Keras symbolic inputs/outputs do not implement `__len__` 错误
Keras symbolic inputs/outputs do not implement `__len__` error
我想让 AI 播放我的自定义环境，不幸的是，当我 运行 我的代码时，出现以下错误：
File "C:\Program Files\JetBrains\PyCharm Community Edition 2021.2\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm Community Edition 2021.2\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "D:/PycharmProjects/Custom Enviroment AI/Enviroment.py", line 88, in <module>
DQN = buildAgent(model, actions)
File "D:/PycharmProjects/Custom Enviroment AI/Enviroment.py", line 82, in buildAgent
dqn = DQNAgent(model, memory=memory, policy=policy, nb_actions=actions, nb_steps_warmup=10,
File "D:\PycharmProjects\Custom Enviroment AI\venv\lib\site-packages\rl\agents\dqn.py", line 108, in __init__
if hasattr(model.output, '__len__') and len(model.output) > 1:
File "D:\PycharmProjects\Custom Enviroment AI\venv\lib\site-packages\keras\engine\keras_tensor.py", line 221, in __len__
raise TypeError('Keras symbolic inputs/outputs do not '
TypeError: Keras symbolic inputs/outputs do not implement `__len__`. You may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model. This error will also get raised if you try asserting a symbolic input/output directly.
错误说你不应该使用 len() 而你应该使用 .shape istead，不幸的是这似乎是 tensorflow 中的一个错误
我的完整代码是：
from rl.memory import SequentialMemory
from rl.policy import BoltzmannQPolicy
from rl.agents.dqn import DQNAgent
from keras.layers import Dense
import tensorflow as tf
import numpy as np
import random
import pygame
import gym
class Env(gym.Env):
def __init__(self):
self.action_space = gym.spaces.Discrete(4)
self.observation_space = gym.spaces.MultiDiscrete([39, 27])
self.screen = pygame.display.set_mode((800, 600))
self.PlayerX = 0
self.PlayerY = 0
self.FoodX = 0
self.FoodY = 0
self.state = [self.FoodX - self.PlayerX + 19, self.FoodY - self.PlayerY + 14]
self.timeLimit = 1000
def render(self, mode="human"):
self.screen.fill((0, 0, 0))
pygame.draw.rect(self.screen, (255, 255, 255), pygame.Rect(self.PlayerX * 40, self.PlayerY * 40, 40, 40))
pygame.draw.rect(self.screen, (255, 0, 0), pygame.Rect(self.FoodX * 40, self.FoodY * 40, 40, 40))
pygame.display.update()
def reset(self):
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
self.PlayerX = 0
self.PlayerY = 0
self.timeLimit = 1000
return self.state
def step(self, action):
self.timeLimit -= 1
reward = -1
if action == 0 and self.PlayerY > 0:
self.PlayerY -= 1
if action == 1 and self.PlayerX > 0:
self.PlayerX -= 1
if action == 2 and self.PlayerY < 14:
self.PlayerY += 1
if action == 3 and self.PlayerX < 19:
self.PlayerX += 1
if self.PlayerX == self.FoodX and self.PlayerY == self.FoodY:
reward += 30
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
if self.timeLimit <= 0:
done = True
else:
done = False
self.state = [self.FoodX - self.PlayerX, self.FoodY - self.PlayerY]
return self.state, reward, done
env = Env()
states = env.observation_space.shape
actions = env.action_space.n
def build_model(states, actions):
model = tf.keras.Sequential()
model.add(Dense(2, activation='relu', input_shape=states))
model.add(Dense(4, activation='relu'))
model.add(Dense(actions, activation='linear'))
return model
def buildAgent(model, actions):
policy = BoltzmannQPolicy()
memory = SequentialMemory(limit=50000, window_length=1)
dqn = DQNAgent(model, memory=memory, policy=policy, nb_actions=actions, nb_steps_warmup=10,
target_model_update=1e-2)
return dqn
model = build_model(states, actions)
DQN = buildAgent(model, actions)
DQN.compile(tf.keras.optimizers.Adam(learning_rate=1e-3), metrics=['mae'])
DQN.fit(env, nb_steps=50000, visualize=False, verbose=1)
scores = DQN.test(env, nb_episodes=100, visualize=True)
print(np.mean(scores.history['episode_reward']))
pygame.quit()
model.save('model.h5')
我使用 Tensorflow：2.8.0。这似乎是 Tensorflow 代码中的一个错误，但我不知道该怎么做
如前所述here，您需要安装更新版本的keras-rl：
!pip install keras-rl2
您还需要为输入形状添加一个额外的维度，并在末尾添加一个 Flatten 层，因为 Keras 在使用 DQN 代理时需要这样做：
def build_model(states, actions):
model = tf.keras.Sequential()
model.add(Dense(2, activation='relu', input_shape=(1, states[0])))
model.add(Dense(4, activation='relu'))
model.add(Dense(actions, activation='linear'))
model.add(Flatten())
return model
最后，您自定义环境中的 step 方法还必须 return 一个 info 字典（我刚刚创建了一个空字典）：
