amt | Adversarial Machine Translation with pytorch | Machine Learning library
kandi X-RAY | amt Summary
kandi X-RAY | amt Summary
Adversarial Machine Translation with pytorch
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Top functions reviewed by kandi - BETA
- Builds the next batch
- Pads a list of lines
- Generate next batch
- Construct source and target batches
- Translate source into sequence
- Create a tensorflow input tensor
- Updates the beam with the given probabilities
- Start search
- Forward computation
- Perform projection on input matrix
- Repeats an input tensor
- Compute the critic
- Gradient of the gradient loss function
- Gumbel softmax
- Forward computation
- Apply n_heads to input tensor
- Project the heads of the input image
- Perform a forward projection
- Create an autogressive mask
- Forward the layer
- Unroll a masked mask
- Train the translator
- Zero gradient
- Translates source translation
- Train the model
- Step the optimizer
amt Key Features
amt Examples and Code Snippets
Community Discussions
Trending Discussions on amt
QUESTION
I have two scenarios:
- Scenario 1: L node having child node SL
- Scenario 2: L node with no child node SL
I need to form multiple L nodes if text "L1" () is found at other nodes like and .
Id attribute of SL node(i.e ) is formed using "L1" in . Also ref attribute of pit node(i.e ) is formed using "L1" in ,
I need to check whether "L1" is present in either id attribute of SL or ref attribute of pit and form the desired output.
Input xml as below
...ANSWER
Answered 2022-Mar-31 at 18:25I suppose in the second scenario there is a relation between the L/@Id and the pit/@ref. For now I used the assumption that the first two chars of pit/@ref should match the L/@Id.
If that is correct you could try something like this:
QUESTION
I have the following database along with a couple of input values:
...ANSWER
Answered 2022-Mar-11 at 18:25We may use .data to select the column based on the input col
QUESTION
def change_salary(data: list, amt: int) -> list:
new_data = data[:]
new_data[1] += amt
return new_data
def change_salaries(employees: list, amt: int) -> list:
return list(map(change_salary, employees, [amt]*amt))
employees = [
["Person1", 2000000],
["Person2", 2500000]
]
happier_employees = change_salaries(employees, 100000)
ANSWER
Answered 2022-Feb-26 at 11:40You can use currying, like with partial from functools:
QUESTION
I have tried the similar problems' solutions on here but none seem to work. It seems that I get a memory error when installing tensorflow from requirements.txt. Does anyone know of a workaround? I believe that installing with --no-cache-dir would fix it but I can't figure out how to get EB to do that. Thank you.
Logs:
...ANSWER
Answered 2022-Feb-05 at 22:37The error says MemoryError. You must upgrade your ec2 instance to something with more memory. tensorflow is very memory hungry application.
QUESTION
Reading a large file from S3 ( >5GB) into lambda with the following code:
...ANSWER
Answered 2022-Jan-29 at 19:42As mentioned in the bug you linked to, the core issue in Python 3.8 is the bug with reading more than 1gb at a time. You can use a variant of the workaround suggested in the bug to read the file in chunks.
QUESTION
I'm trying to receive dhcp6 messages in rust. This is the code I have right now:
...ANSWER
Answered 2022-Jan-02 at 11:40The problem was the following line:
QUESTION
I'm trying to understand histomorphisms from this blog on recursion schemes. I'm facing a problem when I'm running the example to solve the change making problem as mentioned in the blog.
Change making problem takes the denominations for a currency and tries to find the minimum number of coins required to create a given sum of money. The code below is taken from the blog and should compute the answer.
...ANSWER
Answered 2021-Oct-20 at 12:38I see two problems with this program. One of them I know how to fix, but the other apparently requires more knowledge of recursion schemes than I have.
The one I can fix is that it's looking up the wrong values in its cache. When given = 10, of course validCoins = [10,5,1], and so we find (zeroes, toProcess) = ([0], [5,9]). So far so good: we can give a dime directly, or give a nickel and then make change for the remaining five cents, or we can give a penny and change the remaining nine cents. But then when we write lookup 9 attr, we're saying "look 9 steps in history to when curr = 1", where what we meant was "look 1 step into history to when curr = 9". As a result we drastically undercount in pretty much all cases: even change 100 is only 16, while a Google search claims the right result is 292 (I haven't verified this today by implementing it myself).
