I use google colab for execution.
In the following code, as all the arguments are passed by default. I can pass the args as an empty list, which can be seen from the last line:
DEFAULT_ENV_NAME = "PongNoFrameskip-v4"
MEAN_REWARD_BOUND = 19.5
parser.add_argument("--cuda", default=True, action="store_true", help="Enable cuda")
parser.add_argument("--env", default=DEFAULT_ENV_NAME,
help="Name of the environment, default=" + DEFAULT_ENV_NAME)
parser.add_argument("--reward", type=float, default=MEAN_REWARD_BOUND,
help="Mean reward boundary for stop of training, default=%.2f" % MEAN_REWARD_BOUND)
args = parser.parse_args(args = [])
when I use print(args) I got:
Namespace(cuda=True, env='PongNoFrameskip-v4', reward=19.5)
Also when I execute the following code:
DEFAULT_ENV_NAME = "PongNoFrameskip-v4"
parser.add_argument("-m", "--model", required=True, help="Model file to load")
parser.add_argument("-e", "--env", default=DEFAULT_ENV_NAME,
help="Environment name to use, default=" + DEFAULT_ENV_NAME)
parser.add_argument("-r", "--record", help="Directory to store video recording")
parser.add_argument("--no-visualize", default=True, action='store_false', dest='visualize',
help="Disable visualization of the game play")
args = parser.parse_args(args=['dqn_model'])
I am getting errors in syntax, which is because of the last line. I need to send required values for model argument (say dqn_model).
What is the correct syntax for passing the arguments?
I tried the following which gave me errors:
args = parser.parse_args(args=['dqn_model'])
args = parser.parse_args(args=[model='dqn_model'])
args = parser.parse_args(args=[m='dqn_model'])
this worked out for me
use :
args = argparse.Namespace(cuda="Argument=required",env="PongNoFrameskip-v4",reward=19.5)
make sure you have added all the argument keys with their respective value.
Related
I have tried to apply the advice in this question but I still don't seem to get the code to work. I am trying to pass two variables
How to pass variables in python flask to mysqldb?
cur = mysql.connection.cursor()
for row in jdict:
headline = row['title']
url = row['url']
cur.execute("INSERT INTO headlinetitles (Title, Url) VALUES ('%s','%s');",str(headline),str(url))
mysql.connection.commit()
I get the error TypeError: execute() takes from 2 to 3 positional arguments but 4 were given
I found the solution. Maybe this will help someone.
cur = mysql.connection.cursor()
for row in jdict:
headline = row['title']
url = row['url']
sqldata = ("INSERT INTO headlinetitles (Title, Url) VALUES ('%s','%s');",str(headline),str(url))
cur.execute = (sqldata)
mysql.connection.commit()
Im trying to get the value of a map by running Code.eval_string/1 and it fails with error:
warning: variable "data" does not exist and is being expanded to "data()", please use parentheses to remove the ambiguity or change the variable name
nofile:1
** (CompileError) nofile:1: undefined function data/0
(elixir 1.10.2) lib/code.ex:332: Code.eval_string_with_error_handling/3
main.exs:65: RulesEngine.evaluate_object_by_condition/2
(elixir 1.10.2) lib/enum.ex:1396: Enum."-map/2-lists^map/1-0-"/2
main.exs:17: RulesEngine.evaluate_object_by_condition/2
Code is:
#doc "Evaluate condition"
#default_op "="
def evaluate_object_by_condition(data, condition) do
IO.puts("Evaluando una regla en particular:")
IO.inspect(condition)
IO.inspect(data)
attr = condition.attr
value = condition.value
IO.inspect(attr)
eval_data = Code.eval_string(attr)
op = Map.get(condition, "op", #default_op)
IO.puts("DATA to EVAL")
IO.inspect(eval_data)
# value_type = Map.get(condition, "type")
# Hacer type checking y agregar a value_type
## Falta obtener el valor del objeto
# res = evaluate("=", value, obj.value)
true
end
then I run:
obj = %{
value: 1,
tiene_beca: 1,
tiene_credito: 1
}
condition= %{
attr: "data.tiene_beca",
value: 1
}
RulesEngine.evaluate_object_by_condition(obj, rules_or)
So I'm trying to get the value of data.tiene_beca, and getting that variable name from a string, which would be the correct way of doing that in elixir?
