在我的论文中，我描述了一个用Python编写的深度强化学习（DRL）示例。我没有编写代码，我只是在linux服务器上运行培训，一切正常。
现在，我希望可视化 tensorboard 的准确性/预测，损失，学习稳定性等等。
我在conda virutal环境中工作，我已经安装了健身房，atari-py，Pillow和PyOpenGL。在服务器上安装了TensorFlow-GPU。 This是我从中获取代码的存储库的链接。
我已经看过有关tensorboard的教程，但我得到了它，但我不能将这些变量（如tf.summary.histogram()）包含到我的代码中。我不知道究竟要把它们放在哪里以及哪些变量对于可视化很重要。
我已经确定了tensorboard将整个网络与文件编写器可视化为一个类似于this的图形。
但现在我被卡住了。每次我想尝试包含变量的直方图时，代码都会抛出错误。好像我把它们放在了错误的位置，或者我试图想象出错误的变量。 （我对python代码不太熟悉，也许我的语法错了。）如果有人可以帮助我，那将是非常好的。
This是我面向的张量板教程的链接。
下面你看到代码。该示例仅包含此文件。
from __future__ import division, print_function, unicode_literals
# Handle arguments (before slow imports so --help can be fast)
import argparse
parser = argparse.ArgumentParser(
description="Train a DQN net to play MsMacman.")
parser.add_argument("-n", "--number-steps", type=int, default=4000000,
help="total number of training steps")
parser.add_argument("-l", "--learn-iterations", type=int, default=4,
help="number of game iterations between each training step")
parser.add_argument("-s", "--save-steps", type=int, default=1000,
help="number of training steps between saving checkpoints")
parser.add_argument("-c", "--copy-steps", type=int, default=10000,
help="number of training steps between copies of online DQN to target DQN")
parser.add_argument("-r", "--render", action="store_true", default=False,
help="render the game during training or testing")
parser.add_argument("-p", "--path", default="my_dqn.ckpt",
help="path of the checkpoint file")
parser.add_argument("-t", "--test", action="store_true", default=False,
help="test (no learning and minimal epsilon)")
parser.add_argument("-v", "--verbosity", action="count", default=0,
help="increase output verbosity")
args = parser.parse_args()
from collections import deque
import gym
import numpy as np
import os
import tensorflow as tf
writer = tf.summary.FileWriter("/home/maggie/tbfiles/1")
env = gym.make("MsPacman-v0")
done = True # env needs to be reset
# First let's build the two DQNs (online & target)
input_height = 88
input_width = 80
input_channels = 1
conv_n_maps = [32, 64, 64]
conv_kernel_sizes = [(8, 8), (4, 4), (3, 3)]
conv_strides = [4, 2, 1]
conv_paddings = ["SAME"] * 3
conv_activation = [tf.nn.relu] * 3
# TESTING ACTIVATION VIS TENSORBOARD
#tf.summary.histogram("activation", conv_activation)
n_hidden_in = 64 * 11 * 10 # conv3 has 64 maps of 11x10 each
n_hidden = 512
hidden_activation = tf.nn.relu
n_outputs = env.action_space.n # 9 discrete actions are available
initializer = tf.contrib.layers.variance_scaling_initializer()
def q_network(X_state, name):
prev_layer = X_state
with tf.variable_scope(name) as scope:
for n_maps, kernel_size, strides, padding, activation in zip(
conv_n_maps, conv_kernel_sizes, conv_strides,
conv_paddings, conv_activation):
prev_layer = tf.layers.conv2d(
prev_layer, filters=n_maps, kernel_size=kernel_size,
strides=strides, padding=padding, activation=activation,
kernel_initializer=initializer)
last_conv_layer_flat = tf.reshape(prev_layer, shape=[-1, n_hidden_in])
hidden = tf.layers.dense(last_conv_layer_flat, n_hidden,
activation=hidden_activation,
kernel_initializer=initializer)
outputs = tf.layers.dense(hidden, n_outputs,
kernel_initializer=initializer)
trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=scope.name)
trainable_vars_by_name = {var.name[len(scope.name):]: var
for var in trainable_vars}
return outputs, trainable_vars_by_name
X_state = tf.placeholder(tf.float32, shape=[None, input_height, input_width,
input_channels], name="x_state")
online_q_values, online_vars = q_network(X_state, name="q_networks/online")
target_q_values, target_vars = q_network(X_state, name="q_networks/target")
# We need an operation to copy the online DQN to the target DQN
copy_ops = [target_var.assign(online_vars[var_name])
for var_name, target_var in target_vars.items()]
copy_online_to_target = tf.group(*copy_ops)
# Now for the training operations
learning_rate = 0.001
momentum = 0.95
with tf.variable_scope("train"):
X_action = tf.placeholder(tf.int32, shape=[None], name="x_action")
y = tf.placeholder(tf.float32, shape=[None, 1], name="labels")
q_value = tf.reduce_sum(online_q_values * tf.one_hot(X_action, n_outputs),
axis=1, keep_dims=True)
error = tf.abs(y - q_value)
clipped_error = tf.clip_by_value(error, 0.0, 1.0)
linear_error = 2 * (error - clipped_error)
loss = tf.reduce_mean(tf.square(clipped_error) + linear_error, name="loss")
global_step = tf.Variable(0, trainable=False, name='global_step')
optimizer = tf.train.MomentumOptimizer(
learning_rate, momentum, use_nesterov=True, name="xent")
training_op = optimizer.minimize(loss, global_step=global_step)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
# Let's implement a simple replay memory
replay_memory_size = 20000
replay_memory = deque([], maxlen=replay_memory_size)
def sample_memories(batch_size):
indices = np.random.permutation(len(replay_memory))[:batch_size]
cols = [[], [], [], [], []] # state, action, reward, next_state, continue
for idx in indices:
memory = replay_memory[idx]
for col, value in zip(cols, memory):
col.append(value)
cols = [np.array(col) for col in cols]
return (cols[0], cols[1], cols[2].reshape(-1, 1), cols[3],
cols[4].reshape(-1, 1))
# And on to the epsilon-greedy policy with decaying epsilon
eps_min = 0.1
eps_max = 1.0 if not args.test else eps_min
eps_decay_steps = args.number_steps // 2
def epsilon_greedy(q_values, step):
epsilon = max(eps_min, eps_max - (eps_max - eps_min) * step / eps_decay_steps)
if np.random.rand() < epsilon:
return np.random.randint(n_outputs) # random action
else:
return np.argmax(q_values) # optimal action
# We need to preprocess the images to speed up training
mspacman_color = np.array([210, 164, 74]).mean()
def preprocess_observation(obs):
img = obs[1:176:2, ::2] # crop and downsize
img = img.mean(axis=2) # to greyscale
img[img == mspacman_color] = 0 # Improve contrast
img = (img - 128) / 128 - 1 # normalize from -1. to 1.
