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',
