ValueError: Error when checking input: expected dense_input to have 2 dimensions, but got array with shape (1, 1, 2)
ValueError: Error when checking input: expected dense_input to have 2 dimensions, but got array with shape (1, 1, 2)
编辑：问题已解决。解决方案如下。
正在尝试构建 RL 模型来处理任务。
有两个输入：x 和 y，两者都是在 1 到 100 的整数范围内测量的。
基于这两个输入，应该有一个输出（要采取的操作，离散（5））和置信度。
此外，我对这个领域还很陌生。请随时问我任何问题或纠正我一些看似彻头彻尾的事情 dumb/wrong.
这是我的程序（导入还没有清理....）：
from abc import ABC
import gym
from tensorflow import keras
from gym import Env
from gym.spaces import Discrete, Box
import random
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers, losses, metrics
from tensorflow.keras.layers import Dense, Flatten, Input
from tensorflow.keras.optimizers import Adam
import os
from rl.agents import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
steps = 10000
episodes = 100
score_requirement = 1000
class PlantEnv(Env, ABC):
def __init__(self):
# Actions = water: 0=(none), 1=(3 seconds), 2=(4 seconds), 3=(5 seconds), 4=(6 seconds)
self.action_space = Discrete(5)
# Starting Moisture
moisture = 20 + random.randint(-10, 10)
# Starting Chance of Rain
chance_of_rain = 50 + random.randint(-50, 50)
# Observations
self.observation_space = Box(low=np.array([0, 0]), high=np.array([100, 100]), dtype=np.int)
self.state = moisture, chance_of_rain
# Number of water steps left
self.water_length = steps
def step(self, action):
# Action section
water = 0
if action == 1:
water = 2
elif action == 2:
water = 3
elif action == 3:
water = 4
elif action == 4:
water = 5
moisture, chance_of_rain = self.state
moisture += (water * 5)
self.water_length -= 1
# Reward Section
reward = 0
if 40 <= moisture <= 60:
reward = 2
# If moisture is dry or wet
elif 60 < moisture <= 80 or 20 <= moisture < 40:
reward = 0.5
# If moisture is really dry or really wet
elif 80 < moisture <= 100 or 0 <= moisture < 20:
reward = -1
# If moisture is really dry or really wet
elif 100 < moisture or moisture < 0:
reward = -2
# Check if shower is done
if self.water_length <= 0:
done = True
else:
done = False
moistureLoss = random.randint(15, 25)
moisture -= moistureLoss
chance_of_rain = 50 + random.randint(-50, 50)
xfactor = chance_of_rain + random.randint(-50, 50)
if xfactor > 100:
moisture += (10 + random.randint(0, 15))
# Set placeholder for info
info = {}
# Save current state
self.state = moisture, chance_of_rain
# Return step information
return self.state, reward, done, info
def reset(self):
# Reset test environment
# Set starting moisture
moisture = 50 + random.randint(-10, 10)
# Set starting chance of rain array
chance_of_rain = 50 + random.randint(-50, 50)
self.state = moisture, chance_of_rain
# Reset Test time
self.water_length = steps
return self.state
def build_model():
model = Sequential()
model.add(Flatten(input_shape=(1, 4)))
model.add(Dense(24, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(2, activation='linear'))
return model
def build_agent(model):
policy = BoltzmannQPolicy()
memory = SequentialMemory(limit=50000, window_length=1)
dqn = DQNAgent(model=model, memory=memory, policy=policy, nb_actions=2,
nb_steps_warmup=10, target_model_update=1e-2)
return dqn
# Create environment
env = PlantEnv()
accepted_scores = []
training_data = []
scores = []
good_episodes = 0
# Create episodes and initiate simulation
for episode in range(1, episodes + 1):
observation = env.reset()
done = False
score = 0
history = []
prev_observation = []
while not done:
action = env.action_space.sample()
if observation[0] > 100:
action = 0
elif observation[0] < 0:
action = 4
observation, reward, done, info = env.step(action)
score += reward
if len(prev_observation) > 0:
history.append([prev_observation, action])
prev_observation = observation
if score >= score_requirement:
good_episodes += 1
accepted_scores.append(score)
for data in history:
if data[1] == 1:
output = [1]
else:
output = [0]
training_data.append([data[0], output])
scores.append(score)
if len(accepted_scores) > 0:
print("Average accepted score: ", np.mean(accepted_scores))
print("Median accepted score : ", np.median(accepted_scores))
print("Episodes above accepted score of {}: {}/{}\n".format(score_requirement, good_episodes, episodes))
model = build_model()
model.summary()
dqn = build_agent(model)
dqn.compile(Adam(learning_rate=1e-3), metrics=['mae'])
dqn.fit(env, nb_steps=50000, visualize=False, verbose=1)
第一个模型在尝试 dqn.fit 时出现此错误：
ValueError：检查输入时出错：预期 dense_input 有 2 个维度，但得到形状为 (1, 1, 2)
