code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def command_indices(self, cmd):
"""indices TABLE: Lists all indices on table TABLE
"""
if len(cmd)!=1:
raise self.Error("indices takes one table name")
self.push_output()
self.header=False
self.output=self.output_list
try:
self.process_sql... | indices TABLE: Lists all indices on table TABLE
| command_indices | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_load(self, cmd):
"""load FILE ?ENTRY?: Loads a SQLite extension library
Note: Extension loading may not be enabled in the SQLite
library version you are using.
Extensions are an easy way to add new functions and
functionality. For a useful extension look at the bot... | load FILE ?ENTRY?: Loads a SQLite extension library
Note: Extension loading may not be enabled in the SQLite
library version you are using.
Extensions are an easy way to add new functions and
functionality. For a useful extension look at the bottom of
https://sqlite.org/contri... | command_load | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_mode(self, cmd):
"""mode MODE ?TABLE?: Sets output mode to one of"""
if len(cmd) in (1,2):
w=cmd[0]
if w=="tabs":
w="list"
m=getattr(self, "output_"+w, None)
if w!="insert":
if len(cmd)==2:
ra... | mode MODE ?TABLE?: Sets output mode to one of | command_mode | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_nullvalue(self, cmd):
"""nullvalue STRING: Print STRING in place of null values
This affects textual output modes like column and list and
sets how SQL null values are shown. The default is a zero
length string. Insert mode and dumps are not affected by this
settin... | nullvalue STRING: Print STRING in place of null values
This affects textual output modes like column and list and
sets how SQL null values are shown. The default is a zero
length string. Insert mode and dumps are not affected by this
setting. You can use double quotes to supply a zer... | command_nullvalue | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_output(self, cmd):
"""output FILENAME: Send output to FILENAME (or stdout)
If the FILENAME is stdout then output is sent to standard
output from when the shell was started. The file is opened
using the current encoding (change with .encoding command).
"""
# ... | output FILENAME: Send output to FILENAME (or stdout)
If the FILENAME is stdout then output is sent to standard
output from when the shell was started. The file is opened
using the current encoding (change with .encoding command).
| command_output | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_prompt(self, cmd):
"""prompt MAIN ?CONTINUE?: Changes the prompts for first line and continuation lines
The default is to print 'sqlite> ' for the main prompt where
you can enter a dot command or a SQL statement. If the SQL
statement is complete (eg not ; terminated) then y... | prompt MAIN ?CONTINUE?: Changes the prompts for first line and continuation lines
The default is to print 'sqlite> ' for the main prompt where
you can enter a dot command or a SQL statement. If the SQL
statement is complete (eg not ; terminated) then you are
prompted for more using the... | command_prompt | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_read(self, cmd):
"""read FILENAME: Processes SQL and commands in FILENAME (or Python if FILENAME ends with .py)
Treats the specified file as input (a mixture or SQL and/or
dot commands). If the filename ends in .py then it is treated
as Python code instead.
For Pyt... | read FILENAME: Processes SQL and commands in FILENAME (or Python if FILENAME ends with .py)
Treats the specified file as input (a mixture or SQL and/or
dot commands). If the filename ends in .py then it is treated
as Python code instead.
For Python code the symbol 'shell' refers to th... | command_read | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_restore(self, cmd):
"""restore ?DB? FILE: Restore database from FILE into DB (default "main")
Copies the contents of FILE to the current database (default "main").
The backup is done at the page level - SQLite copies the pages as
is. There is no round trip through SQL code.... | restore ?DB? FILE: Restore database from FILE into DB (default "main")
Copies the contents of FILE to the current database (default "main").
The backup is done at the page level - SQLite copies the pages as
is. There is no round trip through SQL code.
| command_restore | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_schema(self, cmd):
"""schema ?TABLE? [TABLE...]: Shows SQL for table
If you give one or more tables then their schema is listed
(including indices). If you don't specify any then all
schemas are listed. TABLE is a like pattern so you can % for
wildcards.
"""... | schema ?TABLE? [TABLE...]: Shows SQL for table
If you give one or more tables then their schema is listed
(including indices). If you don't specify any then all
schemas are listed. TABLE is a like pattern so you can % for
wildcards.
| command_schema | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_separator(self, cmd):
"""separator STRING: Change separator for output mode and .import
You can use quotes and backslashes. For example to set the
separator to space tab space you can use:
.separator " \\t "
The setting is automatically changed when you switch t... | separator STRING: Change separator for output mode and .import
You can use quotes and backslashes. For example to set the
separator to space tab space you can use:
.separator " \t "
The setting is automatically changed when you switch to csv or
tabs output mode. You should... | command_separator | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_show(self, cmd):
"""show: Show the current values for various settings."""
if len(cmd)>1:
raise self.Error("show takes at most one parameter")
if len(cmd):
what=cmd[0]
if what not in self._shows:
raise self.Error("Unknown show: '%s'... | show: Show the current values for various settings. | command_show | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_tables(self, cmd):
"""tables ?PATTERN?: Lists names of tables matching LIKE pattern
This also returns views.
