'''adapted from https://arxiv.org/pdf/1602.07360.pdf'''

import keras

import numpy as np

from keras.layers import Input, Dense, Lambda, Merge

from keras.models import Model

from keras import backend as K

from keras import objectives

from keras.datasets import mnist

from keras.layers.core import Reshape

from keras.models import Sequential

from keras.layers import Dense, Dropout, Activation, Flatten

from keras.layers import Convolution2D, MaxPooling2D

from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, UpSampling2D

from keras.utils import np_utils

from keras.layers.normalization import BatchNormalization

from keras.optimizers import SGD

from keras.layers.advanced_activations import ELU

from keras.layers.pooling import GlobalAveragePooling2D

import pandas as pd

import matplotlib.pyplot as plt

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.astype('float32') / 255.

x_test = x_test.astype('float32') / 255.

x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))

x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))

x_train_CNN=x_train[1:2]

y_train1=pd.get_dummies(y_train[1:2]).T

y_train2=y_train1

epochs=3

learning_rate = 0.07

decay_rate = 5e-5

momentum = 0.6

sgd = SGD(lr=learning_rate,momentum=momentum, decay=decay_rate, nesterov=False)

input_shape=(28,28,1)

squeeze = Sequential()

squeeze.add(Lambda(lambda x: x ** 2,input_shape=(784,),output_shape=(1,784)))

squeeze.add(Reshape((28,28,1)))

squeeze.add(Convolution2D(2, 3,3,

border_mode='valid',

input_shape=input_shape))

squeeze.add(BatchNormalization(mode=2))

squeeze.add(ELU(alpha=1.0))

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

squeeze.add(Convolution2D(1, 1, 1,

init='glorot_uniform'))

squeeze.add(BatchNormalization(mode=2))

squeeze.add(ELU(alpha=1.0))

squeeze_left=squeeze

squeeze_left.add(Convolution2D(2, 3,3,

border_mode='valid',

input_shape=input_shape))

squeeze_left.add(ELU(alpha=1.0))

squeeze_right=squeeze

squeeze_right.add(Convolution2D(2, 3,3,

border_mode='valid',

input_shape=input_shape))

squeeze_right.add(ELU(alpha=1.0))

squeeze0 = Sequential()

squeeze0.add(Merge([squeeze_left,squeeze_right],mode = 'concat'))

squeeze0.add(Dropout(0.2))

squeeze0.add(GlobalAveragePooling2D((1, 28, 28, 1)))

squeeze0.add(Dense(1))

squeeze0.add(Activation('sigmoid'))

squeeze0.compile(loss='mean_squared_error', optimizer=sgd,metrics = ['accuracy'])

squeeze0.summary()

squeeze0.fit(x_train_CNN,np.array(y_train2),

nb_epoch=15,

batch_size=30,verbose=1)

a1=squeeze0.predict_classes(x_train_CNN,verbose=1)

a1