The idea behind this project is to eventually remove the "test" variables and utilize real data from sensors. The test environment works, but now I'd like to be able to use real data.
Two datapoints, ints from 1 to 100, are used as input: soil moisture and chance of rain.
Bottom line: I just want to input two numbers and get the model's best prediction for the action to take (and, if possible, a confidence percentage).
I'm getting errors when trying to make a prediction though.
Things I've tried:
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)
I saw some other threads mention reshaping, but I'm a little burnt out on this project and not sure if that is the solution.
I lowered some of the variables for purposes of testing quicker, but here. Here is my current code:
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 -> steps
# Rewards for watering well and punishes for watering bad
# Action -> Observation -> 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 <= moisture <= 60:
reward = 2
# If moisture is dry or wet
elif 60 < moisture <= 80 or 20 <= moisture < 40:
reward = 1
# 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
# 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 > 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] > 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
# # 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 >= 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) > 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))
CodePudding user response：
Make sure your input to your model has the correct shape. It requires a 3D tensor, where the first dimension is the batch size. Try this:
test_array = np.random.random((1, 1, 2))
print('Test array --> ', test_array)
print('Predictions --> ', dqn.model.predict(test_array))
Test array --> [[[0.4636345 0.18498545]]]
Predictions --> [[-0.01383634 0.006188 0.03987967 0.03497294 0.02642388]]