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cnn.py
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cnn.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jul 28 18:13:08 2020
@author: SAI AJAY
"""
# import libraries
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
# Pre Processing
#pre processing train set
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
training_set = train_datagen.flow_from_directory('dataset/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
# Pre Processing Test Data
test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
#Building CNN
# Intilaization
cnn = tf.keras.models.Sequential()
# Convolution
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[64, 64, 3]))
# Pooling
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
#Adding 2nd Convolutional Layer
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
#Flattening
cnn.add(tf.keras.layers.Flatten())
# Fully connected layer
cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))
#Output Layer
cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))
#Training CNN
cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
cnn.fit(x = training_set, validation_data = test_set, epochs = 25)
#Making Prediction
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 = cnn.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
prediction = 'dog'
else:
prediction = 'cat'
print(prediction)