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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)

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I need  An expert person in machine learning  that can help me . I n.docx

  • 1. 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
  • 2. 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'))
  • 3. 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',
  • 4. 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)