lkrsnik/stress_asignment
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Test ready bidirectional architectural inputs

Browse Source
This commit is contained in:
lkrsnik
2018-03-27 10:35:14 +02:00
parent 9edad0ad07
commit 94ce159d44
3 changed files with 131 additions and 113 deletions
Download Patch File Download Diff File Expand all files Collapse all files
workbench.py
+22 -37
View File
@@ -27,29 +27,16 @@ from prepare_data import *
# 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',
data = Data('l', bidirectional_basic_input=True, bidirectional_architectural_input=True)
data.generate_data('letters_word_accetuation_bidirectional_train',
'letters_word_accetuation_bidirectional_test',
'letters_word_accetuation_bidirectional_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_output_dim = 10
nn_hdim = 516
batch_size = 16
# actual_epoch = 1
@@ -57,32 +44,28 @@ 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_shape=(23, 36)
othr_input = (140, )
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 = Conv1D(115, (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)
conv_input2 = Input(shape=conv_input_shape, name='conv_input2')
x_conv2 = Conv1D(115, (3), padding='same', activation='relu')(conv_input2)
x_conv2 = Conv1D(46, (3), padding='same', activation='relu')(x_conv2)
x_conv2 = MaxPooling1D(pool_size=2)(x_conv2)
x_conv2 = Flatten()(x_conv2)
# x_conv = Dense(516, activation='relu', kernel_constraint=maxnorm(3))(x_conv)
othr_input = Input(shape=othr_input, name='othr_input')
x = concatenate([x_conv, othr_input])
x = concatenate([x_conv, x_conv2, othr_input])
# x = Dense(1024, input_dim=(516 + 256), activation='relu')(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.3)(x)
@@ -95,7 +78,7 @@ x = Dense(nn_output_dim, activation='sigmoid')(x)
model = Model(inputs=[conv_input, othr_input], outputs=x)
model = Model(inputs=[conv_input, conv_input2, 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'])
@@ -104,10 +87,12 @@ model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[actual_accurac
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 = '40_epoch'
name = '20_epoch'
model.save(name + '.h5')
output = open(name + '_history.pkl', 'wb')
pickle.dump(history.history, output)