"""
self.push_output()
self.output=self.output_list
self.header=False
try:
if len(cmd)==0:
cmd=['%']
... | tables ?PATTERN?: Lists names of tables matching LIKE pattern
This also returns views.
| command_tables | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_timeout(self, cmd):
"""timeout MS: Try opening locked tables for MS milliseconds
If a database is locked by another process SQLite will keep
retrying. This sets how many thousandths of a second it will
keep trying for. If you supply zero or a negative number then
a... | timeout MS: Try opening locked tables for MS milliseconds
If a database is locked by another process SQLite will keep
retrying. This sets how many thousandths of a second it will
keep trying for. If you supply zero or a negative number then
all busy handlers are disabled.
| command_timeout | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_timer(self, cmd):
"""timer ON|OFF: Control printing of time and resource usage after each query
The values displayed are in seconds when shown as floating
point or an absolute count. Only items that have changed
since starting the query are shown. On non-Windows platforms
... | timer ON|OFF: Control printing of time and resource usage after each query
The values displayed are in seconds when shown as floating
point or an absolute count. Only items that have changed
since starting the query are shown. On non-Windows platforms
considerably more information can... | command_timer | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def command_width(self, cmd):
"""width NUM NUM ...: Set the column widths for "column" mode
In "column" output mode, each column is a fixed width with values truncated to
fit. Specify new widths using this command. Use a negative number
to right justify and zero for default column wid... | width NUM NUM ...: Set the column widths for "column" mode
In "column" output mode, each column is a fixed width with values truncated to
fit. Specify new widths using this command. Use a negative number
to right justify and zero for default column width.
| command_width | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def _terminal_width(self):
"""Works out the terminal width which is used for word wrapping
some output (eg .help)"""
try:
if sys.platform=="win32":
import ctypes, struct
h=ctypes.windll.kernel32.GetStdHandle(-12) # -12 is stderr
buf=cty... | Works out the terminal width which is used for word wrapping
some output (eg .help) | _terminal_width | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def push_output(self):
"""Saves the current output settings onto a stack. See
:meth:`pop_output` for more details as to why you would use
this."""
o={}
for k in "separator", "header", "nullvalue", "output", "widths", "truncate":
o[k]=getattr(self, k)
self._ou... | Saves the current output settings onto a stack. See
:meth:`pop_output` for more details as to why you would use
this. | push_output | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def pop_output(self):
"""Restores most recently pushed output. There are many
output parameters such as nullvalue, mode
(list/tcl/html/insert etc), column widths, header etc. If you
temporarily need to change some settings then
:meth:`push_output`, change the settings and then ... | Restores most recently pushed output. There are many
output parameters such as nullvalue, mode
(list/tcl/html/insert etc), column widths, header etc. If you
temporarily need to change some settings then
:meth:`push_output`, change the settings and then pop the old
ones back.
... | pop_output | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def _append_input_description(self):
"""When displaying an error in :meth:`handle_exception` we
want to give context such as when the commands being executed
came from a .read command (which in turn could execute another
.read).
"""
if self.interactive:
return... | When displaying an error in :meth:`handle_exception` we
want to give context such as when the commands being executed
came from a .read command (which in turn could execute another
.read).
| _append_input_description | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def fixup_backslashes(self, s):
"""Implements the various backlash sequences in s such as
turning backslash t into a tab.
This function is needed because shlex does not do it for us.
"""
if "\\" not in s: return s
# See the resolve_backslashes function in SQLite shell so... | Implements the various backlash sequences in s such as
turning backslash t into a tab.
This function is needed because shlex does not do it for us.
| fixup_backslashes | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def write(self, dest, text):
"""Writes text to dest. dest will typically be one of self.stdout or self.stderr."""
# ensure text is unicode to catch codeset issues here
if type(text)!=unicode:
text=unicode(text)
try:
dest.write(text)
... | Writes text to dest. dest will typically be one of self.stdout or self.stderr. | write | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def getline(self, prompt=""):
"""Returns a single line of input (may be incomplete SQL) from self.stdin.
If EOF is reached then return None. Do not include trailing
newline in return.
"""
self.stdout.flush()
self.stderr.flush()
try:
if self.interacti... | Returns a single line of input (may be incomplete SQL) from self.stdin.
