2D, MaxPooling2D import numpy as np from
os import listdir from PIL import Image import os.path import numpy as np path =
'/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE' h = 256
#height of image w = 256 #width of image # be careful with using this function, it will consume
memory, access to disk and time images = [] for f in listdir('/media/malkuwari/New
Volume/Ransomware_Detection_using _CNN/mixEXE'): with
open(os.path.join('/media/malkuwari/New Volume/Ransomware_Detection_using
_CNN/mixEXE', f), 'rb') as img_set: img_arr = img_set.read(h*w) while img_arr: if len(img_arr)
== h*w and img_arr not in images: images.append(img_arr) img_arr = img_set.read(h*w) #And
you can save them into png files # count = 0 # for img in images: # png =
Image.fromarray(np.reshape(list(img), (h,w)).astype('float32'), mode='L') #
png.save('image_l%d.png'%count) # count += 1 #reshape images to fit into the CNN model
img_list = np.zeros(shape = (len(images), h,w,1), dtype = np.uint8) for j in range(len(images)):
img_list[j,:,:,0] = np.reshape(list(images[j]), (h,w)) img_list = img_list.astype('float32') img_list
/= 255 # ################################################################
#approca 1 failed go back from scrtuch # implement the architecture of the CNN model =
Sequential() #Conv2D Layers model.add(Conv2D(12, (25, 25),
padding='same',input_shape=img_list.shape[1:], activation = 'relu')) model.add(Conv2D(12, (25,
25), activation = 'relu')) #Max Pooling Layer model.add(MaxPooling2D(pool_size=(2, 2)))
#Conv2D Layer model.add(Conv2D(12, (13, 13), padding='same', activation = 'relu'))
model.add(Conv2D(12, (13, 13), activation = 'relu')) #Max Pooling
model.add(MaxPooling2D(pool_size=(2, 2))) #Flattening Layer model.add(Flatten()) #Dense
Layer model.add(Dense(1024, activation = 'relu')) model.add(Dense(1, activation = 'sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy'])
model.summary()
''' FINALLY If you have a set of executable files from both types (benign and malicious) you can
try to fit the model. You should prepare a list of images for malwares and a list for benign and
here how you fit the model. ''' #Change the parameters to whatever suits you
I already have to files that contain benign files and malicious images, but it shows me
syntax error!](https://image.slidesharecdn.com/pythonprogramtobuilddeeplearningalgorithmusingacnnsmodelto-230222052326-0ac14ed3/85/Python-program-to-build-deep-learning-algorithm-using-a-CNNs-model-to-docx-1-320.jpg)
![batch_size = 512 epochs = 100 labels = [0 for _ in benign_images ] + [1 for _ in
malicious_images ] model.fit( benign_images + malicious_images , labels, batch_size =
batch_size, epochs = epochs, validation_split = 0.25,
please, if you can help me with this issue and if there is additional coding needed to my project,
thank you](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Using the code below- I need help with creating code for the following.pdf
Using the code below, I need help with creating code for the following:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display th.
Using the code below- I need help with the following 3 things- 1) Writ.pdf
Using the code below, I need help with the following 3 things:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
3) Comment on the network performance.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a .
Can someone please explain what the code below is doing and comment on.pdf
Can someone please explain what the code below is doing and comment on its network
performance? Please and thank you.
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * generated_images + 0.5
fig, axs = plt.subplots(r, c)
count = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(ge.
Need helping adding to the code below to plot the images from the firs.pdf
Need helping adding to the code below to plot the images from the first epoch. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
29-kashyap-mask-detaction.pptx
i want to create mask detection using ai and i want explain all workings in tutorial of youtube and also i will make ppt and blog for refrences
Need an detailed analysis of what this code-model is doing- Thanks #St.pdf
Need an detailed analysis of what this code/model is doing. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
# Generate a batch of random noise
noise = np.random.normal(0, 1, (16, latent_dimensions))
# Generate images from the noise
generated_images = generator.predict(noise)
# Rescale the images to 0.
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Using the code below- I need help with creating code for the following.pdf
Using the code below, I need help with creating code for the following:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display th.
Using the code below- I need help with the following 3 things- 1) Writ.pdf
Using the code below, I need help with the following 3 things:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
3) Comment on the network performance.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a .
Can someone please explain what the code below is doing and comment on.pdf
Can someone please explain what the code below is doing and comment on its network
performance? Please and thank you.
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * generated_images + 0.5
fig, axs = plt.subplots(r, c)
count = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(ge.
Need helping adding to the code below to plot the images from the firs.pdf
Need helping adding to the code below to plot the images from the first epoch. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
29-kashyap-mask-detaction.pptx
i want to create mask detection using ai and i want explain all workings in tutorial of youtube and also i will make ppt and blog for refrences
Need an detailed analysis of what this code-model is doing- Thanks #St.pdf
Need an detailed analysis of what this code/model is doing. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
# Generate a batch of random noise
noise = np.random.normal(0, 1, (16, latent_dimensions))
# Generate images from the noise
generated_images = generator.predict(noise)
