Save and load at a same time

Deep Learning

Preparation
1.  Virtual box and Vagrant

2.  Install Apache

3.  Install MySQL

4.  Install Python

5.  Hello World with Python

Deep learning programming
1. Install Keras and test deep learning

2. Save learned parameters and load the parameters

3. Save and load at a same time

4. Use own dataset

Save and load at a same time

1. Create a "reuters_tosaveload.py" file and open the file. Write as follows in the file:

#!/usr/bin/env python
# -*- coding: UTF-8 -*-

'''Trains and evaluate a simple MLP
on the Reuters newswire topic classification task.
'''
from __future__ import print_function

import numpy as np
import keras
from keras.datasets import reuters
from keras.models import Sequential
from keras.models import model_from_json
from keras.layers import Dense, Dropout, Activation
from keras.utils import np_utils
from keras.optimizers import Adam
from keras.preprocessing.text import Tokenizer
import keras.callbacks
import keras.backend.tensorflow_backend as KTF
import tensorflow as tf
import os.path

f_log = './log'
f_model = './model'
model_filename = 'reuters_model.json'
weights_filename = 'reuters_model_weights.hdf5'

max_words = 1000
batch_size = 32
epochs = 5

print('Loading data...')
(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_words,
                                                         test_split=0.2)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

num_classes = np.max(y_train) + 1
print(num_classes, 'classes')

print('Vectorizing sequence data...')
tokenizer = Tokenizer(num_words=max_words)
x_train = tokenizer.sequences_to_matrix(x_train, mode='binary')
x_test = tokenizer.sequences_to_matrix(x_test, mode='binary')
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)

print('Convert class vector to binary class matrix '
      '(for use with categorical_crossentropy)')
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

old_session = KTF.get_session()

print('y_train shape:', y_train.shape)
print('y_test shape:', y_test.shape)

print('Building model...')
session = tf.Session('')
KTF.set_session(session)

json_string = open(os.path.join(f_model, model_filename)).read()
model = model_from_json(json_string)

model.summary()
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
model.load_weights(os.path.join(f_model,weights_filename))

tb_cb = keras.callbacks.TensorBoard(log_dir=f_log, histogram_freq=0)
cp_cb = keras.callbacks.ModelCheckpoint(filepath = os.path.join(f_model,'reuters_model{epoch:02d}-loss{loss:.2f}-acc{acc:.2f}-vloss{val_loss:.2f}-vacc{val_acc:.2f}.hdf5'), monitor='val_loss', verbose=1, save_best_only=True, mode='auto')
cbks = [tb_cb, cp_cb]

history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_split=0.1,
                    callbacks=cbks)
score = model.evaluate(x_test, y_test,
                       batch_size=batch_size, verbose=1)
print('')
print('Test score:', score[0])
print('Test accuracy:', score[1])
print('save the architecture of a model')

json_string = model.to_json()
open(os.path.join(f_model,'reuters_model.json'), 'w').write(json_string)
yaml_string = model.to_yaml()
open(os.path.join(f_model,'reuters_model.yaml'), 'w').write(yaml_string)
print('save weights')
model.save_weights(os.path.join(f_model,'reuters_model_weights.hdf5'))
KTF.set_session(old_session)

This file will save the parameters and load the parameters at a same time. We will try this script. Run the following commands:

$ sudo cd /vagrant
$ sudo python3 reuters_tosaveload.php

Then the learning will start:

Learning and test finished.

The test accuracy is getting lower and the test score is getting higher.

Maybe because I'm using exactly same data set repeatedly.