If EOF is reached then return None. Do not include trailing
newline in return.
| getline | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def getcompleteline(self):
"""Returns a complete input.
For dot commands it will be one line. For SQL statements it
will be as many as is necessary to have a
:meth:`~apsw.complete` statement (ie semicolon terminated).
Returns None on end of file."""
try:
sel... | Returns a complete input.
For dot commands it will be one line. For SQL statements it
will be as many as is necessary to have a
:meth:`~apsw.complete` statement (ie semicolon terminated).
Returns None on end of file. | getcompleteline | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def handle_interrupt(self):
"""Deal with keyboard interrupt (typically Control-C). It
will :meth:`~Connection.interrupt` the database and print"^C" if interactive."""
self.db.interrupt()
if not self.bail and self.interactive:
self.write(self.stderr, "^C\n")
retur... | Deal with keyboard interrupt (typically Control-C). It
will :meth:`~Connection.interrupt` the database and print"^C" if interactive. | handle_interrupt | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def process_complete_line(self, command):
"""Given some text will call the appropriate method to process
it (eg :meth:`process_sql` or :meth:`process_command`)"""
try:
if len(command.strip())==0:
return
if command[0]==".":
self.process_comm... | Given some text will call the appropriate method to process
it (eg :meth:`process_sql` or :meth:`process_command`) | process_complete_line | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def push_input(self):
"""Saves the current input parameters to a stack. See :meth:`pop_input`."""
d={}
for i in "interactive", "stdin", "input_line_number":
d[i]=getattr(self, i)
self._input_stack.append(d) | Saves the current input parameters to a stack. See :meth:`pop_input`. | push_input | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def pop_input(self):
"""Restore most recently pushed input parameters (interactive,
self.stdin, linenumber etc). Use this if implementing a
command like read. Push the current input, read the file and
then pop the input to go back to before.
"""
assert(len(self._input_s... | Restore most recently pushed input parameters (interactive,
self.stdin, linenumber etc). Use this if implementing a
command like read. Push the current input, read the file and
then pop the input to go back to before.
| pop_input | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def complete(self, token, state):
"""Return a possible completion for readline
This function is called with state starting at zero to get the
first completion, then one/two/three etc until you return None. The best
implementation is to generate the list when state==0, save it,
... | Return a possible completion for readline
This function is called with state starting at zero to get the
first completion, then one/two/three etc until you return None. The best
implementation is to generate the list when state==0, save it,
and provide members on each increase.
... | complete | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def _get_prev_tokens(self, line, end):
"Returns the tokens prior to pos end in the line"
return re.findall(r'"?\w+"?', line[:end]) | Returns the tokens prior to pos end in the line | _get_prev_tokens | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def complete_sql(self, line, token, beg, end):
"""Provide some completions for SQL
:param line: The current complete input line
:param token: The word readline is looking for matches
:param beg: Integer offset of token in line
:param end: Integer end of token in line
:re... | Provide some completions for SQL
:param line: The current complete input line
:param token: The word readline is looking for matches
:param beg: Integer offset of token in line
:param end: Integer end of token in line
:return: A list of completions, or an empty list if none
... | complete_sql | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def complete_command(self, line, token, beg, end):
"""Provide some completions for dot commands
:param line: The current complete input line
:param token: The word readline is looking for matches
:param beg: Integer offset of token in line
:param end: Integer end of token in lin... | Provide some completions for dot commands
:param line: The current complete input line
:param token: The word readline is looking for matches
:param beg: Integer offset of token in line
:param end: Integer end of token in line
:return: A list of completions, or an empty list if ... | complete_command | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def get_resource_usage(self):
"""Return a dict of various numbers (ints or floats). The
.timer command shows the difference between before and after
results of what this returns by calling :meth:`display_timing`"""
if sys.platform=="win32":
import ctypes, time, platform
... | Return a dict of various numbers (ints or floats). The
.timer command shows the difference between before and after
results of what this returns by calling :meth:`display_timing` | get_resource_usage | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def display_timing(self, b4, after):
"""Writes the difference between b4 and after to self.stderr.
The data is dictionaries returned from
:meth:`get_resource_usage`."""
v=list(b4.keys())
for i in after:
if i not in v:
v.append(i)
v.sort()
... | Writes the difference between b4 and after to self.stderr.
The data is dictionaries returned from
:meth:`get_resource_usage`. | display_timing | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def main():
# Docstring must start on second line so dedenting works correctly
"""
Call this to run the interactive shell. It automatically passes
in sys.argv[1:] and exits Python when done.