# Rescale the images to 0.
I have to understand this code and i have to explain the each line of.pdf
I have to understand this code and i have to explain the each line of code to my Project guide
Can you plz add comment to each line of code which would be helpful me to understand the
code
Code for Accident detection using CNN:
from tkinter import messagebox
from tkinter import *
from tkinter import simpledialog
import tkinter
from tkinter import filedialog
from tkinter.filedialog import askopenfilename
import time
import cv2
import tensorflow as tf
from collections import namedtuple
import numpy as np
import winsound
main = tkinter.Tk()
main.title("Accident Detection")
main.geometry("1300x1200")
net =
cv2.dnn.readNetFromCaffe("model/MobileNetSSD_deploy.prototxt.txt","model/MobileNetSSD_deploy.caffemodel")
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
global filename
global detectionGraph
global msg
def loadModel():
global detectionGraph
detectionGraph = tf.Graph()
with detectionGraph.as_default():
od_graphDef = tf.compat.v1.GraphDef()
with tf.compat.v2.io.gfile.GFile('model/frozen_inference_graph.pb', 'rb') as file:
serializedGraph = file.read()
od_graphDef.ParseFromString(serializedGraph)
tf.import_graph_def(od_graphDef, name='')
messagebox.showinfo("Training model loaded","Training model loaded")
def beep():
frequency = 2500 # Set Frequency To 2500 Hertz
duration = 1000 # Set Duration To 1000 ms == 1 second
winsound.Beep(frequency, duration)
def uploadVideo():
global filename
filename = filedialog.askopenfilename(initialdir="videos")
pathlabel.config(text=filename)
text.delete('1.0', END)
text.insert(END,filename+" loaded\n");
def calculateCollision(boxes,classes,scores,image_np):
global msg
#cv2.putText(image_np, "NORMAL!", (230, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,
255, 255), 2, cv2.LINE_AA)
for i, b in enumerate(boxes[0]):
if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:
if scores[0][i] > 0.5:
for j, c in enumerate(boxes[0]):
if (i != j) and (classes[0][j] == 3 or classes[0][j] == 6 or classes[0][j] == 8) and scores[0][j]> 0.5:
Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
ra = Rectangle(boxes[0][i][3], boxes[0][i][2], boxes[0][i][1], boxes[0][i][3])
rb = Rectangle(boxes[0][j][3], boxes[0][j][2], boxes[0][j][1], boxes[0][j][3])
ar = rectArea(boxes[0][i][3], boxes[0][i][1],boxes[0][i][2],boxes[0][i][3])
col_threshold = 0.6*np.sqrt(ar)
area(ra, rb)
if (area(ra,rb)<col_threshold) :
print('accident')
msg = 'ACCIDENT!'
beep()
return True
else:
return False
def rectArea(xmax, ymax, xmin, ymin):
x = np.abs(xmax-xmin)
y = np.abs(ymax-ymin)
return x*y
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
def area(a, b): # returns None if rectangles don't intersect
dx = min(a.xma.
Denis Sergienko "Pip install driven deep learning"
Machine learning is still a relatively new and rapidly growing field of computer science. Every day we see new methods for solving increasingly complex problems. But at the same time, using machine learning in your project may look like a challenging task.
In this talk, I want to show that a lot of practical problems can be solved with the help of ready to go pip packages. We will look into the most popular tasks of computer vision and natural language processing.
I have tried running this code below- and it is working- but the accur.pdf
I have tried running this code below, and it is working, but the accuracy is less than expected.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_loss value
not decreased
earlystop = EarlyStopping(patience=10)
# Learning Rate Reduction
learning_rate_reduction = ReduceLROnPlateau(mon.
What is the UML Class diagram for accident detection using CNN- i have.pdf
What is the UML Class diagram for accident detection using CNN.
i have make the class diagram which is not sufficient to my guide.
Class diagram: .
SYSTEM ARCHITECTURE:
Code for the accident detection:-
from tkinter import messagebox
from tkinter import *
from tkinter import simpledialog
import tkinter
from tkinter import filedialog
from tkinter.filedialog import askopenfilename
import time
import cv2
import tensorflow as tf
from collections import namedtuple
import numpy as np
import winsound
main = tkinter.Tk()
main.title("Accident Detection")
main.geometry("1300x1200")
net =
cv2.dnn.readNetFromCaffe("model/MobileNetSSD_deploy.prototxt.txt","model/MobileNetSSD_deploy.caffemodel")
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
global filename
global detectionGraph
global msg
def loadModel():
global detectionGraph
detectionGraph = tf.Graph()
with detectionGraph.as_default():
od_graphDef = tf.compat.v1.GraphDef()
with tf.compat.v2.io.gfile.GFile('model/frozen_inference_graph.pb', 'rb') as file:
serializedGraph = file.read()
od_graphDef.ParseFromString(serializedGraph)
tf.import_graph_def(od_graphDef, name='')
messagebox.showinfo("Training model loaded","Training model loaded")
def beep():
frequency = 2500 # Set Frequency To 2500 Hertz
duration = 1000 # Set Duration To 1000 ms == 1 second
winsound.Beep(frequency, duration)
def uploadVideo():
global filename
filename = filedialog.askopenfilename(initialdir="videos")
pathlabel.config(text=filename)
text.delete('1.0', END)
text.insert(END,filename+" loaded\n");
def calculateCollision(boxes,classes,scores,image_np):
global msg
#cv2.putText(image_np, "NORMAL!", (230, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,
255, 255), 2, cv2.LINE_AA)
for i, b in enumerate(boxes[0]):
if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:
if scores[0][i] > 0.5:
for j, c in enumerate(boxes[0]):
if (i != j) and (classes[0][j] == 3 or classes[0][j] == 6 or classes[0][j] == 8) and scores[0][j]> 0.5:
Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
ra = Rectangle(boxes[0][i][3], boxes[0][i][2], boxes[0][i][1], boxes[0][i][3])
rb = Rectangle(boxes[0][j][3], boxes[0][j][2], boxes[0][j][1], boxes[0][j][3])
ar = rectArea(boxes[0][i][3], boxes[0][i][1],boxes[0][i][2],boxes[0][i][3])
col_threshold = 0.6*np.sqrt(ar)
area(ra, rb)
if (area(ra,rb)<col_threshold) :
print('accident')
msg = 'ACCIDENT!'
beep()
return True
else:
return False
def rectArea(xmax, ymax, xmin, ymin):
x = np.abs(xmax-xmin)
y = np.abs(ymax-ymin)
return x*y
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
def area(a, b): # returns None if rectangles don't intersect
dx = min(a.xmax, b.xmax) - max(a.xmin, .
