def test_fast_bw():
print("Make op...")
from NativeOp import FastBaumWelchOp
op = FastBaumWelchOp().make_op() # (am_scores, edges, weights, start_end_states, float_idx, state_buffer)
print("Op:", op)
n_batch = 3
seq_len = 5
n_classes = 5
from Fsa import FastBwFsaShared
fsa = FastBwFsaShared()
fsa.add_inf_loop(state_idx=0, num_emission_labels=n_classes)
fast_bw_fsa = fsa.get_fast_bw_fsa(n_batch=n_batch)
edges = fast_bw_fsa.edges.view("float32")
edges_placeholder = T.fmatrix(name="edges")
weights = fast_bw_fsa.weights
weights_placeholder = T.fvector(name="weights")
start_end_states = fast_bw_fsa.start_end_states.view("float32")
start_end_states_placeholder = T.fmatrix(name="start_end_states")
am_scores = numpy.random.normal(size=(seq_len, n_batch, n_classes)).astype("float32") # in -log space
am_scores_placeholder = T.ftensor3(name="am_scores")
float_idx = numpy.ones((seq_len, n_batch), dtype="float32")
float_idx_placeholder = T.fmatrix(name="float_idx")
last_state_idx = numpy.max(fast_bw_fsa.start_end_states[1]) # see get_automata_for_batch
state_buffer = numpy.zeros((2, last_state_idx + 1), dtype="float32")
state_buffer_placeholder = T.fmatrix(name="state_buffer")
print("Construct call...")
fwdbwd, obs_scores = op(
am_scores_placeholder, edges_placeholder, weights_placeholder, start_end_states_placeholder, float_idx_placeholder, state_buffer_placeholder)
f = theano.function(inputs=[am_scores_placeholder, edges_placeholder, weights_placeholder, start_end_states_placeholder, float_idx_placeholder, state_buffer_placeholder], outputs=[fwdbwd, obs_scores])
print("Done.")
print("Eval:")
_, score = f(am_scores, edges, weights, start_end_states, float_idx, state_buffer)
print("score:", score)
def test_FastBaumWelch():
print("Make op...")
op = make_fast_baum_welch_op(compiler_opts=dict(
verbose=True)) # will be cached, used inside :func:`fast_baum_welch`
print("Op:", op)
n_batch = 3
seq_len = 5
n_classes = 10
from Fsa import FastBwFsaShared
fsa = FastBwFsaShared()
fsa.add_inf_loop(state_idx=0, num_emission_labels=n_classes)
fast_bw_fsa = fsa.get_fast_bw_fsa(n_batch=n_batch)
edges = tf.constant(fast_bw_fsa.edges, dtype=tf.int32)
weights = tf.constant(fast_bw_fsa.weights, dtype=tf.float32)
start_end_states = tf.constant(fast_bw_fsa.start_end_states,
dtype=tf.int32)
am_scores = tf.constant(numpy.random.normal(size=(seq_len, n_batch,
n_classes)),
dtype=tf.float32) # in -log space
float_idx = tf.ones((seq_len, n_batch), dtype=tf.float32)
print("Construct call...")
fwdbwd, obs_scores = fast_baum_welch(am_scores=am_scores,
float_idx=float_idx,
edges=edges,
weights=weights,
start_end_states=start_end_states)
print("Done.")
print("Eval:")
_, score = session.run([fwdbwd, obs_scores])
print("score:", score)