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Идея этого проекта состоит в том, чтобы в конечном итоге удалить «тестовые» переменные и использовать реальные данные с датчиков. Тестовая среда работает, но теперь я хотел бы иметь возможность использовать реальные данные.

В качестве входных данных используются две точки данных, от 1 до 100: влажность почвы и вероятность дождя.

Итог: я просто хочу ввести два числа и получить наилучший прогноз модели для предпринимаемых действий (и, если возможно, доверительный процент).

Однако я получаю ошибки при попытке сделать прогноз.

Вещи, которые я пробовал:

 pred = dqn.model.predict(np.array([30, 30])) ValueError: Error when checking input: expected flatten_input to have 3 dimensions, but got array with shape (2, 1)  pred = dqn.model.predict(np.expand_dims(np.array([30, 30]), axis=0)) ValueError: Error when checking input: expected flatten_input to have 3 dimensions, but got array with shape (1, 2)   

Я видел, как в некоторых других потоках упоминалось изменение формы, но я немного перегорел в этом проекте и не уверен, что это решение.

Я понизил некоторые переменные для более быстрого тестирования, но здесь. Вот мой текущий код:

 import os import random from abc import ABC import numpy as np from gym import Env from gym.spaces import Discrete, Box from rl.agents import DQNAgent from rl.memory import SequentialMemory from rl.policy import BoltzmannQPolicy from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.models import Sequential from tensorflow.keras.optimizers import Adam  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Step per episode steps = 10000 # Number of episodes episodes = 100 # Score requirement per episode # Used for stats and to filter training data score_requirement = 1000   # Creates a Model that emulates a Markov Decision Process # Finite process -gt; steps # Rewards for watering well and punishes for watering bad # Action -gt; Observation -gt; Reward 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   # Retrieve previous state  moisture, chance_of_rain = self.state   # The lower/higher this is, greatly affects the scoring  # 5 or 6 is the best with this setup  moisture  = (water * 5)  self.water_length -= 1   # Reward Section  reward = 0  if 40 lt;= moisture lt;= 60:  reward = 2  # If moisture is dry or wet  elif 60 lt; moisture lt;= 80 or 20 lt;= moisture lt; 40:  reward = 1  # If moisture is really dry or really wet  elif 80 lt; moisture lt;= 100 or 0 lt;= moisture lt; 20:  reward = -1  # If moisture is really dry or really wet  elif 100 lt; moisture or moisture lt; 0:  reward = -2   # Check if shower is done  if self.water_length lt;= 0:  done = True  else:  done = False   # Apply noise to test program  # Simulate real-life conditions: evaporation, water loss, rain  # Not used in final program  moistureLoss = random.randint(15, 25)  moisture -= moistureLoss  # Simulate chance of rain  chance_of_rain = 50   random.randint(-50, 50)  xfactor = chance_of_rain   random.randint(-50, 50)  if xfactor gt; 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   # # Builds a model using previously defined states and actions # def build_model(): # inputs = Input(shape=(1, 2), name="input") # inputsF = Flatten()(inputs) # common = Dense(24, activation="relu", name="state")(inputsF) # action = Dense(5, activation="softmax", name="action")(common) # critic = Dense(1, name="output")(common) # model = keras.Model(inputs=inputs, outputs=[action, critic]) # return model  # Build Model def build_model():  model = Sequential()  model.add(Flatten(input_shape=(1, 2)))  model.add(Dense(24, activation='relu'))  model.add(Dense(48, activation='relu'))  model.add(Dense(5, activation='linear'))  return model   # Build Agent def build_agent(model):  policy = BoltzmannQPolicy()  memory = SequentialMemory(limit=1000, window_length=1)  dqn = DQNAgent(model=model, memory=memory, policy=policy, nb_actions=5,  nb_steps_warmup=50, target_model_update=1e-3)  return dqn   # Build Deep-Q-Network def build_dqn(dqn):  dqn.compile(Adam(learning_rate=1e-3), metrics=['mae', 'accuracy'])  dqn.fit(env, nb_steps=2000, visualize=False, verbose=1)  return dqn   # Create environment env = PlantEnv()  # Store data to show scoring stats and to use for training. 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 = []   # Print starting moisture to compare to ending moisture  # print("Start Moisture: {}%".format(observation[0]))  while not done:  action = env.action_space.sample()  # Force action override for plant protection  if observation[0] gt; 100:  action = 0  elif observation[0] lt; 0:  action = 4  observation, reward, done, info = env.step(action)  score  = reward  if len(prev_observation) gt; 0:  history.append([prev_observation, action])  prev_observation = observation   # # Print ending moisture to compare to starting moisture  # # Then print Episode number and score  # print("End Moisture : {}%".format(observation[0]))  # print('Episode: {} Score:{}n'.format(episode, score))   # Gather scores for episodes scoring above requirement  if score gt;= 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)  # Print number of episodes above score requirement if len(accepted_scores) gt; 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))  # Build Model and print summary model = build_model() model.summary()  # # Save Model # model.save('./testModel1', overwrite=True) # print("Model saved.")  dqn = build_agent(model) dqn = build_dqn(dqn)   scores = dqn.test(env, nb_episodes=1, visualize=False) print(np.mean(scores.history['episode_reward']))  pred = dqn.model.predict(np.expand_dims(np.array([30, 30]), axis=0))    

Убедитесь, что ваш ввод в модель имеет правильную форму. Для этого требуется 3D-тензор, где первым измерением является размер пакета. Попробуй это:

 test_array = np.random.random((1, 1, 2)) print('Test array --gt; ', test_array) print('Predictions --gt; ', dqn.model.predict(test_array))  

 Test array --gt; [[[0.4636345 0.18498545]]] Predictions --gt; [[-0.01383634 0.006188 0.03987967 0.03497294 0.02642388]]