def step(self, action):
self.timeLimit -= 1
reward = -1
if action == 0 and self.PlayerY > 0:
self.PlayerY -= 1
if action == 1 and self.PlayerX > 0:
self.PlayerX -= 1
if action == 2 and self.PlayerY < 14:
self.PlayerY += 1
if action == 3 and self.PlayerX < 19:
self.PlayerX += 1
if self.PlayerX == self.FoodX and self.PlayerY == self.FoodY:
reward += 30
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
if self.timeLimit <= 0:
done = True
else:
done = False
self.state = [self.FoodX - self.PlayerX, self.FoodY - self.PlayerY]
return self.state, reward, done, {}
如果您进行这些更改，它应该可以正常工作。这是完整的工作代码：
from rl.memory import SequentialMemory
from rl.policy import BoltzmannQPolicy
from rl.agents.dqn import DQNAgent
from keras.layers import Dense, Flatten
import tensorflow as tf
import numpy as np
import random
import pygame
import gym
class Env(gym.Env):
def __init__(self):
self.action_space = gym.spaces.Discrete(4)
self.observation_space = gym.spaces.MultiDiscrete([39, 27])
self.screen = pygame.display.set_mode((800, 600))
self.PlayerX = 0
self.PlayerY = 0
self.FoodX = 0
self.FoodY = 0
self.state = [self.FoodX - self.PlayerX + 19, self.FoodY - self.PlayerY + 14]
self.timeLimit = 1000
def render(self, mode="human"):
self.screen.fill((0, 0, 0))
pygame.draw.rect(self.screen, (255, 255, 255), pygame.Rect(self.PlayerX * 40, self.PlayerY * 40, 40, 40))
pygame.draw.rect(self.screen, (255, 0, 0), pygame.Rect(self.FoodX * 40, self.FoodY * 40, 40, 40))
pygame.display.update()
def reset(self):
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
self.PlayerX = 0
self.PlayerY = 0
self.timeLimit = 1000
return self.state
def step(self, action):
self.timeLimit -= 1
reward = -1
if action == 0 and self.PlayerY > 0:
self.PlayerY -= 1
if action == 1 and self.PlayerX > 0:
self.PlayerX -= 1
if action == 2 and self.PlayerY < 14:
self.PlayerY += 1
if action == 3 and self.PlayerX < 19:
self.PlayerX += 1
if self.PlayerX == self.FoodX and self.PlayerY == self.FoodY:
reward += 30
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
if self.timeLimit <= 0:
done = True
else:
done = False
self.state = [self.FoodX - self.PlayerX, self.FoodY - self.PlayerY]
return self.state, reward, done, {}
env = Env()
states = env.observation_space.shape
actions = env.action_space.n
def build_model(states, actions):
model = tf.keras.Sequential()
model.add(Dense(2, activation='relu', input_shape=(1, states[0])))
model.add(Dense(4, activation='relu'))
model.add(Dense(actions, activation='linear'))
model.add(Flatten())
return model
def buildAgent(model, actions):
policy = BoltzmannQPolicy()
memory = SequentialMemory(limit=50000, window_length=1)
dqn = DQNAgent(model, memory=memory, policy=policy, nb_actions=actions, nb_steps_warmup=10,
target_model_update=1e-2)
return dqn
model = build_model(states, actions)
DQN = buildAgent(model, actions)
DQN.compile(tf.keras.optimizers.Adam(learning_rate=1e-3), metrics=['mae'])
DQN.fit(env, nb_steps=50000, visualize=False, verbose=1)
scores = DQN.test(env, nb_episodes=100, visualize=True)
print(np.mean(scores.history['episode_reward']))
pygame.quit()
model.save('model.h5')
有关详细信息，请参阅 docs。
我想让 AI 播放我的自定义环境，不幸的是，当我 运行 我的代码时，出现以下错误：
File "C:\Program Files\JetBrains\PyCharm Community Edition 2021.2\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm Community Edition 2021.2\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "D:/PycharmProjects/Custom Enviroment AI/Enviroment.py", line 88, in <module>
DQN = buildAgent(model, actions)
File "D:/PycharmProjects/Custom Enviroment AI/Enviroment.py", line 82, in buildAgent
dqn = DQNAgent(model, memory=memory, policy=policy, nb_actions=actions, nb_steps_warmup=10,
File "D:\PycharmProjects\Custom Enviroment AI\venv\lib\site-packages\rl\agents\dqn.py", line 108, in __init__
if hasattr(model.output, '__len__') and len(model.output) > 1:
File "D:\PycharmProjects\Custom Enviroment AI\venv\lib\site-packages\keras\engine\keras_tensor.py", line 221, in __len__
raise TypeError('Keras symbolic inputs/outputs do not '
TypeError: Keras symbolic inputs/outputs do not implement `__len__`. You may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model. This error will also get raised if you try asserting a symbolic input/output directly.
