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cnn.py
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cnn.py
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# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
# Part -1 Building The CNN
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
# Part - 2 Initialiasing the CNN
classifier = Sequential()
#Step 1 - Convolution
"""
Theano backend: We use input_shape(3, 64, 64)
Tensorflow backend: input_shape(64, 64, 3)
"""
classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation= 'relu'))
#Step 2- Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
#Step 3- Flattering
classifier.add(Flatten())
#Step 4- Full Connection Layers
classifier.add(Dense(output_dim = 128, activation= 'relu' ))
classifier.add(Dense(output_dim = 1, activation= 'sigmoid' ))
#Compiling The CNN
classifier.compile(optimizer= 'adam', loss= 'binary_crossentropy' , metrics= ['accuracy'])
# Part 3 - Fitting the CNN to the images with the images augmentation
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
rotation_range=90,
width_shift_range=0.2,
height_shift_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)
# Improving our CNN model : Two options : By augmenting the convolution or by adding more full connected layers
classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation= 'relu'))
#Step 2- Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Second convolution layer
classifier.add(Convolution2D(32, 3, 3, activation= 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
#Step 3- Flattering
classifier.add(Flatten())
#Step 4- Full Connection Layers
classifier.add(Dense(output_dim = 128, activation= 'relu' ))
classifier.add(Dense(output_dim = 1, activation= 'sigmoid' ))
#Compiling The CNN
classifier.compile(optimizer= 'adam', loss= 'binary_crossentropy' , metrics= ['accuracy'])
# Part 3 - Fitting the CNN to the images with the images augmentation
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
rotation_range=90,
width_shift_range=0.2,
height_shift_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)
# Making Single prediction4
import numpy as np
from keras.preprocessing import image
test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size=(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices
if result[][] == 1 :
prediction = 'dog'
else:
prediction = 'cat'