Image classifier in 5 minutes. For explanation click http://www.hellocodings.com/2017/10/build-neural-network-classifier-in-5.html

IM5.py

# -*- coding: utf-8 -*-
"""
Created on Tue Oct 10 18:44:48 2017

@author: shash
"""

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import *
#from keras.layers import Conv2D, MaxPooling2D
#Step 2 Load all imageas as data and testing images
''' gathered_data 
		/train
			/Men
			/Women
		/Validate
			/Men
			/Women
Tree Structure of my data sets
'''
train_directory = r'gathered_data/train'#Directory of Training Data
check_directory = r'gathered_data/validate'	#Directory of data that will be checked after training

no_of_training_samples = 1410 	#No of images of training
no_of_validation_samples = 400	#No of images to check after training

epoch = 10
batch_size = 20

# data input shape
height, width = 150, 150

#Neural Network
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape = (width, height, 3)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
'''Pooling Layers further downsamples the images
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))'''
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Flatten())
'''Dense - It deals with 2D tensor and outputs a 2d tensor
every node gets connected'''
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(Dense(1))
model.add(Activation('sigmoid'))


model.compile(loss='binary_crossentropy', optimizer='adam', metrics = ['accuracy'])
inp_data = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2)
valid_datax = ImageDataGenerator(rescale = 1. /255, horizontal_flip = True)
#Convert the image directory contents in a readable form for Keras using image data generator
input_data = inp_data.flow_from_directory(train_directory, target_size=(150, 150), batch_size = 20, class_mode = 'binary')

#Convert the validation directory contents into a readable form for Keras using the same image data generator
valid_data = valid_datax.flow_from_directory(check_directory, target_size = (150, 150), batch_size = 20, class_mode = 'binary')

model.fit_generator(input_data, steps_per_epoch = 70, epochs = 50,
          validation_data = valid_data, validation_steps = 20)