return img.reshape(88, 80, 1)
# TensorFlow - Execution phase
training_start = 10000 # start training after 10,000 game iterations
discount_rate = 0.99
skip_start = 90 # Skip the start of every game (it's just waiting time).
batch_size = 50
iteration = 0 # game iterations
done = True # env needs to be reset
# We will keep track of the max Q-Value over time and compute the mean per game
loss_val = np.infty
game_length = 0
total_max_q = 0
mean_max_q = 0.0
with tf.Session() as sess:
if os.path.isfile(args.path + ".index"):
saver.restore(sess, args.path)
else:
init.run()
copy_online_to_target.run()
while True:
step = global_step.eval()
if step >= args.number_steps:
break
iteration += 1
if args.verbosity > 0:
print("\rIteration {} Training step {}/{} ({:.1f})% "
"Loss {:5f} Mean Max-Q {:5f} ".format(
iteration, step, args.number_steps, step * 100 / args.number_steps,
loss_val, mean_max_q), end="")
if done: # game over, start again
obs = env.reset()
for skip in range(skip_start): # skip the start of each game
obs, reward, done, info = env.step(0)
state = preprocess_observation(obs)
if args.render:
env.render()
# Online DQN evaluates what to do
q_values = online_q_values.eval(feed_dict={X_state: [state]})
action = epsilon_greedy(q_values, step)
# Online DQN plays
obs, reward, done, info = env.step(action)
next_state = preprocess_observation(obs)
# Let's memorize what happened
replay_memory.append((state, action, reward, next_state, 1.0 - done))
state = next_state
if args.test:
continue
# Compute statistics for tracking progress (not shown in the book)
total_max_q += q_values.max()
game_length += 1
if done:
mean_max_q = total_max_q / game_length
total_max_q = 0.0
game_length = 0
if iteration < training_start or iteration % args.learn_iterations != 0:
continue # only train after warmup period and at regular intervals
# Sample memories and use the target DQN to produce the target Q-Value
X_state_val, X_action_val, rewards, X_next_state_val, continues = (
sample_memories(batch_size))
next_q_values = target_q_values.eval(
feed_dict={X_state: X_next_state_val})
max_next_q_values = np.max(next_q_values, axis=1, keepdims=True)
y_val = rewards + continues * discount_rate * max_next_q_values
# Train the online DQN
_, loss_val = sess.run([training_op, loss], feed_dict={
X_state: X_state_val, X_action: X_action_val, y: y_val})
# Regularly copy the online DQN to the target DQN
if step % args.copy_steps == 0:
copy_online_to_target.run()
# And save regularlys
if step % args.save_steps == 0:
saver.save(sess, os.path.join(os.getcwd(), 'my_dqn.ckpt'))
merged_summary = tf.summary.merge_all()
writer.add_graph(sess.graph)
这是我在评论第#tf.summary.histogram("activation", conv_activation)行时从终端获得的错误。（第48行）
(pacman) maggie@neuronalresearch:~/Documents/AI/my_project_folder/my_project$ python tiny_dqn.py -v --number-steps 1000
Traceback (most recent call last):
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 468, in make_tensor_proto
str_values = [compat.as_bytes(x) for x in proto_values]
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 468, in <listcomp>
str_values = [compat.as_bytes(x) for x in proto_values]
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/util/compat.py", line 65, in as_bytes
(bytes_or_text,))
TypeError: Expected binary or unicode string, got <function relu at 0x7f8999cca048>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "tiny_dqn.py", line 48, in <module>
tf.summary.histogram("activation", conv_activation)
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/summary/summary.py", line 192, in histogram
tag=tag, values=values, name=scope)
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/ops/gen_logging_ops.py", line 188, in _histogram_summary
"HistogramSummary", tag=tag, values=values, name=name)
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 513, in _apply_op_helper
raise err
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 926, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 229, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 208, in constant
value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/opt/anaconda3/envs/pacman/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 472, in make_tensor_proto
"supported type." % (type(values), values))
TypeError: Failed to convert object of type <class 'list'> to Tensor. Contents: [<function relu at 0x7f8999cca048>, <function relu at 0x7f8999cca048>, <function relu at 0x7f8999cca048>]. Consider casting elements to a supported type.
(pacman) maggie@neuronalresearch:~/Documents/AI/my_project_folder/my_project$