There are a few equivalent ways to fix this; the smallest diff would be to replace
QUESTION
I have written few classes for my data parsing. Data is the dict with very big depth and lots of string values (numbers, dates, bools) are all strings. With data which is presented there is no problems and type conversion works well. But with "empty" values ("" ones) I get validation error. I was trying to write validator but unsuccessfully. How should I realize that?
...ANSWER
Answered 2021-Sep-30 at 13:01You need to add pre=True to your validator:
QUESTION
I have two CSV files. One that contains Vendor data and one that contains Employee data. Similar to what "Fuzzy Lookup" in excel does, I'm looking to do two types of matches and output all columns from both csv files, including a new column as the similarity ratio for each row. In excel, I would use a 0.80 threshold. The below is sample data and my actual data has 2 million rows in one of the files which is going to be a nightmare if done in excel.
Output 1: From Vendor file, fuzzy match "Vendor Name" with "Employee Name" from Employee file. Display all columns from both files and a new column for Similarity Ratio
Output 2: From Vendor file, fuzzy match "SSN" with "SSN" from Employee file. Display all columns from both files and a new column for Similarity Ratio
These are two separate outputs
Dataframe 1: Vendor Data
Company Vendor ID Vendor Name Invoice Number Transaction Amt Vendor Type SSN 15 58421 CLIFFORD BROWN 854 500 Misc 668419628 150 9675 GREEN 7412 70 One Time 774801971 200 15789 SMITH, JOHN 80 40 Employee 965214872 200 69997 HAROON, SIMAN 964 100 Misc 741-98-7821Dataframe 2: Employee Data
Employee Name Employee ID Manager SSN BROWN, CLIFFORD 1 Manager 1 668-419-628 BLUE, CITY 2 Manager 2 874126487 SMITH, JOHN 3 Manager 3 965-21-4872 HAROON, SIMON 4 Manager 4 741-98-7820Expected output 1 - Match Name
Employee Name Employee ID Manager SSN Company Vendor ID Vendor Name Invoice Number Transaction Amt Vendor Type SSN Similarity Ratio BROWN, CLIFFORD 1 Manager 1 668-419-628 150 58421 CLIFFORD BROWN 854 500 Misc 668419628 1.00 SMITH, JOHN 3 Manager 3 965-21-4872 200 15789 SMITH, JOHN 80 40 Employee 965214872 1.00 HAROON, SIMON 4 Manager 4 741-98-7820 200 69997 HAROON, SIMAN 964 100 Misc 741-98-7821 0.96 BLUE, CITY 2 Manager 2 874126487 0.00Expected output 2 - Match SSN
Employee Name Employee ID Manager SSN Company Vendor ID Vendor Name Invoice Number Transaction Amt Vendor Type SSN Similarity Ratio BROWN, CLIFFORD 1 Manager 1 668-419-628 150 58421 CLIFFORD, BROWN 854 500 Misc 668419628 0.97 SMITH, JOHN 3 Manager 3 965-21-4872 200 15789 SMITH, JOHN 80 40 Employee 965214872 0.97 BLUE, CITY 2 Manager 2 874126487 0.00 HAROON, SIMON 4 Manager 4 741-98-7820 0.00I've tried the below code:
...ANSWER
Answered 2021-Sep-24 at 06:03To concatenate the two DataFrames horizontally, I aligned the Employees DataFrame by the index of the matched Vendor Name. If no Vendor Name was matched, I just put an empty row instead.
In more details:
- I iterated over the vendor names, and for each vendor name, I added the index of the employee name with the highest score to a list of indices. Note that I added at most one matched employee record to each vendor name.
- If no match was found (too low score), I added the index of an empty record that I have added manually to the Employees Dataframe.
- This list of indices is then used to reorder the Employees DataDrame.
- at last, I just merge the two DataFrame horizontally. Note that the two DataFrames at this point doesn't have to be of the same size, but in such a case, the
concatmethod just fill the gap with appending missing rows to the smaller DataFrame.
The code is as follows:
QUESTION
I have a list of objects. Each object contains a list of categories as a comma delimited string. I want to know how many objects i have for each category. For this i think i need to group by the categories and then count the entries - however i can't wrap my head around grouping by a list.
...ANSWER
Answered 2021-Sep-09 at 07:31One option is to use SelectMany to flatten categories and transform the dtos in key-value pairs (I use valu tuples to store them):
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