import tensorflow as tf
import numpy as np                                
#import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import keras as k
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from keras.optimizers import SGD, Adam
from keras.regularizers import l2
import h5py
from keras.models import load_model
from keras.preprocessing.image import ImageDataGenerator

#load data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
img_rows, img_cols , channels= 32,32,3
#for i in range(0,9):
#    plt.subplot(330 + 1 + i)
#    plt.imshow(x_train[i])
#plt.show()#load data
#(x_train, y_train), (x_test, y_test) = cifar10.load_data()
#img_rows, img_cols , channels= 32,32,3
#for i in range(0,9):
#    plt.subplot(330 + 1 + i)
#    plt.imshow(x_train[i])
#plt.show()

#reshape into images
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, channels)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, channels)
input_shape = (img_rows, img_cols, 1)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

#convert integers to float; normalise and center the mean
x_train=x_train.astype("float32")  
x_test=x_test.astype("float32")
mean=np.mean(x_train)
std=np.std(x_train)
x_test=(x_test-mean)/std
x_train=(x_train-mean)/std

# labels
num_classes=10
y_train = k.utils.to_categorical(y_train, num_classes)
y_test = k.utils.to_categorical(y_test, num_classes)

# build and compile the model  (roughly following the VGG paper)

#reg=l2(1e-4)   # L2 or "ridge" regularisation
reg=None
num_filters=32
ac='relu'
adm=Adam(lr=0.001,decay=0, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
opt=adm
drop_dense=0.5
drop_conv=0

model = Sequential()

model.add(Conv2D(num_filters, (3, 3), activation=ac, kernel_regularizer=reg, input_shape=(img_rows, img_cols, channels),padding='same'))
model.add(BatchNormalization(axis=-1))
model.add(Conv2D(num_filters, (3, 3), activation=ac,kernel_regularizer=reg,padding='same'))
model.add(BatchNormalization(axis=-1))
model.add(MaxPooling2D(pool_size=(2, 2)))   # reduces to 16x16x3xnum_filters
model.add(Dropout(drop_conv))

model.add(Conv2D(2*num_filters, (3, 3), activation=ac,kernel_regularizer=reg,padding='same'))
model.add(BatchNormalization(axis=-1))
model.add(Conv2D(2*num_filters, (3, 3), activation=ac,kernel_regularizer=reg,padding='same'))
model.add(BatchNormalization(axis=-1))
model.add(MaxPooling2D(pool_size=(2, 2)))   # reduces to 8x8x3x(2*num_filters)
model.add(Dropout(drop_conv))

model.add(Conv2D(4*num_filters, (3, 3), activation=ac,kernel_regularizer=reg,padding='same'))
model.add(BatchNormalization(axis=-1))
model.add(Conv2D(4*num_filters, (3, 3), activation=ac,kernel_regularizer=reg,padding='same'))
model.add(BatchNormalization(axis=-1))
model.add(MaxPooling2D(pool_size=(2, 2)))   # reduces to 4x4x3x(4*num_filters)
model.add(Dropout(drop_conv))

model.add(Flatten())
model.add(Dense(512, activation=ac,kernel_regularizer=reg))
model.add(BatchNormalization())
model.add(Dropout(drop_dense))
model.add(Dense(num_classes, activation='softmax'))

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


#print model summary
model.summary()