75 lines
3.0 KiB
Python
Executable File
75 lines
3.0 KiB
Python
Executable File
# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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# text in Western (Windows 1252)
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import pickle
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import numpy as np
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np.random.seed(7)
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import sys
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from prepare_data import *
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# preprocess data
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# data = Data('l', allow_shuffle_vector_generation=True, save_generated_data=False, shuffle_all_inputs=True)
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data = Data('l', save_generated_data=False, shuffle_all_inputs=True)
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data.generate_data('../../internal_representations/inputs/letters_word_accentuation_train',
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'../../internal_representations/inputs/letters_word_accentuation_test',
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'../../internal_representations/inputs/letters_word_accentuation_validate',
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content_location='../accetuation/data/',
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content_name='SlovarIJS_BESEDE_utf8.lex',
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inputs_location='../accetuation/cnn/internal_representations/inputs/',
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content_shuffle_vector='content_shuffle_vector',
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shuffle_vector='shuffle_vector')
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# combine all data (if it is unwanted comment code below)
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data.x_train = np.concatenate((data.x_train, data.x_test, data.x_validate), axis=0)
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data.x_other_features_train = np.concatenate((data.x_other_features_train, data.x_other_features_test, data.x_other_features_validate), axis=0)
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data.y_train = np.concatenate((data.y_train, data.y_test, data.y_validate), axis=0)
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# build neural network architecture
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nn_output_dim = 10
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batch_size = 16
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actual_epoch = 20
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num_fake_epoch = 20
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conv_input_shape=(23, 36)
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othr_input = (140, )
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conv_input = Input(shape=conv_input_shape, name='conv_input')
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x_conv = Conv1D(115, (3), padding='same', activation='relu')(conv_input)
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x_conv = Conv1D(46, (3), padding='same', activation='relu')(x_conv)
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x_conv = MaxPooling1D(pool_size=2)(x_conv)
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x_conv = Flatten()(x_conv)
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othr_input = Input(shape=othr_input, name='othr_input')
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x = concatenate([x_conv, othr_input])
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x = Dense(256, activation='relu')(x)
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x = Dropout(0.3)(x)
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x = Dense(256, activation='relu')(x)
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x = Dropout(0.3)(x)
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x = Dense(256, activation='relu')(x)
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x = Dropout(0.3)(x)
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x = Dense(nn_output_dim, activation='sigmoid')(x)
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model = Model(inputs=[conv_input, othr_input], outputs=x)
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opt = optimizers.Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
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model.compile(loss='mean_squared_error', optimizer=opt, metrics=[actual_accuracy,])
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# model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
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# start learning
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history = model.fit_generator(data.generator('train', batch_size, content_name='SlovarIJS_BESEDE_utf8.lex', content_location='../accetuation/data/'),
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data.x_train.shape[0]/(batch_size * num_fake_epoch),
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epochs=actual_epoch*num_fake_epoch,
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validation_data=data.generator('test', batch_size),
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validation_steps=data.x_test.shape[0]/(batch_size * num_fake_epoch))
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# save generated data
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name = 'test_data/20_epoch'
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model.save(name + '.h5')
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output = open(name + '_history.pkl', 'wb')
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pickle.dump(history.history, output)
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output.close()
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