Load data ------------------------------- ''' #Source reference: https://github.com/aymericdamien/TensorFlow-Examples.git/input_data.py def dense_to_one_hot(labels_dense, num_classes=10): """Convert class labels from scalars to one-hot vectors.""" num_labels = labels_dense.shape[0] index_offset = np.arange(num_labels) * num_classes labels_one_hot = np.zeros((num_labels, num_classes)) labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 return labels_one_hot # Load data data = mdl_data.YLIMED('YLIMED_info.csv', FILEPATH + '/YLIMED150924/audio/mfcc20', FILEPATH + '/YLIMED150924/keyframe/fc7') X_img_train = data.get_img_X_train() X_aud_train = data.get_aud_X_train() y_train = data.get_y_train() Y_train = dense_to_one_hot(y_train) # Shuffle initial data p = np.random.permutation(len(Y_train)) X_img_train = X_img_train[p] X_aud_train = X_aud_train[p] Y_train = Y_train[p] # Load test data X_img_test = data.get_img_X_test() X_aud_test = data.get_aud_X_test()
from keras.utils import np_utils from keras.utils.np_utils import accuracy from keras.layers.core import Dense, Dropout, Activation from keras.layers.embeddings import Embedding from keras.layers.recurrent import LSTM #from keras.datasets import imdb #from keras.optimizers import RMSprop import mdl_data import numpy as np np.random.seed(1337) # for reproducibility data = mdl_data.YLIMED('YLIMED_info.csv', '/DATA/YLIMED150924/audio/mfcc20', '/DATA/YLIMED150924/keyframe/fc7') X_img_train = data.get_img_X_train() X_aud_train = data.get_aud_X_train() y_train = data.get_y_train() Y_train = np_utils.to_categorical(y_train, 10) model = Graph() maxlen = len(X_img_train[0]) model.add_input(name='img_input', input_shape=(maxlen, )) model.add_node(Dense(1000, activation='relu'), name='img_dense1', input='img_input') model.add_node(Dropout(0.5), name='img_dropout1', input='img_dense1') model.add_node(Dense(600, activation='relu'), name='img_dense2',