First of all, consider whether you actually need all this flexibility. Code.eval_string runs the code without any checks or restrictions whatsoever, so this potentially opens a security hole in the code. I would do something like this instead:
["data", field_name] = String.split(attr, ".")
field_name = String.to_existing_atom(field_name)
eval_data = data[field_name]
That said, the reason your code isn't working is that Code.eval_string doesn't have access to local variables in the calling function, so you'd need to pass the variable as a binding explicitly:
eval_data = Code.eval_string(attr, [data: data])
I want to use the variable that I passed to a function which contains a file path. However, I don't get it working.
For example, I have a path like "/samba-test/log_gen/log_gen/log_generator" and when I read this path to a variable it doesn't work as expected. Please refer to my explaination in the code. My comments
are tagged with the string "VENK" . Any help would be appreciated.
/* caller */
config_path = "/samba-test/log_gen/log_gen/log_generator"
ReadWrite_Config(config_path)
/*definition*/
def ReadWrite_Timeline(lp_readpath, lp_filterlist):
current_parent_path = lp_readpath
current_search_list = lp_filterlist
print(current_parent_path) >>>>>> VENK - PATH prints fine here as expected <<<<<<<<.
strings_1 = ("2e88422c-4b61-41d7-9cf9-4650edaa4e56", "2017-11-27 16:1")
for index in range(0,3):
print (current_search_list[index])
files=None
filext=[".txt",".log"]
#outputfile = open(wrsReportFileName, "a")
for ext in filext:
print("Current_Parent_Path",current_parent_path ) <<<<<<VENK - Prints as Expected ""
#VENK - The above line prints as ('Current_Parent_Path', '/samba-test/log_gen/log_gen/log_generator') which is expected
#The actual files are inside the 'varlog' where the 'varlog' folder is inside '/samba-test/log_gen/log_gen/log_generator'
#possible problematic line below.
varlogpath = "(current_parent_path/varlog)/*"+ext >>>>>>>>>>> VENK- Unable to find the files if given in this format <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
print("varlogpath",varlogpath) >>>>>>>>>>>> VENK- varlogpath doesn't print as expected <<<<<<<<<<<<<<<<<<<<
#VENK - The above line prints as ('varlogpath', 'current_parent_path/varlog/*.txt') which I feel is problematic.
#VENK - If I give the absolute path as below it works fine
#varlogpath = "/samba-test/log_gen/log_gen/log_generator/varlog/*"+ext
files = glob.glob(varlogpath)
for file in files:
fname_varlog = open(file, 'r')
outputfile.write("\n")
outputfile.write(file)
outputfile.write("\n")
for line in fname_varlog:
#if any(s in line for s in strings):
"""
#s1 searches the mandatory arguments
#s2 searches the optional arguments
"""
if all(s1 in line for s1 in strings_1):
#if all(s1 in line for s1 in strings_1) or all(s2 in line for s2 in strings_2):
#print (file, end="")
outputfile.write(line)
fname_varlog.close()
outputfile.write("\n")
outputfile.write("10.[##### Summary of the Issue #####] -- Enter Data Manually \n")
outputfile.write("\n")
outputfile.close()
#print (ext)
A path join to the variable 'current_parent_path' helped to resolve the problem (like below).
varlogpath = os.path.join(current_parent_path, "*"+ext)
I am trying to understand more about certain surprising results i see in implementing a tf graph .
The graph i am working with is just a forest (bunch of trees). This is just a plain forward inference graph , and nothing related to training. I am sharing the snippets for 2 implementation
code snippet 1:
with tf.name_scope("main"):
def get_tree_output(offset):
loop_vars = (offset,)
leaf_indice = tf.while_loop(cond,
body,
loop_vars,
back_prop=False,
parallel_iterations=1,
name="while_loop")
tree_score = tf.gather(score_tensor, leaf_indice, name="tree-scores")
output = tf.add(tree_score, output)
leaf_indices = tf.map_fn(get_tree_output,
tree_offsets_tensor,
dtype=INT_TYPE,
parallel_iterations=n_trees,
back_prop=False,
name="tree-scores")
tree_scores = tf.gather(score_tensor, leaf_indices, name="tree-scores")
output = tf.reduce_sum(tree_scores, name="sum-output")
output = tf.sigmoid(output, name="sigmoid-output")
code snippet 2:
with tf.name_scope("main"):
tree_offsets_tensor = tf.constant(tree_offsets, dtype=INT_TYPE, name="tree_offsets_tensor")
loop_vars = (tree_offsets_tensor,)
leaf_indices = tf.while_loop(cond,
body,
loop_vars,
back_prop=False,
parallel_iterations=n_trees,
name="while_loop")
tree_scores = tf.gather(score_tensor, leaf_indices, name="tree-scores")
output = tf.reduce_sum(tree_scores, name="sum-output")
output = tf.sigmoid(output, name="sigmoid-output")
The rest of the code is exactly the same : the constant tensors , variables, condition and body for the while loop. thread and parallelism was also the same in both case
code snippet2 : takes about 500 micro sec to do inference
code snippet 1 : take about 12 milli sec to do inference
The difference is that in snippet 1 , I use map_fn to operate on tree_offset_tensor, where as in snippet 2 , I get rid of that map_fn, and just directly use that tensor, so as I understand in snippet1 get_tree_output method gets called with one element from tree_offset_tensor, we are having multiple while_loop for each individual offset value, whereas in snippet 2 we just have one while_loop that just takes multiple offset values (basically the offset_tensor).
