【发布时间】：2018-05-21 00:16:00
【问题描述】：
我正在使用 Keras 使用 Open AI 的 Gym 库中的图像创建一个深度神经网络。
我尝试使用以下代码重塑图像：
def reshape_dimensions(observation):
processed = np.mean(observation,2,keepdims = False)
cropped = processed[35:195]
result = cropped[::2,::2]
return result
这给了我一个形状 (80,80) 的图像，但每次我尝试在 Keras 网络的第一层输入该形状时它都不起作用。
我应该使用什么形状才能进一步开发网络？
附上整个代码：
第一部分检索训练数据
import gym
import random
import numpy as np
from statistics import mean, median
from collections import Counter
### GAME VARIABLE SETTINGS ###
env = gym.make('MsPacman-v0')
env.reset()
goal_steps = 2000
score_requirement = 250
initial_games = 200
print('Options to play: ',env.unwrapped.get_action_meanings())
### DEFINE FUNCTIONS ####
def reshape_dimensions(observation):
processed = np.mean(observation,2,keepdims = False)
cropped = processed[35:195]
result = cropped[::2,::2]
return result
def initial_population():
training_data = []
scores = []
accepted_scores = []
for _ in range(initial_games):
score = 0
game_memory = []
prev_obvservation = []
for _ in range(goal_steps):
#env.render()
action = env.action_space.sample() #Take random action in the env
observation, reward, done, info = env.step(action)
reshape_observation = reshape_dimensions(observation)
if len(prev_obvservation) > 0:
game_memory.append([prev_obvservation, action])
prev_obvservation = reshape_observation
score = score + reward
if done:
break
if score >= score_requirement:
accepted_scores.append(score)
for data in game_memory:
if data[1] == 0:
output = [1,0,0,0,0,0,0,0,0]
elif data[1] == 1:
output = [0,1,0,0,0,0,0,0,0]
elif data[1] == 2:
output = [0,0,1,0,0,0,0,0,0]
elif data[1] == 3:
output = [0,0,0,1,0,0,0,0,0]
elif data[1] == 4:
output = [0,0,0,0,1,0,0,0,0]
elif data[1] == 5:
output = [0,0,0,0,0,1,0,0,0]
elif data[1] == 6:
output = [0,0,0,0,0,0,1,0,0]
elif data[1] == 7:
output = [0,0,0,0,0,0,0,1,0]
elif data[1] == 8:
output = [0,0,0,0,0,0,0,0,1]
training_data.append([data[0],output])
env.reset()
scores.append(score)
print('Average accepted scores:', mean(accepted_scores))
print('Median accepted scores:', median(accepted_scores))
print(Counter(accepted_scores))
return training_data
### RUN CODE ###
training_data = initial_population()
np.save('data_for_training_200.npy', training_data)
第二部分训练模型
import gym
import random
import numpy as np
import keras
from statistics import mean, median
from collections import Counter
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping
from keras.optimizers import Adam
### LOAD DATA ###
raw_training_data = np.load("data_for_training_200.npy")
training_data = [i[0:2] for i in raw_training_data]
print(np.shape(training_data))
### DEFINE FUNCTIONS ###
def neural_network_model():
network = Sequential()
network.add(Dense(100, activation = 'relu', input_shape = (80,80)))
network.add(Dense(9,activation = 'softmax'))
optimizer = Adam(lr = 0.001)
network.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics=['accuracy'])
return network
def train_model(training_data):
X = [i[0] for i in training_data]
y = [i[1] for i in training_data]
#X = np.array([i[0] for i in training_data])
#y = np.array([i[1] for i in training_data])
print('shape of X: ', np.shape(X))
print('shape of y: ', np.shape(y))
early_stopping_monitor = EarlyStopping(patience = 3)
model = neural_network_model()
model.fit(X, y, epochs = 20, callbacks = [early_stopping_monitor])
return model
train_model(training_data = training_data)
【问题讨论】：
-
您能否在问题中添加您遇到的确切错误？运行代码时终端的输出是什么？
-
附上错误代码：
ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 array(s), but instead got the following list of 36859 arrays: [array([[150. , 150. , 54.66666667, ..., 54.66666667, 150. , 150. ], [150. , 150. , 54.66666667, ..., 54.66666667, 150. , 15...
标签： python-3.x neural-network keras deep-learning openai-gym