#Covnet model had been defined class ConvNetNew(torch.nn.Module).pdf
#Covnet model had been defined
class ConvNetNew(torch.nn.Module):
def __init__(self):
super(ConvNetNew, self).__init__()
#############################################################################
#
# TODO: Complete the network #Note: similar as Task 1
#############################################################################
#
# Block 1: 3 x 175 x 300 --> 32 x 87 x 150
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(32)
self.relu1 = nn.ReLU()
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
# Block 2: 32 x 87 x 150 --> 64 x 43 x 75
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(64)
self.relu2 = nn.ReLU()
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
# Block 3: 64 x 43 x 75 --> 128 x 21 x 37
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.relu3 = nn.ReLU()
self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)
# Block 4: 128 x 21 x 37 --> 256 x 10 x 18
self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1)
self.bn4 = nn.BatchNorm2d(256)
self.relu4 = nn.ReLU()
self.maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2)
#### [3%] ####
# AdaptiveAvgPool: 1 x 1
self.avgpool = nn.AdaptiveAvgPool1d(1)
# Linear layers: 256 x 1 x 1 --> 128
self.fc1 = nn.Linear(256 * 1 * 1, 128)
# Dropout
self.dropout = nn.Dropout(0.5)
#############################################################################
#
# END OF YOUR CODE #
#############################################################################
#
def forward(self, x):
#############################################################################
#
# TODO: implement the fordward #Note: similar as Task 1
#############################################################################
#
# Block 1: 3 x 175 x 300 --> 32 x 87 x 150
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.maxpool1(x)
# Block 2: 32 x 87 x 150 --> 64 x 43 x 75
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.maxpool2(x)
# Block 3: 64 x 43 x 75 --> 128 x 21 x 37
x = self.conv3(x)
x = self.bn3(x)
x = self.relu3(x)
x = self.maxpool3(x)
# Block 4: 128 x 21 x 37 --> 256 x 10 x 18
x = self.conv4(x)
x = self.bn4(x)
x = self.relu4(x)
x = self.maxpool4(x)
#### [3%] ####
# AdaptiveAvgPool:
self.avgpool = nn.AdaptiveAvgPool1d(1)
# Flatten the output for the linear layers
x = x.view(x.size(0), -1)
# Linear layers: 256 x 1 x 1 --> 256
x = self.fc1(x)
x = self.relu1(x)
x = self.dropout(x)
#############################################################################
#
# END OF YOUR CODE #
#############################################################################
#
return x
#With the defined convolution layers (ConvNetNew()), the whole contrastive learning
framework could be constructed. The encoder_q and encoder_k have the same convolutional
layers. However, the encoder_k will not be optimized by the.
Generation of Deepfake images using GAN and Least squares GAN.ppt
Our project is to generate deep fake images using deep learning techniques i.e Generative
Adversarial Networks.
We generated Deep Fake images using Traditional GAN and least squares GAN.
could you draw uml diagram for this code from PIL import Image, Im.pdf
could you draw uml diagram for this code
from PIL import Image, ImageTk # used Python Imaging Library (PIL) modules
import numpy as np # fundamental Python module for scientific computing
import os # os module is used for file and directory operations
# tkinter and its modules are used for creating a graphical user interface (gui)
import tkinter as tk
from tkinter import filedialog, messagebox
# Declaration and initialization of the global variables used in this program
# -------------------------------------------------------------------------------
# get the current directory where this program is placed
current_directory = os.path.dirname(os.path.realpath(__file__))
image_file_path = current_directory + '/thumbs_up.bmp' # default image
# Main function where this program starts execution
# -------------------------------------------------------------------------------
def start():
# create a window for the graphical user interface (gui)
gui = tk.Tk()
# set the title of the window
gui.title('Image Operations')
# set the background color of the window
gui['bg'] = 'SeaGreen1'
# create and place a frame on the window with some padding for all four sides
frame = tk.Frame(gui)
# using the grid method for layout management
frame.grid(row = 0, column = 0, padx = 15, pady = 15)
# set the background color of the frame
frame['bg'] = 'DodgerBlue4'
# read and display the default image which is a thumbs up emoji
gui_img = ImageTk.PhotoImage(file = image_file_path)
gui_img_panel = tk.Label(frame, image = gui_img)
# columnspan = 5 -> 5 columns as there are 5 buttons
gui_img_panel.grid(row = 0, column = 0, columnspan = 5, padx = 10, pady = 10)
# create and place five buttons below the image (button commands are expressed
# as lambda functions for enabling input arguments)
# ----------------------------------------------------------------------------
# the first button enables the user to open and view an image from a file
btn1 = tk.Button(frame, text = 'Open Image', width = 10)
btn1['command'] = lambda:open_image(gui_img_panel)
btn1.grid(row = 1, column = 0)
# create and place the second button that shows the image in grayscale
btn2 = tk.Button(frame, text = 'Grayscale', bg = 'gray', width = 10)
btn2.grid(row = 1, column = 1)
btn2['command'] = lambda:display_in_grayscale(gui_img_panel)
# create and place the third button that shows the red channel of the image
btn3 = tk.Button(frame, text = 'Red', bg = 'red', width = 10)
btn3.grid(row = 1, column = 2)
btn3['command'] = lambda:display_color_channel(gui_img_panel, 'red')
# create and place the third button that shows the green channel of the image
btn4 = tk.Button(frame, text = 'Green', bg = 'SpringGreen2', width = 10)
btn4.grid(row = 1, column = 3)
btn4['command'] = lambda:display_color_channel(gui_img_panel, 'green')
# create and place the third button that shows the blue channel of the image
btn5 = tk.Button(frame, text = 'Blue', bg = 'DodgerBlue2', width = 10)
btn5.grid(row = 1, column = 4)
btn5['command'.
