assert False, 'no activation function'
def cost_func(y, y_hat, batch_size, choice='euclidean'):
if choice == 'euclidean':
return T.sum((y-y_hat)**2) / batch_size
else:
assert False, 'no cost function'
t_start = time.time()
sample_file = 'mfcc/train.ark'
test_file = 'mfcc/test.ark'
label_file = 'label/train.lab'
label_map_file = 'phones/48_39.map'
DataParser.load(sample_file, label_file, label_map_file)
#DataParser.test()
dim_x = DataParser.dimension_x
dim_y_hat = DataParser.dimension_y
batch_size = 21
neuron_num = 64
epoch_cycle = 50
learning_rate = 0.01
lr = theano.shared(learning_rate)
lr_decay = 1.0
# e.g. matrix 3*2 dot matrix 2*1 = matrix 3*1
# [[1., 3.], [2., 2.], [3.,1.]] dot [[2], [1]] = [[5.], [6.], [7.]]
x = T.matrix('input', dtype='float64') # matrix of dim_x * batch_size
y_hat = T.matrix('reference', dtype='float64') # matrix of dim_y_hat * batch_size
