attribute.py

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 11 19:59:32 2017

@author: shreya
"""
from keras.utils import np_utils
import h5py
import keras
from keras import backend as K
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.layers import Activation
import numpy as np
from keras.callbacks import ModelCheckpoint
from keras.utils.generic_utils import get_custom_objects

def custom_activation(x):
    return (K.sigmoid(x) * x)

get_custom_objects().update({'custom_activation': Activation(custom_activation)})

scores=np.zeros([40,1])

with h5py.File('vgg_face.h5')as hf:
        x_train=hf['train'][:].transpose()
        y_train=hf['train_label'][:].transpose()
        x_test=hf['test'][:].transpose()
        y_test=hf['test_label'][:].transpose()       

for i in range(40):
    model_name='celeba_attr_'+str(i+1)+'.h5'
    
    y_train=y_train[:,i]
    y_test=y_test[:,i]   
    y_train = np_utils.to_categorical(y_train,2)
    y_test = np_utils.to_categorical(y_test, 2)
    
        
    model = Sequential()
    model.add(Dense(1024, input_shape=(4096,)))
    model.add(Activation(custom_activation))
    model.add(Dropout(0.2))
    model.add(Dense(512))
    model.add(Activation(custom_activation))
    model.add(Dropout(0.4))
    model.add(Dense(64))
    model.add(Activation(custom_activation))
    model.add(Dense(2, activation='softmax'))
    model.summary()
    
      
    model.compile(loss='binary_crossentropy',optimizer=keras.optimizers.SGD( lr=0.01, decay=0 , momentum=0.9, nesterov=True), metrics=["accuracy"])
    filepath="weights.h5"
    checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
    early_stopping=keras.callbacks.EarlyStopping(monitor='val_acc', patience=50, verbose=0, mode='max')
        
    model.fit(x_train, y_train, batch_size=64, nb_epoch=500, callbacks=[checkpoint,early_stopping],verbose=1, validation_data=(x_test, y_test))
    model.load_weights("weights.h5")

    model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD(lr=0.1, decay=0 , momentum=0.9, nesterov=True), metrics=['accuracy'])

    score = model.evaluate([x_test], y_test,  verbose=0)
    scores[i]=score[1];    
    model.save(model_name)