错误说你不应该使用 len() 而你应该使用 .shape istead，不幸的是这似乎是 tensorflow 中的一个错误 我的完整代码是：
from rl.memory import SequentialMemory
from rl.policy import BoltzmannQPolicy
from rl.agents.dqn import DQNAgent
from keras.layers import Dense
import tensorflow as tf
import numpy as np
import random
import pygame
import gym
class Env(gym.Env):
def __init__(self):
self.action_space = gym.spaces.Discrete(4)
self.observation_space = gym.spaces.MultiDiscrete([39, 27])
self.screen = pygame.display.set_mode((800, 600))
self.PlayerX = 0
self.PlayerY = 0
self.FoodX = 0
self.FoodY = 0
self.state = [self.FoodX - self.PlayerX + 19, self.FoodY - self.PlayerY + 14]
self.timeLimit = 1000
def render(self, mode="human"):
self.screen.fill((0, 0, 0))
pygame.draw.rect(self.screen, (255, 255, 255), pygame.Rect(self.PlayerX * 40, self.PlayerY * 40, 40, 40))
pygame.draw.rect(self.screen, (255, 0, 0), pygame.Rect(self.FoodX * 40, self.FoodY * 40, 40, 40))
pygame.display.update()
def reset(self):
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
self.PlayerX = 0
self.PlayerY = 0
self.timeLimit = 1000
return self.state
def step(self, action):
self.timeLimit -= 1
reward = -1
if action == 0 and self.PlayerY > 0:
self.PlayerY -= 1
if action == 1 and self.PlayerX > 0:
self.PlayerX -= 1
if action == 2 and self.PlayerY < 14:
self.PlayerY += 1
if action == 3 and self.PlayerX < 19:
self.PlayerX += 1
if self.PlayerX == self.FoodX and self.PlayerY == self.FoodY:
reward += 30
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
if self.timeLimit <= 0:
done = True
else:
done = False
self.state = [self.FoodX - self.PlayerX, self.FoodY - self.PlayerY]
return self.state, reward, done
env = Env()
states = env.observation_space.shape
actions = env.action_space.n
def build_model(states, actions):
model = tf.keras.Sequential()
model.add(Dense(2, activation='relu', input_shape=states))
model.add(Dense(4, activation='relu'))
model.add(Dense(actions, activation='linear'))
return model
def buildAgent(model, actions):
policy = BoltzmannQPolicy()
memory = SequentialMemory(limit=50000, window_length=1)
dqn = DQNAgent(model, memory=memory, policy=policy, nb_actions=actions, nb_steps_warmup=10,
target_model_update=1e-2)
return dqn
model = build_model(states, actions)
DQN = buildAgent(model, actions)
DQN.compile(tf.keras.optimizers.Adam(learning_rate=1e-3), metrics=['mae'])
DQN.fit(env, nb_steps=50000, visualize=False, verbose=1)
scores = DQN.test(env, nb_episodes=100, visualize=True)
print(np.mean(scores.history['episode_reward']))
pygame.quit()
model.save('model.h5')
我使用 Tensorflow：2.8.0。这似乎是 Tensorflow 代码中的一个错误，但我不知道该怎么做
如前所述here，您需要安装更新版本的keras-rl：
!pip install keras-rl2
您还需要为输入形状添加一个额外的维度，并在末尾添加一个 Flatten 层，因为 Keras 在使用 DQN 代理时需要这样做：
def build_model(states, actions):
model = tf.keras.Sequential()
model.add(Dense(2, activation='relu', input_shape=(1, states[0])))
model.add(Dense(4, activation='relu'))
model.add(Dense(actions, activation='linear'))
model.add(Flatten())
return model
最后，您自定义环境中的 step 方法还必须 return 一个 info 字典（我刚刚创建了一个空字典）：
def step(self, action):
self.timeLimit -= 1
reward = -1