的数组
第二个模型在尝试 build_agent 时出现此错误：
AttributeError: 'list' 对象没有属性 'shape'
关于我做错了什么或如何纠正它的任何想法都将是一个巨大的帮助。我对自己的环境设置正确有 95% 的信心。
我最初使用第一个模型只是为了看看我是否可以让程序编译和工作。然后，经过进一步研究，我构建了第二个模型，因为我知道它能够给我一个具有置信度评级的动作。两回合都出错。
您的模型需要二维输入，但您将其定义为一维。这是一个工作示例：
from abc import ABC
import gym
from tensorflow import keras
from gym import Env
from gym.spaces import Discrete, Box
import random
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers, losses, metrics
from tensorflow.keras.layers import Dense, Flatten, Input
from tensorflow.keras.optimizers import Adam
import os
from rl.agents import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
steps = 10000
episodes = 100
score_requirement = 1000
class PlantEnv(Env, ABC):
def __init__(self):
# Actions = water: 0=(none), 1=(3 seconds), 2=(4 seconds), 3=(5 seconds), 4=(6 seconds)
self.action_space = Discrete(5)
# Starting Moisture
moisture = 20 + random.randint(-10, 10)
# Starting Chance of Rain
chance_of_rain = 50 + random.randint(-50, 50)
# Observations
self.observation_space = Box(low=np.array([0, 0]), high=np.array([100, 100]), dtype=np.int)
self.state = moisture, chance_of_rain
# Number of water steps left
self.water_length = steps
def step(self, action):
# Action section
water = 0
if action == 1:
water = 2
elif action == 2:
water = 3
elif action == 3:
water = 4
elif action == 4:
water = 5
moisture, chance_of_rain = self.state
moisture += (water * 5)
self.water_length -= 1
# Reward Section
reward = 0
if 40 <= moisture <= 60:
reward = 2
# If moisture is dry or wet
elif 60 < moisture <= 80 or 20 <= moisture < 40:
reward = 0.5
# If moisture is really dry or really wet
elif 80 < moisture <= 100 or 0 <= moisture < 20:
reward = -1
# If moisture is really dry or really wet
elif 100 < moisture or moisture < 0:
reward = -2
# Check if shower is done
if self.water_length <= 0:
done = True
else:
done = False
moistureLoss = random.randint(15, 25)
moisture -= moistureLoss
chance_of_rain = 50 + random.randint(-50, 50)
xfactor = chance_of_rain + random.randint(-50, 50)
if xfactor > 100:
moisture += (10 + random.randint(0, 15))
# Set placeholder for info
info = {}
# Save current state
self.state = moisture, chance_of_rain
# Return step information
return self.state, reward, done, info
def reset(self):
# Reset test environment
# Set starting moisture
moisture = 50 + random.randint(-10, 10)
# Set starting chance of rain array
chance_of_rain = 50 + random.randint(-50, 50)
self.state = moisture, chance_of_rain
# Reset Test time
self.water_length = steps
return self.state
def build_model():
model = Sequential()
model.add(Flatten(input_shape=(1, 2)))
model.add(Dense(24, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(5, activation='linear'))
return model
def build_agent(model):
policy = BoltzmannQPolicy()
memory = SequentialMemory(limit=50000, window_length=1)
dqn = DQNAgent(model=model, memory=memory, policy=policy, nb_actions=5,
nb_steps_warmup=10, target_model_update=1e-2)
return dqn
# Create environment
env = PlantEnv()
accepted_scores = []
training_data = []
scores = []
good_episodes = 0
# Create episodes and initiate simulation
for episode in range(1, episodes + 1):
observation = env.reset()
done = False
score = 0
history = []
prev_observation = []
while not done:
action = env.action_space.sample()
if observation[0] > 100:
action = 0
elif observation[0] < 0:
action = 4
observation, reward, done, info = env.step(action)
score += reward
if len(prev_observation) > 0:
history.append([prev_observation, action])
prev_observation = observation
if score >= score_requirement:
good_episodes += 1
accepted_scores.append(score)
for data in history:
if data[1] == 1:
output = [1]
else:
output = [0]
training_data.append([data[0], output])
scores.append(score)
if len(accepted_scores) > 0:
print("Average accepted score: ", np.mean(accepted_scores))
print("Median accepted score : ", np.median(accepted_scores))
print("Episodes above accepted score of {}: {}/{}\n".format(score_requirement, good_episodes, episodes))
model = build_model()
model.summary()
dqn = build_agent(model)
dqn.compile(Adam(learning_rate=1e-3), metrics=['mae'])
dqn.fit(env, nb_steps=50000, visualize=False, verbose=1)
请注意，input_shape 4 等于 states 的数量，输出节点 (2) 等于 actions 的数量。您将必须根据您的数据集更改这些参数。
编辑：问题已解决。解决方案如下。
正在尝试构建 RL 模型来处理任务。 有两个输入：x 和 y，两者都是在 1 到 100 的整数范围内测量的。 基于这两个输入，应该有一个输出（要采取的操作，离散（5））和置信度。
此外，我对这个领域还很陌生。请随时问我任何问题或纠正我一些看似彻头彻尾的事情 dumb/wrong.