"""
try:
s=Shell()
_,_,cmds=s.process_args(sys.argv[1:])
if len(cmds)==0:
... |
Call this to run the interactive shell. It automatically passes
in sys.argv[1:] and exits Python when done.
| main | python | plasticityai/magnitude | pymagnitude/third_party/_apsw/tools/shell.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_apsw/tools/shell.py | MIT |
def system_with_status_and_output(
command, attach_env=True, should_silence_err=False,
exit_with_error_message=None, _stdout=PIPE):
"""Runs a system command with various options and returns
the status of the command and the output of the command.
"""
if should_silence_err:
stderr... | Runs a system command with various options and returns
the status of the command and the output of the command.
| system_with_status_and_output | python | plasticityai/magnitude | pymagnitude/third_party/_pysqlite/setup.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_pysqlite/setup.py | MIT |
def system_with_output(
command, attach_env=True, should_silence_err=False,
exit_with_error_message=None):
"""Runs a system command with various options and returns
the output of the command.
"""
return system_with_status_and_output(
command, attach_env, should_silence_err, exit_... | Runs a system command with various options and returns
the output of the command.
| system_with_output | python | plasticityai/magnitude | pymagnitude/third_party/_pysqlite/setup.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_pysqlite/setup.py | MIT |
def _iterdump(connection):
"""
Returns an iterator to the dump of the database in an SQL text format.
Used to produce an SQL dump of the database. Useful to save an in-memory
database for later restoration. This function should not be called
directly but instead called from the Connection method,... |
Returns an iterator to the dump of the database in an SQL text format.
Used to produce an SQL dump of the database. Useful to save an in-memory
database for later restoration. This function should not be called
directly but instead called from the Connection method, iterdump().
| _iterdump | python | plasticityai/magnitude | pymagnitude/third_party/_pysqlite/lib/dump.py | https://github.com/plasticityai/magnitude/blob/master/pymagnitude/third_party/_pysqlite/lib/dump.py | MIT |
def get_size(obj, seen=None):
"""Recursively finds size of objects
Source: https://goshippo.com/blog/measure-real-size-any-python-object/
"""
size = sys.getsizeof(obj)
if seen is None:
seen = set()
obj_id = id(obj)
if obj_id in seen:
return 0
# Important mark as seen *... | Recursively finds size of objects
Source: https://goshippo.com/blog/measure-real-size-any-python-object/
| get_size | python | plasticityai/magnitude | tests/benchmark.py | https://github.com/plasticityai/magnitude/blob/master/tests/benchmark.py | MIT |
def get_channel_dim(input_tensor, data_format='INVALID'):
"""Returns the number of channels in the input tensor."""
shape = input_tensor.get_shape().as_list()
assert data_format != 'INVALID'
assert len(shape) == 4
if data_format == 'NHWC':
return int(shape[3])
elif data_format == 'NCHW':
return int(... | Returns the number of channels in the input tensor. | get_channel_dim | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def block_build(self, input_tensors, is_training, lengths=None, hparams=None):
"""Builds a block for phoenix.
Args:
input_tensors: A list of input tensors.
is_training: Whether we are training. Used for regularization.
lengths: The lengths of the input sequences in the batch.
hparams: T... | Builds a block for phoenix.
Args:
input_tensors: A list of input tensors.
is_training: Whether we are training. Used for regularization.
lengths: The lengths of the input sequences in the batch.
hparams: The training HParams.
Returns:
output_tensors: A list of the output tensors.... | block_build | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def is_input_order_important(self):
"""Is the order of the entries in the input tensor important.
Returns:
A bool specifying if the order of the entries in the input is important.
Examples where the order is important: Input for a cnn layer.
(e.g., pixels an image). Examples when the order is... | Is the order of the entries in the input tensor important.
Returns:
A bool specifying if the order of the entries in the input is important.
Examples where the order is important: Input for a cnn layer.
(e.g., pixels an image). Examples when the order is not important:
Input for a dense lay... | is_input_order_important | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def __init__(self,
max_output_size=100,
max_number_of_parameters=None,
apply_batch_norm=False,
residual_connection_type=None,
**kwargs):
"""Initializes a new FullyConnectedBlock instance.
Args:
max_output_size: The maximum number ... | Initializes a new FullyConnectedBlock instance.
