def beam_search_generator(sess, net, initial_state, initial_sample,
early_term_token, beam_width, forward_model_fn, forward_args):
'''Run beam search! Yield consensus tokens sequentially, as a generator;
return when reaching early_term_token (newline).
Args:
sess: tensorflow session reference
net: tensorflow net graph (must be compatible with the forward_net function)
initial_state: initial hidden state of the net
initial_sample: single token (excluding any seed/priming material)
to start the generation
early_term_token: stop when the beam reaches consensus on this token
(but do not return this token).
beam_width: how many beams to track
forward_model_fn: function to forward the model, must be of the form:
probability_output, beam_state =
forward_model_fn(sess, net, beam_state, beam_sample, forward_args)
(Note: probability_output has to be a valid probability distribution!)
tot_steps: how many tokens to generate before stopping,
unless already stopped via early_term_token.
Returns: a generator to yield a sequence of beam-sampled tokens.'''
# Store state, outputs and probabilities for up to args.beam_width beams.
# Initialize with just the one starting entry; it will branch to fill the beam
# in the first step.
beam_states = [initial_state] # Stores the best activation states
beam_outputs = [[initial_sample]] # Stores the best generated output sequences so far.
beam_probs = [1.] # Stores the cumulative normalized probabilities of the beams so far.
while True:
# Keep a running list of the best beam branches for next step.
# Don't actually copy any big data structures yet, just keep references
# to existing beam state entries, and then clone them as necessary
# at the end of the generation step.
new_beam_indices = []
new_beam_probs = []
new_beam_samples = []
# Iterate through the beam entries.
for beam_index, beam_state in enumerate(beam_states):
beam_prob = beam_probs[beam_index]
beam_sample = beam_outputs[beam_index][-1]
# Forward the model.
prediction, beam_states[beam_index] = forward_model_fn(
sess, net, beam_state, beam_sample, forward_args)
# Sample best_tokens from the probability distribution.
# Sample from the scaled probability distribution beam_width choices
# (but not more than the number of positive probabilities in scaled_prediction).
count = min(beam_width, sum(1 if p > 0. else 0 for p in prediction))
best_tokens = np.random.choice(len(prediction), size=count,
replace=False, p=prediction)
for token in best_tokens:
prob = prediction[token] * beam_prob
if len(new_beam_indices) < beam_width:
# If we don't have enough new_beam_indices, we automatically qualify.
new_beam_indices.append(beam_index)
new_beam_probs.append(prob)
new_beam_samples.append(token)
else:
# Sample a low-probability beam to possibly replace.
np_new_beam_probs = np.array(new_beam_probs)
inverse_probs = -np_new_beam_probs + max(np_new_beam_probs) + min(np_new_beam_probs)
inverse_probs = inverse_probs / sum(inverse_probs)
sampled_beam_index = np.random.choice(beam_width, p=inverse_probs)
if new_beam_probs[sampled_beam_index] <= prob:
# Replace it.
new_beam_indices[sampled_beam_index] = beam_index
new_beam_probs[sampled_beam_index] = prob
new_beam_samples[sampled_beam_index] = token
# Replace the old states with the new states, first by referencing and then by copying.
already_referenced = [False] * beam_width
new_beam_states = []
new_beam_outputs = []
for i, new_index in enumerate(new_beam_indices):
if already_referenced[new_index]:
new_beam = copy.deepcopy(beam_states[new_index])
else:
new_beam = beam_states[new_index]
already_referenced[new_index] = True
new_beam_states.append(new_beam)
new_beam_outputs.append(beam_outputs[new_index] + [new_beam_samples[i]])
# Normalize the beam probabilities so they don't drop to zero
beam_probs = new_beam_probs / sum(new_beam_probs)
beam_states = new_beam_states
beam_outputs = new_beam_outputs
# Prune the agreed portions of the outputs
# and yield the tokens on which the beam has reached consensus.
l, early_term = consensus_length(beam_outputs, early_term_token)
if l > 0:
for token in beam_outputs[0][:l]: yield token
beam_outputs = [output[l:] for output in beam_outputs]
if early_term: return early_term
如何将early_term的最终结果转换为字符串？
该文件是chatbot.py，取自https://github.com/pender/chatbot-rnn