l1_conv_filter_type=[
[layer1, l1_conv_filter_type[0], -1, 'valid', (k[0], 1), 0.5],
[layer1, l1_conv_filter_type[1], -1, 'valid', (k[0], 1), 0.],
[layer1, l1_conv_filter_type[2], -1, 'valid', (k[0], 1), 0.],
],
l2_conv_filter_type=[
[layer2, l2_conv_filter_type[0], -1, 'valid', (k[1], 1), 0.5]
],
full_connected_layer_units=[hidden1, hidden2],
output_dropout_rate=0.2,
nb_epoch=nb_epoch,
earlyStoping_patience=50,
optimizers='sgd',
batch_size=32,
lr=1e-2,
)
bow_cnn.print_model_descibe()
print(bow_cnn.fit(
(train_X_feature, train_y),
(test_X_feature, test_y)))
y_pred, is_correct, accu, f1, test_loss = bow_cnn.accuracy((test_X_feature, test_y))
result_file_path = 'result/single_bow3_%s_%d.csv' % (feature_type, rand_seed)
data_util.save_result(test_data, predict=y_pred, is_correct=is_correct, path=result_file_path)
end_time = timeit.default_timer()
print('end! Running time:%ds!' % (end_time - start_time))
logging.debug('=' * 20)
logging.debug('end! Running time:%ds!' % (end_time - start_time))
[layer1, l1_conv_filter_type[2], -1, 'valid', (k[0], 1), 0.],
],
l2_conv_filter_type=[
[layer2, l2_conv_filter_type[0], -1, 'valid', (k[1], 1), 0.5]
],
full_connected_layer_units=[hidden1, hidden2],
output_dropout_rate=0.2,
nb_epoch=nb_epoch,
earlyStoping_patience=50,
optimizers='sgd',
batch_size=32,
lr=1e-2,
)
dev_loss, dev_accuracy, \
val_loss, val_accuracy = bow_cnn.fit((dev_X, dev_y),(val_X, val_y))
print('dev:%f,%f'%(dev_loss, dev_accuracy))
print('val:%f,%f'%(val_loss, val_accuracy))
fout.write('dev:%f,%f\n'%(dev_loss, dev_accuracy))
fout.write('val:%f,%f\n'%(val_loss, val_accuracy))
# y_pred, is_correct, accu, f1, test_loss = bow_cnn.accuracy((val_X, val_y))
ave_acc.append(val_accuracy)
print(ave_acc)
print(np.average(ave_acc))
print('-' * 80)
fout.write('五折验证结果：%s\n'%ave_acc)
fout.write('平均：%f\n'%np.average(ave_acc))
fout.write('-' * 80 + '\n')
bow_cnn = SingleChannelBowCNN(