这是我的程序（导入还没有清理....）：
from abc import ABC
import gym
from tensorflow import keras
from gym import Env
from gym.spaces import Discrete, Box
import random
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers, losses, metrics
from tensorflow.keras.layers import Dense, Flatten, Input
from tensorflow.keras.optimizers import Adam
import os
from rl.agents import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
steps = 10000
episodes = 100
score_requirement = 1000
class PlantEnv(Env, ABC):
def __init__(self):
# Actions = water: 0=(none), 1=(3 seconds), 2=(4 seconds), 3=(5 seconds), 4=(6 seconds)
self.action_space = Discrete(5)
# Starting Moisture
moisture = 20 + random.randint(-10, 10)
# Starting Chance of Rain
chance_of_rain = 50 + random.randint(-50, 50)
# Observations
self.observation_space = Box(low=np.array([0, 0]), high=np.array([100, 100]), dtype=np.int)
self.state = moisture, chance_of_rain
# Number of water steps left
self.water_length = steps
def step(self, action):
# Action section
water = 0
if action == 1:
water = 2
elif action == 2:
water = 3
elif action == 3:
water = 4
elif action == 4:
water = 5
moisture, chance_of_rain = self.state
moisture += (water * 5)
self.water_length -= 1
# Reward Section
reward = 0
if 40 <= moisture <= 60:
reward = 2
# If moisture is dry or wet
elif 60 < moisture <= 80 or 20 <= moisture < 40:
reward = 0.5
# If moisture is really dry or really wet
elif 80 < moisture <= 100 or 0 <= moisture < 20:
reward = -1
# If moisture is really dry or really wet
elif 100 < moisture or moisture < 0:
reward = -2
# Check if shower is done
if self.water_length <= 0:
done = True
else:
done = False
moistureLoss = random.randint(15, 25)
moisture -= moistureLoss
chance_of_rain = 50 + random.randint(-50, 50)
xfactor = chance_of_rain + random.randint(-50, 50)
if xfactor > 100:
moisture += (10 + random.randint(0, 15))
# Set placeholder for info
info = {}
# Save current state
self.state = moisture, chance_of_rain
# Return step information
return self.state, reward, done, info
def reset(self):
# Reset test environment
# Set starting moisture
moisture = 50 + random.randint(-10, 10)
# Set starting chance of rain array
chance_of_rain = 50 + random.randint(-50, 50)
self.state = moisture, chance_of_rain
# Reset Test time
self.water_length = steps
return self.state
def build_model():
model = Sequential()
model.add(Flatten(input_shape=(1, 4)))
model.add(Dense(24, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(2, activation='linear'))
return model
def build_agent(model):
policy = BoltzmannQPolicy()
memory = SequentialMemory(limit=50000, window_length=1)
dqn = DQNAgent(model=model, memory=memory, policy=policy, nb_actions=2,
nb_steps_warmup=10, target_model_update=1e-2)
return dqn
# Create environment
env = PlantEnv()
accepted_scores = []
training_data = []
scores = []
good_episodes = 0
# Create episodes and initiate simulation
for episode in range(1, episodes + 1):
observation = env.reset()
done = False
score = 0
history = []
prev_observation = []
while not done:
action = env.action_space.sample()
if observation[0] > 100:
action = 0
elif observation[0] < 0:
action = 4
observation, reward, done, info = env.step(action)
score += reward
if len(prev_observation) > 0:
history.append([prev_observation, action])
prev_observation = observation
if score >= score_requirement:
good_episodes += 1
accepted_scores.append(score)
for data in history:
if data[1] == 1:
output = [1]
else:
output = [0]
training_data.append([data[0], output])
scores.append(score)
if len(accepted_scores) > 0:
print("Average accepted score: ", np.mean(accepted_scores))
print("Median accepted score : ", np.median(accepted_scores))
print("Episodes above accepted score of {}: {}/{}\n".format(score_requirement, good_episodes, episodes))
model = build_model()
model.summary()
dqn = build_agent(model)
dqn.compile(Adam(learning_rate=1e-3), metrics=['mae'])
dqn.fit(env, nb_steps=50000, visualize=False, verbose=1)
第一个模型在尝试 dqn.fit 时出现此错误： ValueError：检查输入时出错：预期 dense_input 有 2 个维度，但得到形状为 (1, 1, 2)
的数组第二个模型在尝试 build_agent 时出现此错误： AttributeError: 'list' 对象没有属性 'shape'
关于我做错了什么或如何纠正它的任何想法都将是一个巨大的帮助。我对自己的环境设置正确有 95% 的信心。