I also tried another variation for snippet , instead of using the map_fn I write a hand written for loop
code snippet 1 (variation for loop) :
output = 0
with tf.name_scope("main"):
for offset in tree_offsets:
loop_vars = (offset,)
leaf_indice = tf.while_loop(cond,
body,
loop_vars,
back_prop=False,
parallel_iterations=1,
name="while_loop")
tree_score = tf.gather(score_tensor, leaf_indice, name="tree-scores")
output = tf.add(tree_score, output)
#leaf_indices = tf.map_fn(get_tree_output,
# tree_offsets_tensor, dtype=INT_TYPE,
# parallel_iterations=n_trees, back_prop=False,
# name="tree-scores")
#tree_scores = tf.gather(score_tensor, leaf_indices, name="tree-scores")
#output = tf.reduce_sum(tree_scores, name="sum-output")
output = tf.sigmoid(output, name="sigmoid-output")
This gives minor improvement : 9 millisec
I wrote simple class to compress data. Here it is:
LZWCompressor = {}
function LZWCompressor.new()
local self = {}
self.mDictionary = {}
self.mDictionaryLen = 0
-- ...
self.Encode = function(sInput)
self:InitDictionary(true)
local s = ""
local ch = ""
local len = string.len(sInput)
local result = {}
local dic = self.mDictionary
local temp = 0
for i = 1, len do
ch = string.sub(sInput, i, i)
temp = s..ch
if dic[temp] then
s = temp
else
result[#result + 1] = dic[s]
self.mDictionaryLen = self.mDictionaryLen + 1
dic[temp] = self.mDictionaryLen
s = ch
end
end
result[#result + 1] = dic[s]
return result
end
-- ...
return self
end
And i run it by:
local compressor = LZWCompression.new()
local encodedData = compressor:Encode("I like LZW, but it doesnt want to compress this text.")
print("Input length:",string.len(originalString))
print("Output length:",#encodedData)
local decodedString = compressor:Decode(encodedData)
print(decodedString)
print(originalString == decodedString)
But when i finally run it by lua, it shows that interpreter expected string, not Table. That was strange thing, because I pass argument of type string. To test Lua's logs, i wrote at beggining of function:
print(typeof(sInput))
I got output "Table" and lua's error. So how to fix it? Why lua displays that string (That i have passed) is a table? I use Lua 5.3.
Issue is in definition of method Encode(), and most likely Decode() has same problem.
You create Encode() method using dot syntax: self.Encode = function(sInput),
but then you're calling it with colon syntax: compressor:Encode(data)
When you call Encode() with colon syntax, its first implicit argument will be compressor itself (table from your error), not the data.
To fix it, declare Encode() method with colon syntax: function self:Encode(sInput), or add 'self' as first argument explicitly self.Encode = function(self, sInput)
The code you provided should not run at all.
You define function LZWCompressor.new() but call CLZWCompression.new()
Inside Encode you call self:InitDictionary(true) which has not been defined.
Maybe you did not paste all relevant code here.
The reason for the error you get though is that you call compressor:Encode(sInput) which is equivalent to compressor.Encode(self, sInput). (syntactic sugar) As function parameters are not passed by name but by their position sInput inside Encode is now compressor, not your string.
Your first argument (which happens to be self, a table) is then passed to string.len which expects a string.
So you acutally call string.len(compressor) which of course results in an error.
Please make sure you know how to call and define functions and how to use self properly!