def update_video(start_date, end_date, n_clicks_timestamp):
logger.info(f"n_clicks_timestamp={n_clicks_timestamp}")
latest_file = max(Path("history_data/").glob("dxy_minutes*.json"),
key=lambda p: p.stat().st_ctime)
with open(
latest_file,
"r",
encoding="utf8",
) as f:
history = json.load(f)
if (start_date and end_date and n_clicks_timestamp
and (n_clicks_timestamp != -1) and
(datetime.now() -
utils.timestamp2datetime(n_clicks_timestamp / 1000)).seconds < 1):
logger.info("[开始] 更新视频")
fps = 30
dpi = 300
figdir = "assets/figures"
Path(figdir).mkdir(exist_ok=True, parents=True)
utils.rmfigures(figdir)
logger.info("[开始] 生成图片")
utils.generate_figures(history, provinces_geomap, provinces_list,
start_date, end_date, dpi, figdir)
logger.info("[结束] 生成图片")
videoname = f"assets/tncg-{start_date.replace('-', '')}-{end_date.replace('-', '')}-{datetime.now().strftime('%Y%m%d%H%M%S')}.mp4"
logger.info("[开始] 生成视频")
utils.generate_video(f"{figdir}/%d.png", videoname, 30)
logger.info("[结束] 生成视频")
logger.info("[结束] 更新视频")
src = f"/{videoname}"
logger.debug(f"src={src}")
return src
num_words=args.vocab_size,
maxlen=args.maxLen)
x_train = sequence.pad_sequences(x_train, maxlen=args.maxLen)
x_test = sequence.pad_sequences(x_test, maxlen=args.maxLen)
model = cnn(vocab_size=args.vocab_size,
maxLen=args.maxLen,
kernel_size=args.kernel_size,
embedding_dim=args.embed,
hidden_dim=args.hidden,
output_dim=args.output,
keep_prob=args.keep)
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss='binary_crossentropy',
metrics=['accuracy'])
print(model.summary())
history = model.fit(x_train,
y_train,
validation_split=args.val_split,
batch_size=args.batch,
epochs=args.epochs,
callbacks=[EarlyStopping(monitor='val_loss', patience=10)])
y_pred = model.predict(x_test)
generate_figures(history=history,
model_name=args.model_name,
output_dir="figures")
output_performance(model=model, y_test=y_test, y_pred=y_pred)
q_vals = session.run(target_dqn.q_values,
feed_dict={target_dqn.input: new_states})
double_q = q_vals[range(batch_size), arg_q_max]
# Bellman equation. Multiplication with (1-terminal_flags) makes sure that
# if the game is over, targetQ=rewards
target_q = rewards + (gamma * double_q * (1 - terminal_flags))
# Gradient descend step to update the parameters of the main network
loss, _ = session.run(
[main_dqn.loss, main_dqn.update],
feed_dict={
main_dqn.input: states,
main_dqn.target_q: target_q,
main_dqn.action: actions
})
return loss
args = utils.argsparser()
tf.random.set_random_seed(args.seed)
np.random.seed(args.seed)
tf.reset_default_graph()
# Control parameters
if args.task == "train":
utils.train(args, DQN, learn, "dist_dqn")
elif args.task == "evaluate":
utils.sample(args, DQN, "dist_dqn", save=False)
elif args.task == "log":
utils.generate_figures("dist_dqn")
else:
utils.sample(args, DQN, "dist_dqn")
expert_loss, _ = session.run(
[main_dqn.expert_loss, main_dqn.expert_update],
feed_dict={
main_dqn.input: expert_states,
main_dqn.generated_input: generated_states,
main_dqn.expert_action: expert_actions,
main_dqn.expert_weights: weights
})
return loss, expert_loss
args = utils.argsparser()
tf.random.set_random_seed(args.seed)
np.random.seed(args.seed)
tf.reset_default_graph()
# Control parameters
if args.task == "train":
utils.train(args,
DQN,
learn,
"expert_dist_dqn",
expert=True,
bc_training=train_bc,
pretrain_iters=args.pretrain_bc_iter)
elif args.task == "evaluate":
utils.sample(args, DQN, "expert_dist_dqn", save=False)
elif args.task == "log":
utils.generate_figures("expert_dist_dqn")
else:
utils.sample(args, DQN, "expert_dist_dqn")
expert_q_vals = session.run(target_dqn.q_values, feed_dict={target_dqn.input:obs})
double_q = q_vals[range(batch_size), arg_q_max]
expert_q = expert_q_vals[range(batch_size),expert_arg_q_max]
# Bellman equation. Multiplication with (1-terminal_flags) makes sure that
# if the game is over, targetQ=rewards
target_q = rewards + (gamma*double_q * (1-terminal_flags))
# Gradient descend step to update the parameters of the main network
loss, _ = session.run([main_dqn.loss, main_dqn.update],
feed_dict={main_dqn.input:states,
main_dqn.target_q:target_q,
main_dqn.action:actions})
expert_loss, _ = session.run([main_dqn.expert_loss,main_dqn.expert_update],
feed_dict={main_dqn.input:obs,
main_dqn.expert_action:acs,
main_dqn.target_q:expert_q})
return loss,expert_loss
args = utils.argsparser()
tf.random.set_random_seed(args.seed)
np.random.seed(args.seed)
tf.reset_default_graph()
# Control parameters
if args.task == "train":
utils.train(args, DQN, learn, "basic_fengdi_dist_expert_dqn", expert=True)
elif args.task == "evaluate":
utils.sample(args, DQN, "basic_fengdi_dist_expert_dqn", save=False)
elif args.task == "log":
utils.generate_figures("basic_fengdi_dist_expert_dqn")
else:
utils.sample(args, DQN, "basic_fengdi_dist_expert_dqn")