我最初使用第一个模型只是为了看看我是否可以让程序编译和工作。然后，经过进一步研究，我构建了第二个模型，因为我知道它能够给我一个具有置信度评级的动作。两回合都出错。
您的模型需要二维输入，但您将其定义为一维。这是一个工作示例：
from abc import ABC
import gym
from tensorflow import keras
from gym import Env
from gym.spaces import Discrete, Box
import random
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers, losses, metrics
from tensorflow.keras.layers import Dense, Flatten, Input
from tensorflow.keras.optimizers import Adam
import os
from rl.agents import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
steps = 10000
episodes = 100
score_requirement = 1000
class PlantEnv(Env, ABC):
def __init__(self):
# Actions = water: 0=(none), 1=(3 seconds), 2=(4 seconds), 3=(5 seconds), 4=(6 seconds)
self.action_space = Discrete(5)
# Starting Moisture
moisture = 20 + random.randint(-10, 10)
# Starting Chance of Rain
chance_of_rain = 50 + random.randint(-50, 50)
# Observations
self.observation_space = Box(low=np.array([0, 0]), high=np.array([100, 100]), dtype=np.int)
self.state = moisture, chance_of_rain
# Number of water steps left
self.water_length = steps
def step(self, action):
# Action section
water = 0
if action == 1:
water = 2
elif action == 2:
water = 3
elif action == 3:
water = 4
elif action == 4:
water = 5
moisture, chance_of_rain = self.state
moisture += (water * 5)
self.water_length -= 1
# Reward Section
reward = 0
if 40 <= moisture <= 60:
reward = 2
# If moisture is dry or wet
elif 60 < moisture <= 80 or 20 <= moisture < 40:
reward = 0.5
# If moisture is really dry or really wet
elif 80 < moisture <= 100 or 0 <= moisture < 20:
reward = -1
# If moisture is really dry or really wet
elif 100 < moisture or moisture < 0:
reward = -2
# Check if shower is done
if self.water_length <= 0:
done = True
else:
done = False
moistureLoss = random.randint(15, 25)
moisture -= moistureLoss
chance_of_rain = 50 + random.randint(-50, 50)
xfactor = chance_of_rain + random.randint(-50, 50)
if xfactor > 100:
moisture += (10 + random.randint(0, 15))
# Set placeholder for info
info = {}
# Save current state
self.state = moisture, chance_of_rain
# Return step information
return self.state, reward, done, info
def reset(self):
# Reset test environment
# Set starting moisture
moisture = 50 + random.randint(-10, 10)
# Set starting chance of rain array
chance_of_rain = 50 + random.randint(-50, 50)
self.state = moisture, chance_of_rain
# Reset Test time
self.water_length = steps
return self.state
def build_model():
model = Sequential()
model.add(Flatten(input_shape=(1, 2)))
model.add(Dense(24, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(5, activation='linear'))
return model
def build_agent(model):
policy = BoltzmannQPolicy()
memory = SequentialMemory(limit=50000, window_length=1)
dqn = DQNAgent(model=model, memory=memory, policy=policy, nb_actions=5,
nb_steps_warmup=10, target_model_update=1e-2)
return dqn
# Create environment
env = PlantEnv()
accepted_scores = []
training_data = []
scores = []
good_episodes = 0
# Create episodes and initiate simulation
for episode in range(1, episodes + 1):
observation = env.reset()
done = False
score = 0
history = []
prev_observation = []
while not done:
action = env.action_space.sample()
if observation[0] > 100:
action = 0
elif observation[0] < 0:
action = 4
observation, reward, done, info = env.step(action)
score += reward
if len(prev_observation) > 0:
history.append([prev_observation, action])
prev_observation = observation
if score >= score_requirement:
good_episodes += 1
accepted_scores.append(score)
for data in history:
if data[1] == 1:
output = [1]
else:
output = [0]
training_data.append([data[0], output])
scores.append(score)
if len(accepted_scores) > 0:
print("Average accepted score: ", np.mean(accepted_scores))
print("Median accepted score : ", np.median(accepted_scores))
print("Episodes above accepted score of {}: {}/{}\n".format(score_requirement, good_episodes, episodes))
model = build_model()
model.summary()
dqn = build_agent(model)
dqn.compile(Adam(learning_rate=1e-3), metrics=['mae'])
dqn.fit(env, nb_steps=50000, visualize=False, verbose=1)
请注意，input_shape 4 等于 states 的数量，输出节点 (2) 等于 actions 的数量。您将必须根据您的数据集更改这些参数。