Args:
max_output_size: The maximum number of output neurons.
max_number_of_parameters: The maximum number of parameters allowed.
apply_batch_norm: Whether to apply batch normalization to the layer.
residual_connection_type: The ResidualConnect... | __init__ | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def _add_residual_connection(self, input_tensor, output_tensor):
"""Creates the residual connection between the input and the output."""
if self._residual_connection_type == ResidualConnectionType.NONE:
return output_tensor
in_shape = input_tensor.shape[-1]
out_shape = output_tensor.shape[-1]
... | Creates the residual connection between the input and the output. | _add_residual_connection | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def block_build(self, input_tensors, is_training, lengths=None, hparams=None):
"""Applies 2d max pooling on the input tensor."""
input_tensor = input_tensors[-1]
if input_tensor.get_shape().as_list()[2] < self._pool_size:
return input_tensors
max_pool = tf.keras.layers.MaxPool2D(
pool_siz... | Applies 2d max pooling on the input tensor. | block_build | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def block_build(self, input_tensors, is_training, lengths=None, hparams=None):
"""Returns a ReLU activated output of a residual unit with 2 sub layers."""
input_tensor = input_tensors[-1]
net1 = tf.keras.layers.Conv2D(
get_channel_dim(input_tensor),
kernel_size=self._kernel_size,
nam... | Returns a ReLU activated output of a residual unit with 2 sub layers. | block_build | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def block_build(self, input_tensors, is_training, lengths=None, hparams=None):
"""Custom (wide) convolution block with some pooling."""
# Guard so that we won't have zero channels
input_tensor = input_tensors[-1]
if get_channel_dim(input_tensor) < 6:
return input_tensors
reduced = tf.keras.la... | Custom (wide) convolution block with some pooling. | block_build | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def block_build(self, input_tensors, is_training, lengths=None, hparams=None):
"""Builds a basic rnn block.
Args:
input_tensors: A tf.Tensor with the input.
is_training: Whether we are training. Used for regularization.
lengths: The lengths of the input sequences in the batch.
hparams: ... | Builds a basic rnn block.
Args:
input_tensors: A tf.Tensor with the input.
is_training: Whether we are training. Used for regularization.
lengths: The lengths of the input sequences in the batch.
hparams: hparams for the build.
Returns:
output tensor
| block_build | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def block_build(self, input_tensors, is_training, lengths=None, hparams=None):
"""Builds a one dimensional convolutional block.
Args:
input_tensors: A tf.Tensor with the input.
is_training: Whether we are training. Used for regularization.
lengths: The lengths of the input sequences in the ba... | Builds a one dimensional convolutional block.
Args:
input_tensors: A tf.Tensor with the input.
is_training: Whether we are training. Used for regularization.
lengths: The lengths of the input sequences in the batch.
hparams: hparams for the build.
Returns:
output tensor
| block_build | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def block_build(self, input_tensors, is_training, lengths=None, hparams=None):
"""Builds as LSTM block.
Args:
input_tensors: A list of tf.Tensors with the input.
is_training: Whether we are training. Used for regularization.
lengths: The lengths of the input sequences in the batch.
hpar... | Builds as LSTM block.
Args:
input_tensors: A list of tf.Tensors with the input.
is_training: Whether we are training. Used for regularization.
lengths: The lengths of the input sequences in the batch.
hparams: hparams for the build.
Returns:
output tensor
| block_build | python | google/model_search | model_search/block.py | https://github.com/google/model_search/blob/master/model_search/block.py | Apache-2.0 |
def search_space(blocks_to_use=None):
"""Returns required search space for all blocks."""
search_space = ms_hparameters.Hyperparameters()
for block_type in BlockType:
if block_type == BlockType.EMPTY_BLOCK:
continue
if blocks_to_use is None or block_type.name in blocks_to_use:
ta... | Returns required search space for all blocks. | search_space | python | google/model_search | model_search/block_builder.py | https://github.com/google/model_search/blob/master/model_search/block_builder.py | Apache-2.0 |
def replay_is_training_a_tower(self, my_id):
"""Returns True if we are training a new tower in a replay run.
Example:
1. In adaptive ensembling, every trial is training one new tower, so the
return value is always True.
2. In a non-adaptive ensembling, every trial except the last one is
... | Returns True if we are training a new tower in a replay run.
Example:
1. In adaptive ensembling, every trial is training one new tower, so the
return value is always True.
2. In a non-adaptive ensembling, every trial except the last one is
training a new tower, whereas the last trial just e... | replay_is_training_a_tower | python | google/model_search | model_search/controller.py | https://github.com/google/model_search/blob/master/model_search/controller.py | Apache-2.0 |
def replay_is_importing_towers(self, my_id):
"""Returns true if we are importing a tower in this replay trial.
Examples:
1. For adaptive ensembling, we import towers for every trial with id
greater than 1.
2. For non-adaptive ensembling, we import towers only in the last trial.
Args:
... | Returns true if we are importing a tower in this replay trial.
Examples:
1. For adaptive ensembling, we import towers for every trial with id
greater than 1.