Hello- I hope you are doing well- I am doing my project- which is Rans (1).pdf
Hello, I hope you are doing well,
I am doing my project, which is Ransomware attack detection using Deep learning CNNs
I got a result of 0.93, which is not good enough, I have tried to improve my accuracy, but I gave
up. Could you modify the code feature selection or pre-processing to get a higher result? Even
for that result, I have to wait a long time to get it because I increased the epoch, and could you
explain the added part?
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
from keras.preprocessing.image import ImageDataGenerator #, load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation,
BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=128 # maybe 256
IMAGE_HEIGHT=128 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work df['category'].value_counts().plot.bar()
# See sample image
# sample = random.choice(filenames)
# image = load_img("D:\Ransomware_Detection_using _CNN\MixImages"+sample)
# plt.imshow(image)
# in collab it will work df['category'].value_counts().plot.bar()
# Build Model
#===============================================================================================================
# Testing
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='re.
Introduction to Image Processing with MATLAB
The slides describes the fundamentals of image processing with MATLAB for beginners with sample MATLAB codes
I need An expert person in machine learning that can help me . I n.docx
I need An expert person in machine learning that can help me . I need to differentiate between the two images by using CNN code . I'm using google Colab for the execution , Also I need to select my own images dataset
using Google Images .Also presentation from 8 to 10 slides .
this is CNN code :
# Convolutional Neural Network
# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
# Installing Tensorflow
# pip install tensorflow
# Installing Keras
# pip install --upgrade keras
# Part 1 - Building the CNN
# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
# Add convolution Conv2D
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation =
'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32,(3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy',
metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('dataset/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000)
...
I want you to add the output of the F1 score- Precision- ROC AUC- and.pdf
I want you to add the output of the F1 score, Precision, ROC AUC, and Cohen kappa to fit in the
code below, and please consider the code provided.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_loss value
not decreased
earlystop = EarlyStopping(patience=10)
# Learning Rate Reducti.
[AI07] Revolutionizing Image Processing with Cognitive Toolkit
Deep Learning has revolutionized the field of image processing. I'll show real-world examples using CNTK, from anomaly classification using CNNs to generation using Generative Adversarial Networks.
製品/テクノロジ: AI (人工知能)/Deep Learning (深層学習)/Microsoft Azure/Machine Learning (機械学習)
Michael Lanzetta
Microsoft Corporation
Developer Experience and Evangelism
Principal Software Development Engineer
Introduction to Deep Learning and Tensorflow
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session.
Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Python과 node.js기반 데이터 분석 및 가시화
이 슬라이드는 Python과 node.js기반 데이터 분석 및 2D/3D 가시화 도구 및 코딩 방법을 알려주는 내용을 담고 있습니다. 엑셀처럼 데이터 계산 분석하고 싶거나, 수천개 데이터파일을 자동처리한 후, 가시화하고 싶거나, 3D그래픽으로 웹서버 형식 서비스하고 싶을 때 필요한 도구 사용법을 포함하고 있습니다. 데이터 분석, 가시화에 관심있는 분들을 위해, 오픈소스 도구들이 무엇이 있고, 어떻게 설치하고, 사용하는 지 간략히 정리되어 있으니 참고 바랍니다.
예제 소스 코드. https://github.com/mac999/visualize_data_sample
Cypherock Assessment (1).pdf
Title: Embedded Security Analysis Task: Side Channel Analysis and Fault Injection
Objective:
The objective of this task is to assess your ability to work with Python tools, C programming,
Computer Architecture, and apply side channel analysis and fault injection techniques to
uncover a hidden flag embedded within an ELF file compiled for an STM32 processor. You will
use the Lascar and Rainbow tools from Ledger's repository to analyze the binary and retrieve
the hidden flag.
Requirements:
1. Proficiency in Python programming.
2. Basic understanding of side channel analysis and fault injection concepts.
3. Familiarity with ELF file format and embedded systems.
Task Description:
Setup and Familiarization:
1. Clone the Ledger's repository containing Lascar and Rainbow tools.
2. Install the necessary dependencies and set up the environment as per the provided
documentation.
3. Review the documentation and examples to understand how Lascar and Rainbow tools
are used for side channel analysis and fault injection.
Binary Analysis:
1. You will be provided with an ELF file compiled for an STM32 processor.
2. Study the provided stubbed source code for the binary to understand its functionality and
potential vulnerabilities.
Side Channel Analysis/ Fault Injection:
1. Choose a specific side channel analysis technique or fault injection technique based on
your analysis of the binary.
2. Implement the chosen technique using Lascar or Rainbow tools to extract information
from the binary.
3. Document your approach, code snippets, and any findings from the side channel
analysis.
4. Provide a detailed explanation of your fault injection methodology, along with relevant
code snippets and observations.
Flag Retrieval:
1. Apply the insights gained from side channel analysis and fault injection to uncover the
hidden secret embedded within the binary.
2. Document the process you followed to successfully retrieve the secret.
3. Provide the extracted secret as proof of completion.
Evaluation Criteria:
You will be evaluated based on the following criteria:
1. Understanding of side channel analysis and fault injection concepts.
2. Proficiency in understanding the assignment and applying conceptual knowledge in
practice.
