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

from __future__ import absolute_import

from __future__ import print_function

from keras.preprocessing.image import ImageDataGenerator

from keras.models import Sequential

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

from keras.layers.advanced_activations import PReLU

from keras.layers.convolutional import Convolution2D, MaxPooling2D

from keras.optimizers import SGD, Adadelta, Adagrad

from keras.utils import np_utils, generic_utils

from data import load_data

import matplotlib.pyplot as plt

'''

train the dataset of face 42000 image

learning rate : 0.0015

pathc size:100

dropout :0.25

l2 = 0.01

'''

def funcnn(LR,BS):

data, label = load_data()

label = np_utils.to_categorical(label, 10)

model = Sequential()

model.add(Convolution2D(4, 1, 5, 5, border_mode='valid'))

model.add(Activation('relu'))

model.add(Dropout(0.25))

model.add(Convolution2D(8,4, 3, 3, border_mode='valid'))

model.add(Activation('relu'))

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

model.add(Dropout(0.25))

model.add(Convolution2D(16, 8, 3, 3, border_mode='valid'))

model.add(Activation('relu'))

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

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(16*4*4, 256, init='normal'))

model.add(Activation('tanh'))

model.add(Dense(256, 10, init='normal'))

model.add(Activation('softmax'))

sgd = SGD(l2=0.001,lr=LR, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss='categorical_crossentropy', optimizer=sgd,class_mode="categorical")

#checkpointer = ModelCheckpoint(filepath="weight.hdf5",verbose=1,save_best_only=True)

#model.fit(data, label, batch_size=100,nb_epoch=10,shuffle=True,verbose=1,show_accuracy=True,validation_split=0.2,callbacks=[checkpointer])

result = model.fit(data, label, batch_size=BS,nb_epoch=20,shuffle=True,verbose=1,show_accuracy=True,validation_split=0.2)

#model.save_weights(weights,accuracy=False)

# plot the result

plt.figure

plt.plot(result.epoch,result.history['acc'],label="acc")

plt.plot(result.epoch,result.history['val_acc'],label="val_acc")

plt.scatter(result.epoch,result.history['acc'],marker='*')

plt.scatter(result.epoch,result.history['val_acc'])

plt.legend(loc='under right')

plt.show()

plt.figure

plt.plot(result.epoch,result.history['loss'],label="loss")

plt.plot(result.epoch,result.history['val_loss'],label="val_loss")

plt.scatter(result.epoch,result.history['loss'],marker='*')

plt.scatter(result.epoch,result.history['val_loss'],marker='*')

plt.legend(loc='upper right')

plt.show()