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Added num of letters to x_other_features

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lkrsnik
2017-08-18 19:08:42 +02:00
parent 18348b78fc
commit a2fce7c1ae
7 changed files with 1430 additions and 126 deletions
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workbench.py
+64 -44
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@@ -1,26 +1,17 @@
# coding: utf-8
# In[1]:
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
# text in Western (Windows 1252)
import pickle
import numpy as np
# import StringIO
import math
from keras.models import Sequential
from keras.layers import Dense, Dropout, Merge
from keras import optimizers
from keras.models import Model
from keras.layers import Dense, Dropout, Input
from keras.layers.merge import concatenate
from keras import regularizers
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.constraints import maxnorm
from keras.layers import Flatten
from keras.optimizers import SGD
from keras.models import load_model
# from keras import backend as Input
np.random.seed(7)
# get_ipython().magic('run ../../../prepare_data.py')
@@ -34,39 +25,68 @@ 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)
# 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)
data = Data('l', save_generated_data=False, number_of_syllables=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='')
num_examples = len(X_train) # training set size
nn_output_dim = 11
num_examples = len(data.x_train) # training set size
nn_output_dim = 10
nn_hdim = 516
word_processor = Sequential()
word_processor.add(Conv1D(43, (3), input_shape=(23, 43), padding='same', activation='relu'))
word_processor.add(Conv1D(43, (3), padding='same', activation='relu'))
word_processor.add(MaxPooling1D(pool_size=2))
word_processor.add(Flatten())
word_processor.add(Dense(516, activation='relu', kernel_constraint=maxnorm(3)))
metadata_processor = Sequential()
metadata_processor.add(Dense(256, input_dim=167, activation='relu'))
model = Sequential()
model.add(Merge([word_processor, metadata_processor], mode='concat')) # Merge is your sensor fusion buddy
model.add(Dense(1024, input_dim=(516 + 256), activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(1024, input_dim=(516 + 256), activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(nn_output_dim, activation='sigmoid'))
batch_size = 16
# actual_epoch = 1
actual_epoch = 60
# num_fake_epoch = 2
num_fake_epoch = 20
# In[10]:
# epochs = 5
# lrate = 0.1
# decay = lrate/epochs
# sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
model.fit([X_train, X_other_features_train], y_train, validation_data=([X_validate, X_other_features_validate], y_validate), epochs=10, batch_size=10)
model.save('v1_1.h5')
conv_input_shape=(23, 36)
othr_input = (141, )
conv_input = Input(shape=conv_input_shape, name='conv_input')
x_conv = Conv1D(133, (3), padding='same', activation='relu')(conv_input)
x_conv = Conv1D(46, (3), padding='same', activation='relu')(x_conv)
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(0.2)(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,
validation_data=data.generator('test', batch_size, content_name='SlovarIJS_BESEDE_utf8.lex', content_location=''),
validation_steps=data.x_test.shape[0]/(batch_size * num_fake_epoch),
verbose=2
)
name = '60_epoch'
model.save(name + '.h5')
output = open(name + '_history.pkl', 'wb')
pickle.dump(history.history, output)
output.close()