3. Explanation of the approach taken.
4. Successful retrieval of the hidden flag.
Submit all scripts, analysis, images, documentation in form of a zip file directly to Cypherock.
Provide examples of the -monkey- attempting to be transferred from a s.docx
Provide examples of the "monkey" attempting to be transferred from a subordinate to their supervisor in either your work or other area.
If the supervisor allowed the "monkey" to transfer, what did they do that allowed the transfer?
If the supervisor deflected the "monkey" transfer, how did they accomplish it?
Do you think your supervisor was correct in handling the attempted "monkey" transfer in that manner?
Why do you think that?
Oncken, W., & Wass, D. (1990). Management time: Who's got the monkey? The Journal of Nursing Administration, 20 (12), 6-9.
.
Provide 3 substantive audit procedures chosen among the following topi.docx
Provide 3 substantive audit procedures chosen among the following topics: free meal, lawsuit, inventory, delivery services, payroll or selection of new suppliers. You must provide 3 different procedures on 3 different accounts.
Exhibit 1
Quon Queso Inc.
Statement of Earnings
For the period ended December 31, 2016
Revenue
547,500
COGS
191,625
Gross Profit
355,875
Expenses
Advertising Expense
27,375
Insurance Expense
2,500
Property Taxes
5,000
General and administrative expenses
16,425
Salary
202,575
Depreciation expense
28,900
EBIT
73,100
Interest expense
13,232
EBT
59,868
Income Tax Expense
17,960
Net Income
41,907
Quon Queso Inc.
Statement of Retained Earnings
For the period ended December 31, 2016
Balance, December 31, 2015
0
Net profit
41,907
Dividends
25,000
Balance, December 31, 2016
16,907
Exhibit 1 (continued)
Quon Queso Inc. Balance Sheet
As at December 31, 2016
Assets
Current
Cash
11,232
Inventory
20,000
Non-Current
Bulding, net
393,100
Equipment, net
108,000
Total
532,332
Liabilities
Current
Accounts payable
5,000
Short-term debt
20,171
Non-current
Longterm Debt
410,253
Share Holders Equity
Common Shares
80,000
R.E
16,907
Total L + S.E
532,332
.
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I have to understand this code and i have to explain the each line of.pdf
I have to understand this code and i have to explain the each line of code to my Project guide
Can you plz add comment to each line of code which would be helpful me to understand the
code
Code for Accident detection using CNN:
from tkinter import messagebox
from tkinter import *
from tkinter import simpledialog
import tkinter
from tkinter import filedialog
from tkinter.filedialog import askopenfilename
import time
import cv2
import tensorflow as tf
from collections import namedtuple
import numpy as np
import winsound
main = tkinter.Tk()
main.title("Accident Detection")
main.geometry("1300x1200")
net =
cv2.dnn.readNetFromCaffe("model/MobileNetSSD_deploy.prototxt.txt","model/MobileNetSSD_deploy.caffemodel")
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
global filename
global detectionGraph
global msg
def loadModel():
global detectionGraph
detectionGraph = tf.Graph()
with detectionGraph.as_default():
od_graphDef = tf.compat.v1.GraphDef()
with tf.compat.v2.io.gfile.GFile('model/frozen_inference_graph.pb', 'rb') as file:
serializedGraph = file.read()
od_graphDef.ParseFromString(serializedGraph)
tf.import_graph_def(od_graphDef, name='')
messagebox.showinfo("Training model loaded","Training model loaded")
def beep():
frequency = 2500 # Set Frequency To 2500 Hertz
duration = 1000 # Set Duration To 1000 ms == 1 second
winsound.Beep(frequency, duration)
def uploadVideo():
global filename
filename = filedialog.askopenfilename(initialdir="videos")
pathlabel.config(text=filename)
text.delete('1.0', END)
text.insert(END,filename+" loaded\n");
def calculateCollision(boxes,classes,scores,image_np):
global msg
#cv2.putText(image_np, "NORMAL!", (230, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,
255, 255), 2, cv2.LINE_AA)
for i, b in enumerate(boxes[0]):
if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:
if scores[0][i] > 0.5:
for j, c in enumerate(boxes[0]):
if (i != j) and (classes[0][j] == 3 or classes[0][j] == 6 or classes[0][j] == 8) and scores[0][j]> 0.5:
Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
ra = Rectangle(boxes[0][i][3], boxes[0][i][2], boxes[0][i][1], boxes[0][i][3])
rb = Rectangle(boxes[0][j][3], boxes[0][j][2], boxes[0][j][1], boxes[0][j][3])
ar = rectArea(boxes[0][i][3], boxes[0][i][1],boxes[0][i][2],boxes[0][i][3])
col_threshold = 0.6*np.sqrt(ar)
area(ra, rb)
if (area(ra,rb)<col_threshold) :
print('accident')
msg = 'ACCIDENT!'
beep()
return True
else:
return False
def rectArea(xmax, ymax, xmin, ymin):
x = np.abs(xmax-xmin)
y = np.abs(ymax-ymin)
return x*y
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
def area(a, b): # returns None if rectangles don't intersect
dx = min(a.xma.
Denis Sergienko "Pip install driven deep learning"
Machine learning is still a relatively new and rapidly growing field of computer science. Every day we see new methods for solving increasingly complex problems. But at the same time, using machine learning in your project may look like a challenging task.
In this talk, I want to show that a lot of practical problems can be solved with the help of ready to go pip packages. We will look into the most popular tasks of computer vision and natural language processing.