if action == 0 and self.PlayerY > 0:
self.PlayerY -= 1
if action == 1 and self.PlayerX > 0:
self.PlayerX -= 1
if action == 2 and self.PlayerY < 14:
self.PlayerY += 1
if action == 3 and self.PlayerX < 19:
self.PlayerX += 1
if self.PlayerX == self.FoodX and self.PlayerY == self.FoodY:
reward += 30
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
if self.timeLimit <= 0:
done = True
else:
done = False
self.state = [self.FoodX - self.PlayerX, self.FoodY - self.PlayerY]
return self.state, reward, done, {}
如果您进行这些更改，它应该可以正常工作。这是完整的工作代码：
from rl.memory import SequentialMemory
from rl.policy import BoltzmannQPolicy
from rl.agents.dqn import DQNAgent
from keras.layers import Dense, Flatten
import tensorflow as tf
import numpy as np
import random
import pygame
import gym
class Env(gym.Env):
def __init__(self):
self.action_space = gym.spaces.Discrete(4)
self.observation_space = gym.spaces.MultiDiscrete([39, 27])
self.screen = pygame.display.set_mode((800, 600))
self.PlayerX = 0
self.PlayerY = 0
self.FoodX = 0
self.FoodY = 0
self.state = [self.FoodX - self.PlayerX + 19, self.FoodY - self.PlayerY + 14]
self.timeLimit = 1000
def render(self, mode="human"):
self.screen.fill((0, 0, 0))
pygame.draw.rect(self.screen, (255, 255, 255), pygame.Rect(self.PlayerX * 40, self.PlayerY * 40, 40, 40))
pygame.draw.rect(self.screen, (255, 0, 0), pygame.Rect(self.FoodX * 40, self.FoodY * 40, 40, 40))
pygame.display.update()
def reset(self):
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
self.PlayerX = 0
self.PlayerY = 0
self.timeLimit = 1000
return self.state
def step(self, action):
self.timeLimit -= 1
reward = -1
if action == 0 and self.PlayerY > 0:
self.PlayerY -= 1
if action == 1 and self.PlayerX > 0:
self.PlayerX -= 1
if action == 2 and self.PlayerY < 14:
self.PlayerY += 1
if action == 3 and self.PlayerX < 19:
self.PlayerX += 1
if self.PlayerX == self.FoodX and self.PlayerY == self.FoodY:
reward += 30
self.FoodX = random.randint(1, 19)
self.FoodY = random.randint(1, 14)
if self.timeLimit <= 0:
done = True
else:
done = False
self.state = [self.FoodX - self.PlayerX, self.FoodY - self.PlayerY]
return self.state, reward, done, {}
env = Env()
states = env.observation_space.shape
actions = env.action_space.n
def build_model(states, actions):
model = tf.keras.Sequential()
model.add(Dense(2, activation='relu', input_shape=(1, states[0])))
model.add(Dense(4, activation='relu'))
model.add(Dense(actions, activation='linear'))
model.add(Flatten())
return model
def buildAgent(model, actions):
policy = BoltzmannQPolicy()
memory = SequentialMemory(limit=50000, window_length=1)
dqn = DQNAgent(model, memory=memory, policy=policy, nb_actions=actions, nb_steps_warmup=10,
target_model_update=1e-2)
return dqn
model = build_model(states, actions)
DQN = buildAgent(model, actions)
DQN.compile(tf.keras.optimizers.Adam(learning_rate=1e-3), metrics=['mae'])
DQN.fit(env, nb_steps=50000, visualize=False, verbose=1)
scores = DQN.test(env, nb_episodes=100, visualize=True)
print(np.mean(scores.history['episode_reward']))
pygame.quit()
model.save('model.h5')
有关详细信息，请参阅 docs。