# -*- coding: utf-8 -*-

"""

Created on Fri Aug 16 09:24:20 2019

@author: Sumit

"""

from keras.models import Sequential

from keras.layers import Dense, Convolution2D,MaxPooling2D,Flatten

model = Sequential()

model.add(Convolution2D(32,(3,3),input_shape = (64,64,1),activation = 'relu'))

model.add(MaxPooling2D(pool_size = (2,2)))

model.add(Convolution2D(32,(3,3),activation = 'relu'))

model.add(MaxPooling2D(pool_size = (2,2)))

model.add(Flatten())

model.add(Dense(units = 128,activation = 'relu'))

model.add(Dense(units = 10,activation = 'softmax'))

model.compile(optimizer = 'adam',loss = 'categorical_crossentropy',metrics = ['accuracy'])

from keras.preprocessing.image import ImageDataGenerator

train_gen = ImageDataGenerator(rescale = 1./255,horizontal_flip= True,shear_range=0.2,zoom_range=0.2)

test_gen = ImageDataGenerator(rescale = 1./255)

train_data = train_gen.flow_from_directory('data/train',

)

test_data = test_gen.flow_from_directory('data/test',

class_mode='categorical')

model.fit_generator(train_data,

validation_data = test_data,

epochs = 10,

steps_per_epoch = 2599,

validation_steps = 1002

)

json_model = model.to_json()

with open("model-bw.json", "w") as json_file:

json_file.write(json_model)

model.save_weights('model-bw.h5')