I have tried running this code below- and it is working- but the accur.pdf
I have tried running this code below, and it is working, but the accuracy is less than expected.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_loss value
not decreased
earlystop = EarlyStopping(patience=10)
# Learning Rate Reduction
learning_rate_reduction = ReduceLROnPlateau(mon.
What is the UML Class diagram for accident detection using CNN- i have.pdf
What is the UML Class diagram for accident detection using CNN.
i have make the class diagram which is not sufficient to my guide.
Class diagram: .
SYSTEM ARCHITECTURE:
Code for the accident detection:-
from tkinter import messagebox
from tkinter import *
from tkinter import simpledialog
import tkinter
from tkinter import filedialog
from tkinter.filedialog import askopenfilename
import time
import cv2
import tensorflow as tf
from collections import namedtuple
import numpy as np
import winsound
main = tkinter.Tk()
main.title("Accident Detection")
main.geometry("1300x1200")
net =
cv2.dnn.readNetFromCaffe("model/MobileNetSSD_deploy.prototxt.txt","model/MobileNetSSD_deploy.caffemodel")
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
global filename
global detectionGraph
global msg
def loadModel():
global detectionGraph
detectionGraph = tf.Graph()
with detectionGraph.as_default():
od_graphDef = tf.compat.v1.GraphDef()
with tf.compat.v2.io.gfile.GFile('model/frozen_inference_graph.pb', 'rb') as file:
serializedGraph = file.read()
od_graphDef.ParseFromString(serializedGraph)
tf.import_graph_def(od_graphDef, name='')
messagebox.showinfo("Training model loaded","Training model loaded")
def beep():
frequency = 2500 # Set Frequency To 2500 Hertz
duration = 1000 # Set Duration To 1000 ms == 1 second
winsound.Beep(frequency, duration)
def uploadVideo():
global filename
filename = filedialog.askopenfilename(initialdir="videos")
pathlabel.config(text=filename)
text.delete('1.0', END)
text.insert(END,filename+" loaded\n");
def calculateCollision(boxes,classes,scores,image_np):
global msg
#cv2.putText(image_np, "NORMAL!", (230, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,
255, 255), 2, cv2.LINE_AA)
for i, b in enumerate(boxes[0]):
if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:
if scores[0][i] > 0.5:
for j, c in enumerate(boxes[0]):
if (i != j) and (classes[0][j] == 3 or classes[0][j] == 6 or classes[0][j] == 8) and scores[0][j]> 0.5:
Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
ra = Rectangle(boxes[0][i][3], boxes[0][i][2], boxes[0][i][1], boxes[0][i][3])
rb = Rectangle(boxes[0][j][3], boxes[0][j][2], boxes[0][j][1], boxes[0][j][3])
ar = rectArea(boxes[0][i][3], boxes[0][i][1],boxes[0][i][2],boxes[0][i][3])
col_threshold = 0.6*np.sqrt(ar)
area(ra, rb)
if (area(ra,rb)<col_threshold) :
print('accident')
msg = 'ACCIDENT!'
beep()
return True
else:
return False
def rectArea(xmax, ymax, xmin, ymin):
x = np.abs(xmax-xmin)
y = np.abs(ymax-ymin)
return x*y
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
def area(a, b): # returns None if rectangles don't intersect
dx = min(a.xmax, b.xmax) - max(a.xmin, .
#Covnet model had been defined class ConvNetNew(torch.nn.Module).pdf
#Covnet model had been defined
class ConvNetNew(torch.nn.Module):
def __init__(self):
super(ConvNetNew, self).__init__()
#############################################################################
#
# TODO: Complete the network #Note: similar as Task 1
#############################################################################
#
# Block 1: 3 x 175 x 300 --> 32 x 87 x 150
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(32)
self.relu1 = nn.ReLU()
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
# Block 2: 32 x 87 x 150 --> 64 x 43 x 75
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(64)
self.relu2 = nn.ReLU()
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
# Block 3: 64 x 43 x 75 --> 128 x 21 x 37
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.relu3 = nn.ReLU()
self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)
# Block 4: 128 x 21 x 37 --> 256 x 10 x 18
self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1)
self.bn4 = nn.BatchNorm2d(256)
self.relu4 = nn.ReLU()
self.maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2)
#### [3%] ####
# AdaptiveAvgPool: 1 x 1
self.avgpool = nn.AdaptiveAvgPool1d(1)
# Linear layers: 256 x 1 x 1 --> 128
self.fc1 = nn.Linear(256 * 1 * 1, 128)
# Dropout
self.dropout = nn.Dropout(0.5)
#############################################################################
#
# END OF YOUR CODE #
#############################################################################
#
def forward(self, x):
#############################################################################
#
# TODO: implement the fordward #Note: similar as Task 1
#############################################################################
#
# Block 1: 3 x 175 x 300 --> 32 x 87 x 150
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.maxpool1(x)
# Block 2: 32 x 87 x 150 --> 64 x 43 x 75
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.maxpool2(x)
# Block 3: 64 x 43 x 75 --> 128 x 21 x 37
x = self.conv3(x)
x = self.bn3(x)
x = self.relu3(x)
x = self.maxpool3(x)
# Block 4: 128 x 21 x 37 --> 256 x 10 x 18
x = self.conv4(x)
x = self.bn4(x)
x = self.relu4(x)
x = self.maxpool4(x)
#### [3%] ####
# AdaptiveAvgPool:
self.avgpool = nn.AdaptiveAvgPool1d(1)
# Flatten the output for the linear layers
x = x.view(x.size(0), -1)
# Linear layers: 256 x 1 x 1 --> 256
x = self.fc1(x)
x = self.relu1(x)
x = self.dropout(x)
#############################################################################
#
# END OF YOUR CODE #
#############################################################################
#
return x
#With the defined convolution layers (ConvNetNew()), the whole contrastive learning
framework could be constructed. The encoder_q and encoder_k have the same convolutional
layers. However, the encoder_k will not be optimized by the.
