![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',](https://image.slidesharecdn.com/ineedanexpertpersoninmachinelearningthatcanhelpme-221010183234-4963f526/85/I-need-An-expert-person-in-machine-learning-that-can-help-me-I-n-docx-3-320.jpg)
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)