model6.py

# モデル6
#
# ドロップアウト（Epoch追加 1,000=>5,000 / fcノード追加 500=>1,000）
#

import time
from datetime import datetime
from load_data import load2d
from saver import save_arch, save_history
from utils import reshape2d_by_image_dim_ordering
from plotter import plot_hist, plot_model_arch
import pickle

import numpy as np
# This module will be removed in 0.20.
from sklearn.cross_validation import train_test_split

from data_generator import FlippedImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, Flatten, Dense, Activation, Dropout
from keras.optimizers import SGD
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as K


# 変数
model_name = 'model6'
nb_epoch = 5000
validation_split = 0.2
lr = 0.01
start = 0.03
stop = 0.001
learning_rates = np.linspace(start, stop, nb_epoch)
momentum = 0.9
nesterov = True
loss_method = 'mean_squared_error'
arch_path = 'model/' + model_name + '-arch-' + str(nb_epoch) + '.json'
weights_path = 'model/' + model_name + '-weights-' + str(nb_epoch) + '.hdf5'


# データ読み込み
X, y = load2d()
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=validation_split, random_state=42)

# image_dim_orderring に合わせて2D画像のshapeを変える
X_train, input_shape = reshape2d_by_image_dim_ordering(X_train)
X_val,   _           = reshape2d_by_image_dim_ordering(X_val)


# モデル定義
model = Sequential()

model.add(Convolution2D(32, 3, 3, input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.1))

model.add(Convolution2D(64, 2, 2))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Convolution2D(128, 2, 2))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

model.add(Flatten())
model.add(Dense(1000))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1000))
model.add(Activation('relu'))
model.add(Dense(30))

save_arch(model, arch_path) # モデルを保存しておく


# トレーニングの準備
checkpoint_collback = ModelCheckpoint(filepath = weights_path,
                                      monitor='val_loss',
                                      save_best_only=True,
                                      mode='auto')

change_lr = LearningRateScheduler(lambda epoch: float(learning_rates[epoch]))

flip_gen = FlippedImageDataGenerator()

sgd = SGD(lr=lr, momentum=momentum, nesterov=nesterov)
model.compile(loss=loss_method, optimizer=sgd)


# トレーニング
start_time = time.time()
print('start_time: %s' % (datetime.now()))
hist = model.fit_generator(flip_gen.flow(X_train, y_train),
                           samples_per_epoch=X_train.shape[0],
                           nb_epoch=nb_epoch,
                           validation_data=(X_val, y_val),
                           callbacks=[checkpoint_collback, change_lr])
print('end_time: %s, duracion(min): %d' % (datetime.now(), int(time.time()-start_time) / 60))


# プロットしてファイルとして保存する
# plot_hist(hist, model_name)
# plot_model_arch(model, model_name)
save_history(hist, model_name)