Generation of Deepfake images using GAN and Least squares GAN.ppt
Our project is to generate deep fake images using deep learning techniques i.e Generative
Adversarial Networks.
We generated Deep Fake images using Traditional GAN and least squares GAN.
could you draw uml diagram for this code from PIL import Image, Im.pdf
could you draw uml diagram for this code
from PIL import Image, ImageTk # used Python Imaging Library (PIL) modules
import numpy as np # fundamental Python module for scientific computing
import os # os module is used for file and directory operations
# tkinter and its modules are used for creating a graphical user interface (gui)
import tkinter as tk
from tkinter import filedialog, messagebox
# Declaration and initialization of the global variables used in this program
# -------------------------------------------------------------------------------
# get the current directory where this program is placed
current_directory = os.path.dirname(os.path.realpath(__file__))
image_file_path = current_directory + '/thumbs_up.bmp' # default image
# Main function where this program starts execution
# -------------------------------------------------------------------------------
def start():
# create a window for the graphical user interface (gui)
gui = tk.Tk()
# set the title of the window
gui.title('Image Operations')
# set the background color of the window
gui['bg'] = 'SeaGreen1'
# create and place a frame on the window with some padding for all four sides
frame = tk.Frame(gui)
# using the grid method for layout management
frame.grid(row = 0, column = 0, padx = 15, pady = 15)
# set the background color of the frame
frame['bg'] = 'DodgerBlue4'
# read and display the default image which is a thumbs up emoji
gui_img = ImageTk.PhotoImage(file = image_file_path)
gui_img_panel = tk.Label(frame, image = gui_img)
# columnspan = 5 -> 5 columns as there are 5 buttons
gui_img_panel.grid(row = 0, column = 0, columnspan = 5, padx = 10, pady = 10)
# create and place five buttons below the image (button commands are expressed
# as lambda functions for enabling input arguments)
# ----------------------------------------------------------------------------
# the first button enables the user to open and view an image from a file
btn1 = tk.Button(frame, text = 'Open Image', width = 10)
btn1['command'] = lambda:open_image(gui_img_panel)
btn1.grid(row = 1, column = 0)
# create and place the second button that shows the image in grayscale
btn2 = tk.Button(frame, text = 'Grayscale', bg = 'gray', width = 10)
btn2.grid(row = 1, column = 1)
btn2['command'] = lambda:display_in_grayscale(gui_img_panel)
# create and place the third button that shows the red channel of the image
btn3 = tk.Button(frame, text = 'Red', bg = 'red', width = 10)
btn3.grid(row = 1, column = 2)
btn3['command'] = lambda:display_color_channel(gui_img_panel, 'red')
# create and place the third button that shows the green channel of the image
btn4 = tk.Button(frame, text = 'Green', bg = 'SpringGreen2', width = 10)
btn4.grid(row = 1, column = 3)
btn4['command'] = lambda:display_color_channel(gui_img_panel, 'green')
# create and place the third button that shows the blue channel of the image
btn5 = tk.Button(frame, text = 'Blue', bg = 'DodgerBlue2', width = 10)
btn5.grid(row = 1, column = 4)
btn5['command'.
Hello- I hope you are doing well- I am doing my project- which is Rans (1).pdf
Hello, I hope you are doing well,
I am doing my project, which is Ransomware attack detection using Deep learning CNNs
I got a result of 0.93, which is not good enough, I have tried to improve my accuracy, but I gave
up. Could you modify the code feature selection or pre-processing to get a higher result? Even
for that result, I have to wait a long time to get it because I increased the epoch, and could you
explain the added part?
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
from keras.preprocessing.image import ImageDataGenerator #, load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation,
BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=128 # maybe 256
IMAGE_HEIGHT=128 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work df['category'].value_counts().plot.bar()
# See sample image
# sample = random.choice(filenames)
# image = load_img("D:\Ransomware_Detection_using _CNN\MixImages"+sample)
# plt.imshow(image)
# in collab it will work df['category'].value_counts().plot.bar()
# Build Model
#===============================================================================================================
# Testing
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='re.
Introduction to Image Processing with MATLAB
The slides describes the fundamentals of image processing with MATLAB for beginners with sample MATLAB codes
I need An expert person in machine learning that can help me . I n.docx
I need An expert person in machine learning that can help me . I need to differentiate between the two images by using CNN code . I'm using google Colab for the execution , Also I need to select my own images dataset
using Google Images .Also presentation from 8 to 10 slides .
this is CNN code :
# Convolutional Neural Network
# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
# Installing Tensorflow
# pip install tensorflow
# Installing Keras
# pip install --upgrade keras
# Part 1 - Building the CNN
# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
# Add convolution Conv2D
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation =
'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32,(3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy',
metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('dataset/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000)
...
I want you to add the output of the F1 score- Precision- ROC AUC- and.pdf
I want you to add the output of the F1 score, Precision, ROC AUC, and Cohen kappa to fit in the
code below, and please consider the code provided.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_loss value
not decreased
earlystop = EarlyStopping(patience=10)
# Learning Rate Reducti.
[AI07] Revolutionizing Image Processing with Cognitive Toolkit
Deep Learning has revolutionized the field of image processing. I'll show real-world examples using CNTK, from anomaly classification using CNNs to generation using Generative Adversarial Networks.
製品/テクノロジ: AI (人工知能)/Deep Learning (深層学習)/Microsoft Azure/Machine Learning (機械学習)
Michael Lanzetta
Microsoft Corporation
Developer Experience and Evangelism
Principal Software Development Engineer
Introduction to Deep Learning and Tensorflow
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session.
Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Python과 node.js기반 데이터 분석 및 가시화
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Title: Embedded Security Analysis Task: Side Channel Analysis and Fault Injection
Objective:
The objective of this task is to assess your ability to work with Python tools, C programming,
Computer Architecture, and apply side channel analysis and fault injection techniques to
uncover a hidden flag embedded within an ELF file compiled for an STM32 processor. You will
use the Lascar and Rainbow tools from Ledger's repository to analyze the binary and retrieve
the hidden flag.
Requirements:
1. Proficiency in Python programming.
2. Basic understanding of side channel analysis and fault injection concepts.
3. Familiarity with ELF file format and embedded systems.
Task Description:
Setup and Familiarization:
1. Clone the Ledger's repository containing Lascar and Rainbow tools.
2. Install the necessary dependencies and set up the environment as per the provided
documentation.
3. Review the documentation and examples to understand how Lascar and Rainbow tools
are used for side channel analysis and fault injection.
Binary Analysis:
1. You will be provided with an ELF file compiled for an STM32 processor.
2. Study the provided stubbed source code for the binary to understand its functionality and
potential vulnerabilities.
Side Channel Analysis/ Fault Injection:
1. Choose a specific side channel analysis technique or fault injection technique based on
your analysis of the binary.
2. Implement the chosen technique using Lascar or Rainbow tools to extract information
from the binary.
3. Document your approach, code snippets, and any findings from the side channel
analysis.
4. Provide a detailed explanation of your fault injection methodology, along with relevant
code snippets and observations.
Flag Retrieval:
1. Apply the insights gained from side channel analysis and fault injection to uncover the
hidden secret embedded within the binary.
2. Document the process you followed to successfully retrieve the secret.
3. Provide the extracted secret as proof of completion.
Evaluation Criteria:
You will be evaluated based on the following criteria:
1. Understanding of side channel analysis and fault injection concepts.
2. Proficiency in understanding the assignment and applying conceptual knowledge in
practice.
3. Explanation of the approach taken.
4. Successful retrieval of the hidden flag.
Submit all scripts, analysis, images, documentation in form of a zip file directly to Cypherock.
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Quon Queso Inc.
Statement of Earnings
For the period ended December 31, 2016
Revenue
547,500
COGS
191,625
Gross Profit
355,875
Expenses
Advertising Expense
27,375
Insurance Expense
2,500
Property Taxes
5,000
General and administrative expenses
16,425
Salary
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Depreciation expense
28,900
EBIT
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Interest expense
13,232
EBT
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Net Income
41,907
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Statement of Retained Earnings
For the period ended December 31, 2016
Balance, December 31, 2015
0
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41,907
Dividends
25,000
Balance, December 31, 2016
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Exhibit 1 (continued)
Quon Queso Inc. Balance Sheet
As at December 31, 2016
Assets
Current
Cash
11,232
Inventory
20,000
Non-Current
Bulding, net
393,100
Equipment, net
108,000
Total
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Liabilities
Current
Accounts payable
5,000
Short-term debt
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Non-current
Longterm Debt
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Share Holders Equity
Common Shares
80,000
R.E
16,907
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• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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South African Journal of Science: Writing with integrity workshop (2024)
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http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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- 1. Python program to build deep learning algorithm using a CNNs model to detect malware attacks. The code is working, but I faced issues with the last part of the code, and I needed to know precisely where the problem was. I have the dataset of malware and benign in exe, I convert them to grayscale images, and it goes through the filters, and it shows me the output. this is the code import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D import numpy as np from os import listdir from PIL import Image import os.path import numpy as np path = '/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE' h = 256 #height of image w = 256 #width of image # be careful with using this function, it will consume memory, access to disk and time images = [] for f in listdir('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE'): with open(os.path.join('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE', f), 'rb') as img_set: img_arr = img_set.read(h*w) while img_arr: if len(img_arr) == h*w and img_arr not in images: images.append(img_arr) img_arr = img_set.read(h*w) #And you can save them into png files # count = 0 # for img in images: # png = Image.fromarray(np.reshape(list(img), (h,w)).astype('float32'), mode='L') # png.save('image_l%d.png'%count) # count += 1 #reshape images to fit into the CNN model img_list = np.zeros(shape = (len(images), h,w,1), dtype = np.uint8) for j in range(len(images)): img_list[j,:,:,0] = np.reshape(list(images[j]), (h,w)) img_list = img_list.astype('float32') img_list /= 255 # ################################################################ #approca 1 failed go back from scrtuch # implement the architecture of the CNN model = Sequential() #Conv2D Layers model.add(Conv2D(12, (25, 25), padding='same',input_shape=img_list.shape[1:], activation = 'relu')) model.add(Conv2D(12, (25, 25), activation = 'relu')) #Max Pooling Layer model.add(MaxPooling2D(pool_size=(2, 2))) #Conv2D Layer model.add(Conv2D(12, (13, 13), padding='same', activation = 'relu')) model.add(Conv2D(12, (13, 13), activation = 'relu')) #Max Pooling model.add(MaxPooling2D(pool_size=(2, 2))) #Flattening Layer model.add(Flatten()) #Dense Layer model.add(Dense(1024, activation = 'relu')) model.add(Dense(1, activation = 'sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy']) model.summary() ''' FINALLY If you have a set of executable files from both types (benign and malicious) you can try to fit the model. You should prepare a list of images for malwares and a list for benign and here how you fit the model. ''' #Change the parameters to whatever suits you I already have to files that contain benign files and malicious images, but it shows me syntax error!
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