115 lines
4.5 KiB
Python
115 lines
4.5 KiB
Python
# -*- coding: utf-8 -*-
|
|
from __future__ import unicode_literals
|
|
# text in Western (Windows 1252)
|
|
|
|
import pickle
|
|
import numpy as np
|
|
from keras import optimizers
|
|
from keras.models import Model
|
|
from keras.layers import Dense, Dropout, Input
|
|
from keras.layers.merge import concatenate
|
|
from keras.layers.convolutional import Conv1D
|
|
from keras.layers.convolutional import MaxPooling1D
|
|
from keras.layers import Flatten
|
|
# from keras import backend as Input
|
|
np.random.seed(7)
|
|
|
|
# get_ipython().magic('run ../../../prepare_data.py')
|
|
|
|
# import sys
|
|
# # sys.path.insert(0, '../../../')
|
|
# sys.path.insert(0, '/home/luka/Developement/accetuation/')
|
|
from prepare_data import *
|
|
|
|
|
|
# X_train, X_other_features_train, y_train, X_validate, X_other_features_validate, y_validate = generate_full_matrix_inputs()
|
|
# save_inputs('../../internal_representations/inputs/shuffeled_matrix_train_inputs_other_features_output_11.h5', X_train, y_train, other_features = X_other_features_train)
|
|
# save_inputs('../../internal_representations/inputs/shuffeled_matrix_validate_inputs_other_features_output_11.h5', X_validate, y_validate, other_features = X_other_features_validate)
|
|
# X_train, X_other_features_train, y_train = load_inputs('cnn/internal_representations/inputs/shuffeled_matrix_train_inputs_other_features_output_11.h5', other_features=True)
|
|
# X_validate, X_other_features_validate, y_validate = load_inputs('cnn/internal_representations/inputs/shuffeled_matrix_validate_inputs_other_features_output_11.h5', other_features=True)
|
|
# letters
|
|
# data = Data('l', save_generated_data=False, number_of_syllables=True)
|
|
|
|
# syllabled letters
|
|
data = Data('s', save_generated_data=False, accent_classification=True)
|
|
data.generate_data('letters_word_accetuation_train',
|
|
'letters_word_accetuation_test',
|
|
'letters_word_accetuation_validate', content_name='SlovarIJS_BESEDE_utf8.lex',
|
|
content_shuffle_vector='content_shuffle_vector', shuffle_vector='shuffle_vector',
|
|
inputs_location='', content_location='')
|
|
|
|
# concatenate test and train data
|
|
# data.x_train = np.concatenate((data.x_train, data.x_test), axis=0)
|
|
# data.x_other_features_train = np.concatenate((data.x_other_features_train, data.x_other_features_test), axis=0)
|
|
# data.y_train = np.concatenate((data.y_train, data.y_test), axis=0)
|
|
|
|
# concatenate all data
|
|
data.x_train = np.concatenate((data.x_train, data.x_test, data.x_validate), axis=0)
|
|
data.x_other_features_train = np.concatenate((data.x_other_features_train, data.x_other_features_test, data.x_other_features_validate), axis=0)
|
|
data.y_train = np.concatenate((data.y_train, data.y_test, data.y_validate), axis=0)
|
|
|
|
num_examples = len(data.x_train) # training set size
|
|
nn_output_dim = 13
|
|
nn_hdim = 516
|
|
batch_size = 16
|
|
# actual_epoch = 1
|
|
actual_epoch = 20
|
|
# num_fake_epoch = 2
|
|
num_fake_epoch = 20
|
|
|
|
# letters
|
|
# conv_input_shape=(23, 36)
|
|
|
|
# syllabled letters
|
|
# conv_input_shape=(10, 252)
|
|
|
|
# syllables
|
|
conv_input_shape=(10, 5168)
|
|
|
|
|
|
# othr_input = (140, )
|
|
othr_input = (150, )
|
|
|
|
conv_input = Input(shape=conv_input_shape, name='conv_input')
|
|
# letters
|
|
# x_conv = Conv1D(115, (3), padding='same', activation='relu')(conv_input)
|
|
# x_conv = Conv1D(46, (3), padding='same', activation='relu')(x_conv)
|
|
|
|
# syllabled letters
|
|
x_conv = Conv1D(200, (2), padding='same', activation='relu')(conv_input)
|
|
x_conv = MaxPooling1D(pool_size=2)(x_conv)
|
|
x_conv = Flatten()(x_conv)
|
|
|
|
othr_input = Input(shape=othr_input, name='othr_input')
|
|
|
|
x = concatenate([x_conv, othr_input])
|
|
# x = Dense(1024, input_dim=(516 + 256), activation='relu')(x)
|
|
x = Dense(256, activation='relu')(x)
|
|
x = Dropout(0.3)(x)
|
|
x = Dense(256, activation='relu')(x)
|
|
x = Dropout(0.3)(x)
|
|
x = Dense(256, activation='relu')(x)
|
|
x = Dropout(0.3)(x)
|
|
x = Dense(nn_output_dim, activation='sigmoid')(x)
|
|
|
|
|
|
|
|
|
|
model = Model(inputs=[conv_input, othr_input], outputs=x)
|
|
opt = optimizers.Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
|
|
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[actual_accuracy,])
|
|
# model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
|
|
|
|
|
|
history = model.fit_generator(data.generator('train', batch_size, content_name='SlovarIJS_BESEDE_utf8.lex', content_location=''),
|
|
data.x_train.shape[0]/(batch_size * num_fake_epoch),
|
|
epochs=actual_epoch*num_fake_epoch,
|
|
verbose=2
|
|
)
|
|
|
|
name = '40_epoch'
|
|
model.save(name + '.h5')
|
|
output = open(name + '_history.pkl', 'wb')
|
|
pickle.dump(history.history, output)
|
|
output.close()
|