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Convolutional_neural_network.py
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Convolutional_neural_network.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Nov 22 15:31:19 2018
@author: ayo
"""
#Step 1----- building the CNN
#importing the libraries
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
#initializing the CNN
classifier = Sequential()
#Convolution
classifier.add(Convolution2D(32,3,3, input_shape=(150,150,3), activation="relu"))
#64,64
#max pooling
#reducing the models complexity without reducing its performance
classifier.add(MaxPooling2D(pool_size=(2,2)))
#another second convolutional layer
classifier.add(Convolution2D(32,3,3, activation="relu"))
classifier.add(MaxPooling2D(pool_size=(2,2)))
#another third convolutional layer
classifier.add(Convolution2D(32,3,3, activation="relu"))
classifier.add(MaxPooling2D(pool_size=(2,2)))
#Flattening
classifier.add(Flatten())
#FULLL CONNECTION
#fully connected layer
classifier.add(Dense(128,activation="relu"))
#another fully connected layer
classifier.add(Dense(128,activation="relu"))
#output layer
classifier.add(Dense(1,activation="sigmoid"))
#compiling the CNN
classifier.compile(optimizer="adam",loss="binary_crossentropy",metrics=["accuracy"])
#if more than two outcomes , well choose categorical_crossentropy
#part 2
#fitting the CNN to our 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=(150,150),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size=(150,150),
batch_size=32,
class_mode='binary')
classifier.fit_generator(training_set,
steps_per_epoch=8000,
epochs=25,
validation_data=test_set,
validation_steps=2000)