2. For non-adaptive ensembling, we import towers only in the last trial.
Args:
my_id: trial id.
Returns:
True if we a... | replay_is_importing_towers | python | google/model_search | model_search/controller.py | https://github.com/google/model_search/blob/master/model_search/controller.py | Apache-2.0 |
def _return_generators(generators):
"""Sets the number of towers to zero when generator isn't used."""
for generator_name in base_tower_generator.ALL_GENERATORS:
if generator_name not in generators.keys():
architecture_utils.set_number_of_towers(generator_name, 0)
return generators | Sets the number of towers to zero when generator isn't used. | _return_generators | python | google/model_search | model_search/controller.py | https://github.com/google/model_search/blob/master/model_search/controller.py | Apache-2.0 |
def get_generators(self, my_id, all_trials):
"""Returns the `Dict` of generators that need to be triggered.
Args:
my_id: an int with the current trial id.
all_trials: a list of metadata.trial.Trial protos with all information in
the current study.
Returns:
A dict of generator nam... | Returns the `Dict` of generators that need to be triggered.
Args:
my_id: an int with the current trial id.
all_trials: a list of metadata.trial.Trial protos with all information in
the current study.
Returns:
A dict of generator names as keys and GeneratorWithTrials as values.
| get_generators | python | google/model_search | model_search/controller.py | https://github.com/google/model_search/blob/master/model_search/controller.py | Apache-2.0 |
def bundle_logits(self, priors_logits_specs, search_logits_specs,
logits_dimension):
"""Bundles the priors and the search candidate into an ensemble."""
all_specs = priors_logits_specs + search_logits_specs
assert all_specs, "Got no logits specs from both generators."
with tf.compa... | Bundles the priors and the search candidate into an ensemble. | bundle_logits | python | google/model_search | model_search/ensembler.py | https://github.com/google/model_search/blob/master/model_search/ensembler.py | Apache-2.0 |
def as_text(bytes_or_text, encoding='utf-8'):
"""Converts any string-like python input types to unicode.
Returns the input as a unicode string. Uses utf-8 encoding for text
by default.
Args:
bytes_or_text: A `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for decoding un... | Converts any string-like python input types to unicode.
Returns the input as a unicode string. Uses utf-8 encoding for text
by default.
Args:
bytes_or_text: A `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for decoding unicode.
Returns:
A `unicode` (Python 2) or `s... | as_text | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def as_bytes(bytes_or_text, encoding='utf-8'):
"""Converts `bytearray`, `bytes`, or unicode python input types to `bytes`.
Uses utf-8 encoding for text by default.
Args:
bytes_or_text: A `bytearray`, `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for encoding unicode.
... | Converts `bytearray`, `bytes`, or unicode python input types to `bytes`.
Uses utf-8 encoding for text by default.
Args:
bytes_or_text: A `bytearray`, `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for encoding unicode.
Returns:
A `bytes` object.
Raises:
TypeEr... | as_bytes | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def _parse_fail(name, var_type, value, values):
"""Helper function for raising a value error for bad assignment."""
raise ValueError(
'Could not parse hparam \'%s\' of type \'%s\' with value \'%s\' in %s' %
(name, var_type.__name__, value, values)) | Helper function for raising a value error for bad assignment. | _parse_fail | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def _process_scalar_value(name, parse_fn, var_type, m_dict, values,
results_dictionary):
"""Update results_dictionary with a scalar value.
Used to update the results_dictionary to be returned by parse_values when
encountering a clause with a scalar RHS (e.g. "s=5" or "arr[0]=5".)
Mu... | Update results_dictionary with a scalar value.
Used to update the results_dictionary to be returned by parse_values when
encountering a clause with a scalar RHS (e.g. "s=5" or "arr[0]=5".)
Mutates results_dictionary.
Args:
name: Name of variable in assignment ("s" or "arr").
parse_fn: Function for p... | _process_scalar_value | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def _process_list_value(name, parse_fn, var_type, m_dict, values,
results_dictionary):
"""Update results_dictionary from a list of values.
Used to update results_dictionary to be returned by parse_values when
encountering a clause with a list RHS (e.g. "arr=[1,2,3]".)
Mutates results_... | Update results_dictionary from a list of values.
Used to update results_dictionary to be returned by parse_values when
encountering a clause with a list RHS (e.g. "arr=[1,2,3]".)
Mutates results_dictionary.
Args:
name: Name of variable in assignment ("arr").
parse_fn: Function for parsing individual... | _process_list_value | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def _cast_to_type_if_compatible(name, param_type, value):
"""Cast hparam to the provided type, if compatible.
Args:
name: Name of the hparam to be cast.
param_type: The type of the hparam.
value: The value to be cast, if compatible.
