100 lines
4.3 KiB
Python
100 lines
4.3 KiB
Python
# -*- 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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from keras import optimizers
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from keras.models import Model
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from keras.layers import Dense, Dropout, Input
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from keras.layers.merge import concatenate
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from keras.layers.convolutional import Conv1D
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from keras.layers.convolutional import MaxPooling1D
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from keras.layers import Flatten
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# from keras import backend as Input
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np.random.seed(7)
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# get_ipython().magic('run ../../../prepare_data.py')
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# import sys
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# # sys.path.insert(0, '../../../')
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# sys.path.insert(0, '/home/luka/Developement/accetuation/')
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from prepare_data import *
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# X_train, X_other_features_train, y_train, X_validate, X_other_features_validate, y_validate = generate_full_matrix_inputs()
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# save_inputs('../../internal_representations/inputs/shuffeled_matrix_train_inputs_other_features_output_11.h5', X_train, y_train, other_features = X_other_features_train)
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# save_inputs('../../internal_representations/inputs/shuffeled_matrix_validate_inputs_other_features_output_11.h5', X_validate, y_validate, other_features = X_other_features_validate)
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# 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)
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# 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)
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data = Data('l', bidirectional_basic_input=True, bidirectional_architectural_input=True)
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data.generate_data('letters_word_accetuation_bidirectional_train',
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'letters_word_accetuation_bidirectional_test',
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'letters_word_accetuation_bidirectional_validate', content_name='SlovarIJS_BESEDE_utf8.lex',
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content_shuffle_vector='content_shuffle_vector', shuffle_vector='shuffle_vector',
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inputs_location='cnn/internal_representations/inputs/', content_location='data/')
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num_examples = len(data.x_train) # training set size
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nn_output_dim = 10
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nn_hdim = 516
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batch_size = 16
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# actual_epoch = 1
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actual_epoch = 20
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# num_fake_epoch = 2
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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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conv_input2 = Input(shape=conv_input_shape, name='conv_input2')
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x_conv2 = Conv1D(115, (3), padding='same', activation='relu')(conv_input2)
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x_conv2 = Conv1D(46, (3), padding='same', activation='relu')(x_conv2)
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x_conv2 = MaxPooling1D(pool_size=2)(x_conv2)
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x_conv2 = Flatten()(x_conv2)
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# x_conv = Dense(516, activation='relu', kernel_constraint=maxnorm(3))(x_conv)
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othr_input = Input(shape=othr_input, name='othr_input')
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x = concatenate([x_conv, x_conv2, othr_input])
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# x = Dense(1024, input_dim=(516 + 256), activation='relu')(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(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, conv_input2, othr_input], outputs=x)
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opt = optimizers.Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
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model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[actual_accuracy,])
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# model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
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history = model.fit_generator(data.generator('train', batch_size, content_name='SlovarIJS_BESEDE_utf8.lex', content_location='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, content_name='SlovarIJS_BESEDE_utf8.lex', content_location='data/'),
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validation_steps=data.x_test.shape[0]/(batch_size * num_fake_epoch),
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verbose=2
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)
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name = '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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