def train(num_episodes=1000,
save_every=100,
checkpoint_dir="checkpoints",
tensorboard_dir="tensorboard",
tboard_every=10,
find_target_prop=0):
pol = Policy()
writer = tf.contrib.summary.create_file_writer(tensorboard_dir)
for j in range(1, num_episodes + 1):
random_secret = random.randint(0, config.max_guesses - 1)
e = Episode(pol, random_secret, find_target_prop, True)
history = e.generate()
print("Episode:{}, length: {}".format(j, len(history)))
G = -1
optimizer = \
tf.train.GradientDescentOptimizer(
learning_rate=config.reinforce_alpha*G)
for i in reversed(range(1, len(history))):
history_so_far = history[:i]
next_action, _ = history[i]
with tfe.GradientTape() as tape:
action_logits = pol(history_so_far, with_softmax=False)
loss = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=tf.one_hot(tf.convert_to_tensor([next_action]),
config.max_guesses),
logits=action_logits)
grads = tape.gradient(loss, pol.variables)
optimizer.apply_gradients(zip(grads, pol.variables))
G -= 1
optimizer._learning_rate = G * config.reinforce_alpha
optimizer._learning_rate_tensor = None
# hack. Should be able to pass a callable as learning_rate, see
# https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer#args
# can I perhaps submit a PR to fix this bug?
sys.stdout.write("{}/{}\r".format(len(history) - i, len(history)))
if j % save_every == 0 or j == num_episodes:
saver = tfe.Saver(pol.named_variables)
save_path = os.path.join(
checkpoint_dir,
"episode{}".format(str(j).zfill(len(str(num_episodes)))))
saver.save(save_path)
if j % tboard_every == 0:
with writer.as_default():
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar('total_return',
tf.convert_to_tensor([G]),
step=j)
return pol
state = self.lstm.zero_state(1, tf.float32)
for guess, feedback in game_state:
guess_tensor = tf.reshape(tf.convert_to_tensor(guess), (1,))
feedback_tensor = tf.reshape(tf.convert_to_tensor(feedback), (1,))
guess_embedded = self.guess_embedding(guess_tensor)
feedback_embedded = self.feedback_embedding(feedback_tensor)
combined_embedded = tf.concat([guess_embedded,
feedback_embedded],
axis=-1)
# can I do multiple inputs to the LSTM instead of concatenating?
output, state = self.lstm(combined_embedded, state)
logits = self.dense(output)
if with_softmax:
return tf.nn.softmax(logits)
return logits
if __name__ == "__main__":
from episode import Episode
import numpy as np
np.random.seed(123)
p = Policy()
e = Episode(p, "0000")
x = p(e.generate())
print(x.numpy())
for guess, feedback in game_state:
guess_tensor = tf.reshape(tf.convert_to_tensor(guess), (1, ))
feedback_tensor = tf.reshape(tf.convert_to_tensor(feedback), (1, ))
guess_embedded = self.guess_embedding(guess_tensor)
feedback_embedded = self.feedback_embedding(feedback_tensor)
combined_embedded = tf.concat([guess_embedded, feedback_embedded],
axis=-1)
# can I do multiple inputs to the LSTM instead of concatenating?
output, state = self.lstm(combined_embedded, state)
logits = self.dense(output)
if with_softmax:
return tf.nn.softmax(logits)
return logits
if __name__ == "__main__":
from episode import Episode
import numpy as np
np.random.seed(123)
p = Policy()
print("lstm")
print(p.lstm)
e = Episode(p, "0000", find_target_proba=0.3, train=True)
g = e.generate()
print(g)
x = p(g)
print(x)