Returns:
The result of casting `value` to `param_type`.
Rais... | Cast hparam to the provided type, if compatible.
Args:
name: Name of the hparam to be cast.
param_type: The type of the hparam.
value: The value to be cast, if compatible.
Returns:
The result of casting `value` to `param_type`.
Raises:
ValueError: If the type of `value` is not compatible wi... | _cast_to_type_if_compatible | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def parse_values(values, type_map, ignore_unknown=False):
"""Parses hyperparameter values from a string into a python map.
`values` is a string containing comma-separated `name=value` pairs.
For each pair, the value of the hyperparameter named `name` is set to
`value`.
If a hyperparameter name appears multi... | Parses hyperparameter values from a string into a python map.
`values` is a string containing comma-separated `name=value` pairs.
For each pair, the value of the hyperparameter named `name` is set to
`value`.
If a hyperparameter name appears multiple times in `values`, a ValueError
is raised (e.g. 'a=1,a=2'... | parse_values | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def __init__(self, hparam_def=None, model_structure=None, **kwargs):
"""Create an instance of `HParams` from keyword arguments.
The keyword arguments specify name-values pairs for the hyperparameters.
The parameter types are inferred from the type of the values passed.
The parameter names are added as... | Create an instance of `HParams` from keyword arguments.
The keyword arguments specify name-values pairs for the hyperparameters.
The parameter types are inferred from the type of the values passed.
The parameter names are added as attributes of `HParams` object, so they
can be accessed directly with t... | __init__ | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def _init_from_proto(self, hparam_def):
"""Creates a new HParams from `HParamDef` protocol buffer.
Args:
hparam_def: `HParamDef` protocol buffer.
"""
assert isinstance(hparam_def, hparam_pb2.HParamDef)
for name, value in hparam_def.hparam.items():
kind = value.WhichOneof('kind')
i... | Creates a new HParams from `HParamDef` protocol buffer.
Args:
hparam_def: `HParamDef` protocol buffer.
| _init_from_proto | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def add_hparam(self, name, value):
"""Adds {name, value} pair to hyperparameters.
Args:
name: Name of the hyperparameter.
value: Value of the hyperparameter. Can be one of the following types:
int, float, string, int list, float list, or string list.
Raises:
ValueError: if one of... | Adds {name, value} pair to hyperparameters.
Args:
name: Name of the hyperparameter.
value: Value of the hyperparameter. Can be one of the following types:
int, float, string, int list, float list, or string list.
Raises:
ValueError: if one of the arguments is invalid.
| add_hparam | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def set_hparam(self, name, value):
"""Set the value of an existing hyperparameter.
This function verifies that the type of the value matches the type of the
existing hyperparameter.
Args:
name: Name of the hyperparameter.
value: New value of the hyperparameter.
Raises:
KeyError:... | Set the value of an existing hyperparameter.
This function verifies that the type of the value matches the type of the
existing hyperparameter.
Args:
name: Name of the hyperparameter.
value: New value of the hyperparameter.
Raises:
KeyError: If the hyperparameter doesn't exist.
... | set_hparam | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def del_hparam(self, name):
"""Removes the hyperparameter with key 'name'.
Does nothing if it isn't present.
Args:
name: Name of the hyperparameter.
"""
if hasattr(self, name):
delattr(self, name)
del self._hparam_types[name] | Removes the hyperparameter with key 'name'.
Does nothing if it isn't present.
Args:
name: Name of the hyperparameter.
| del_hparam | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def parse(self, values):
"""Override existing hyperparameter values, parsing new values from a string.
See parse_values for more detail on the allowed format for values.
Args:
values: String. Comma separated list of `name=value` pairs where 'value'
must follow the syntax described above.
... | Override existing hyperparameter values, parsing new values from a string.
See parse_values for more detail on the allowed format for values.
Args:
values: String. Comma separated list of `name=value` pairs where 'value'
must follow the syntax described above.
Returns:
The `HParams` ... | parse | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def override_from_dict(self, values_dict):
"""Override existing hyperparameter values, parsing new values from a dictionary.
Args:
values_dict: Dictionary of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_dict` doesn't exist.
... | Override existing hyperparameter values, parsing new values from a dictionary.
Args:
values_dict: Dictionary of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_dict` doesn't exist.
ValueError: If `values_dict` cannot be parsed.
... | override_from_dict | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def to_json(self, indent=None, separators=None, sort_keys=False):
"""Serializes the hyperparameters into JSON.
Args:
indent: If a non-negative integer, JSON array elements and object members
will be pretty-printed with that indent level. An indent level of 0, or
negative, will only insert... | Serializes the hyperparameters into JSON.
