TensorFlow中.crf_log_likelihood（）怎麼用，越具體越好？

# Data settings.
num_examples = 10
num_words = 20
num_features = 100
num_tags = 5

# Random features.
x = np.random.rand(num_examples, num_words, num_features).astype(np.float32)

# Random tag indices representing the gold sequence.
y = np.random.randint(num_tags, size=[num_examples, num_words]).astype(np.int32)

# All sequences in this example have the same length, but they can be variable in a real model.
sequence_lengths = np.full(num_examples, num_words - 1, dtype=np.int32)

# Train and evaluate the model.
with tf.Graph().as_default():
with tf.Session() as session:
# Add the data to the TensorFlow graph.
x_t = tf.constant(x)
y_t = tf.constant(y)
sequence_lengths_t = tf.constant(sequence_lengths)

# Compute unary scores from a linear layer.
weights = tf.get_variable("weights", [num_features, num_tags])
matricized_x_t = tf.reshape(x_t, [-1, num_features])
matricized_unary_scores = tf.matmul(matricized_x_t, weights)
unary_scores = tf.reshape(matricized_unary_scores,
[num_examples, num_words, num_tags])

# Compute the log-likelihood of the gold sequences and keep the transition
# params for inference at test time.
log_likelihood, transition_params = tf.contrib.crf.crf_log_likelihood(
unary_scores, y_t, sequence_lengths_t)

# Add a training op to tune the parameters.
loss = tf.reduce_mean(-log_likelihood)
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

# Train for a fixed number of iterations.
session.run(tf.global_variables_initializer())
for i in range(1000):
tf_unary_scores, tf_transition_params, _ = session.run(
[unary_scores, transition_params, train_op])
if i % 100 == 0:
correct_labels = 0
total_labels = 0
for tf_unary_scores_, y_, sequence_length_ in zip(tf_unary_scores, y,
sequence_lengths):
# Remove padding from the scores and tag sequence.
tf_unary_scores_ = tf_unary_scores_[:sequence_length_]
y_ = y_[:sequence_length_]

# Compute the highest scoring sequence.
viterbi_sequence, _ = tf.contrib.crf.viterbi_decode(
tf_unary_scores_, tf_transition_params)