Added a new program

Dog_Cat.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 29 21:35:39 2019
+
+@author: Amith R
+Image classification using Convolutional Neural Networks
+"""
+#DATA Pre-Processing
+#data set already divided into test and training set
+# encoding is not required
+# feature scaling is required
+# 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
+
+# initialising the CNN
+# object of sequential class
+classifier = Sequential()
+
+# step1 - Convolution
+classifier.add(Convolution2D(32,3,3,input_shape=(64,64,3),activation='relu'))
+# using different feature detectors
+
+#step2- MAxpooling
+classifier.add(MaxPooling2D(pool_size=(2,2)))
+#size of feature map reduced by 2
+
+#adding a second convolutional layer to 
+classifier.add(Convolution2D(32,3,3,activation='relu'))
+classifier.add(MaxPooling2D(pool_size=(2,2)))
+
+#step3 -Flattening
+classifier.add(Flatten())
+
+#step4 - FullConnection
+ #fully connected layer
+classifier.add(Dense(output_dim=128,activation = 'relu'))
+classifier.add(Dense(output_dim=1,activation = 'sigmoid'))
+
+# compliling the CNN
+classifier.compile (optimizer ='adam',loss='binary_crossentropy',metrics=['accuracy'])
+
+# Fitting the CNN to dataset
+from keras.preprocessing.image import ImageDataGenerator
+train_datagen = ImageDataGenerator(
+        rescale=1./255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True)
+#object used for augmentation
+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,
+        samples_per_epoch=8000, #no of images in training set
+        epochs=25,
+        validation_data=test_set,
+        validation_steps=2000)