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CNN.py
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CNN.py
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# Part 1 - Building the CNN
# Importing the Keras libraries and packages
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
train_dir = 'DATASET/train'
test_dir ='DATASET/test'
trsam = 8000
tesam = 2000
epochs =10
batch_size = 32
if K.image_data_format() == 'channels_first':
input_shape = (3, 128, 128)
else:
input_shape = (128, 128, 3)
# Initialising the CNN
model = Sequential()
# Step 1 - Convolution
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
# Step 2 - Pooling
model.add(MaxPooling2D(pool_size=(2, 2)))
# Adding a second convolutional layer
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# Adding a second convolutional layer
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# Step 3 - Flattening
model.add(Flatten())
# Step 4 - Full connection
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(6))
model.add(Activation('softmax'))
# Compiling the CNN
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(128, 128),
batch_size=batch_size,
class_mode='categorical')
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(128, 128),
batch_size=batch_size,
class_mode='categorical')
model.fit_generator(
train_generator,
steps_per_epoch=trsam // batch_size,
epochs=epochs,
validation_data=test_generator,
validation_steps=tesam // batch_size)