def test_fprop_faster(self):
activation = "tanh"
seed = 1234
repeat = 1000
layer = LayerLSTM(input_size=DATA['features_size'],
hidden_size=DATA['hidden_size'],
activation=activation)
layer.initialize(initer.UniformInitializer(seed))
layer_fast = LayerLSTMFast(input_size=DATA['features_size'],
hidden_size=DATA['hidden_size'],
activation=activation)
# Wi, Wo, Wf, Wm
layer_fast.W.set_value(
np.concatenate([
layer.Wi.get_value(),
layer.Wo.get_value(),
layer.Wf.get_value(),
layer.Wm.get_value()
],
axis=1))
layer_fast.U.set_value(
np.concatenate([
layer.Ui.get_value(),
layer.Uo.get_value(),
layer.Uf.get_value(),
layer.Um.get_value()
],
axis=1))
input = T.matrix('input')
input.tag.test_value = DATA['batch_one_step']
last_h = sharedX(DATA['state_h'])
last_m = sharedX(DATA['state_m'])
fprop = theano.function([input], layer.fprop(input, last_h, last_m))
fprop_faster = theano.function([input],
layer_fast.fprop(input, last_h, last_m))
fprop_time = measure("h, m = fprop(DATA['batch_one_step'])", repeat)
fprop_faster_time = measure(
"h, m = fprop_faster(DATA['batch_one_step'])", repeat)
print("fprop time: {:.2f} sec.", fprop_time)
print("fprop faster time: {:.2f} sec.", fprop_faster_time)
print("Speedup: {:.2f}x".format(fprop_time / fprop_faster_time))
for i in range(DATA['seq_len']):
h1, m1 = fprop(DATA['batch'][:, i, :])
h2, m2 = fprop_faster(DATA['batch'][:, i, :])
assert_array_equal(h1, h2)
assert_array_equal(m1, m2)
def test_fprop(self):
activation = "tanh"
seed = 1234
repeat = 1000
layer = LayerLSTM(input_size=DATA['features_size'],
hidden_size=DATA['hidden_size'],
activation=activation)
layer.initialize(initer.UniformInitializer(seed))
# input = T.tensor3('input')
input = T.matrix('input')
input.tag.test_value = DATA['batch_one_step']
last_h = sharedX(DATA['state_h'])
last_m = sharedX(DATA['state_m'])
fprop = theano.function([input], layer.fprop_faster(input, last_h, last_m))
fprop_time = measure("h, m = fprop(DATA['batch_one_step'])", repeat)
print("fprop time: {:.2f} sec.", fprop_time)
h, m = fprop(DATA['batch_one_step'])
def test_fprop_faster(self):
activation = "tanh"
seed = 1234
repeat = 1000
layer = LayerLSTM(input_size=DATA['features_size'],
hidden_size=DATA['hidden_size'],
activation=activation)
layer.initialize(initer.UniformInitializer(seed))
layer_fast = LayerLSTMFast(input_size=DATA['features_size'],
hidden_size=DATA['hidden_size'],
activation=activation)
# Wi, Wo, Wf, Wm
layer_fast.W.set_value(np.concatenate([layer.Wi.get_value(), layer.Wo.get_value(), layer.Wf.get_value(), layer.Wm.get_value()], axis=1))
layer_fast.U.set_value(np.concatenate([layer.Ui.get_value(), layer.Uo.get_value(), layer.Uf.get_value(), layer.Um.get_value()], axis=1))
input = T.matrix('input')
input.tag.test_value = DATA['batch_one_step']
last_h = sharedX(DATA['state_h'])
last_m = sharedX(DATA['state_m'])
fprop = theano.function([input], layer.fprop(input, last_h, last_m))
fprop_faster = theano.function([input], layer_fast.fprop(input, last_h, last_m))
fprop_time = measure("h, m = fprop(DATA['batch_one_step'])", repeat)
fprop_faster_time = measure("h, m = fprop_faster(DATA['batch_one_step'])", repeat)
print("fprop time: {:.2f} sec.", fprop_time)
print("fprop faster time: {:.2f} sec.", fprop_faster_time)
print("Speedup: {:.2f}x".format(fprop_time/fprop_faster_time))
for i in range(DATA['seq_len']):
h1, m1 = fprop(DATA['batch'][:, i, :])
h2, m2 = fprop_faster(DATA['batch'][:, i, :])
assert_array_equal(h1, h2)
assert_array_equal(m1, m2)
def test_fprop(self):
activation = "tanh"
seed = 1234
repeat = 1000
layer = LayerLSTM(input_size=DATA['features_size'],
hidden_size=DATA['hidden_size'],
activation=activation)
layer.initialize(initer.UniformInitializer(seed))
# input = T.tensor3('input')
input = T.matrix('input')
input.tag.test_value = DATA['batch_one_step']
last_h = sharedX(DATA['state_h'])
last_m = sharedX(DATA['state_m'])
fprop = theano.function([input],
layer.fprop_faster(input, last_h, last_m))
fprop_time = measure("h, m = fprop(DATA['batch_one_step'])", repeat)
print("fprop time: {:.2f} sec.", fprop_time)
h, m = fprop(DATA['batch_one_step'])