Args:
indent: If a non-negative integer, JSON array elements and object members
will be pretty-printed with that indent level. An indent level of 0, or
negative, will only insert newlines. `None` (the default) selects the
most compact represen... | to_json | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def get(self, key, default=None):
"""Returns the value of `key` if it exists, else `default`."""
if key in self._hparam_types:
# Ensure that default is compatible with the parameter type.
if default is not None:
param_type, is_param_list = self._hparam_types[key]
type_str = 'list<%s>... | Returns the value of `key` if it exists, else `default`. | get | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def _get_kind_name(param_type, is_list):
"""Returns the field name given parameter type and is_list.
Args:
param_type: Data type of the hparam.
is_list: Whether this is a list.
Returns:
A string representation of the field name.
Raises:
ValueError: If parameter type is not rec... | Returns the field name given parameter type and is_list.
Args:
param_type: Data type of the hparam.
is_list: Whether this is a list.
Returns:
A string representation of the field name.
Raises:
ValueError: If parameter type is not recognized.
| _get_kind_name | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def to_proto(self, export_scope=None): # pylint: disable=unused-argument
"""Converts a `HParams` object to a `HParamDef` protocol buffer.
Args:
export_scope: Optional `string`. Name scope to remove.
Returns:
A `HParamDef` protocol buffer.
"""
hparam_proto = hparam_pb2.HParamDef()
... | Converts a `HParams` object to a `HParamDef` protocol buffer.
Args:
export_scope: Optional `string`. Name scope to remove.
Returns:
A `HParamDef` protocol buffer.
| to_proto | python | google/model_search | model_search/hparam.py | https://github.com/google/model_search/blob/master/model_search/hparam.py | Apache-2.0 |
def bundle_logits(self,
priors_logits_specs,
search_logits_specs,
logits_dimension=None):
"""Bundles the logits from the priors and the search candidate.
Args:
priors_logits_specs: List of LogitSpecs associated with the prior towers.
searc... | Bundles the logits from the priors and the search candidate.
Args:
priors_logits_specs: List of LogitSpecs associated with the prior towers.
search_logits_specs: List containing the LogitSpecs associated with the
search (new) tower. (Empty if there is no search tower.)
logits_dimension: T... | bundle_logits | python | google/model_search | model_search/logit_bundler.py | https://github.com/google/model_search/blob/master/model_search/logit_bundler.py | Apache-2.0 |
def make_regression_loss_fn():
"""Returns the Mean Squared Error loss_fn for regression."""
def _loss_fn(labels, logits, weights=1.0):
return tf.compat.v1.losses.mean_squared_error(
labels=labels,
predictions=logits,
weights=weights,
reduction=tf.compat.v1.losses.Reduction.SUM_O... | Returns the Mean Squared Error loss_fn for regression. | make_regression_loss_fn | python | google/model_search | model_search/loss_fns.py | https://github.com/google/model_search/blob/master/model_search/loss_fns.py | Apache-2.0 |
def make_regression_absolute_difference_loss_fn():
"""Returns the Mean Average Error loss_fn for regression."""
def _loss_fn(labels, logits, weights=1.0):
return tf.compat.v1.losses.absolute_difference(
labels=labels,
predictions=logits,
weights=weights,
reduction=tf.compat.v1.l... | Returns the Mean Average Error loss_fn for regression. | make_regression_absolute_difference_loss_fn | python | google/model_search | model_search/loss_fns.py | https://github.com/google/model_search/blob/master/model_search/loss_fns.py | Apache-2.0 |
def make_regression_logarithmic_loss_fn():
"""Returns Mean Squared Logarithmic Error loss_fn for regression."""
def _loss_fn(labels, logits, weights=1.0):
return tf.compat.v1.losses.mean_squared_error(
labels=tf.math.log1p(tf.nn.relu(labels)),
predictions=logits,
weights=weights,
... | Returns Mean Squared Logarithmic Error loss_fn for regression. | make_regression_logarithmic_loss_fn | python | google/model_search | model_search/loss_fns.py | https://github.com/google/model_search/blob/master/model_search/loss_fns.py | Apache-2.0 |
def make_accuracy_metric_fn(label_vocabulary=None):
"""Makes a metric_fn for accuracy from an optional label_vocabulary.
Args:
label_vocabulary: A 1-D string Tensor or string list (in the single task
setup); or a dictionary mapping string keys to those (in the multi task
setup). The string keys cor... | Makes a metric_fn for accuracy from an optional label_vocabulary.
Args:
label_vocabulary: A 1-D string Tensor or string list (in the single task
setup); or a dictionary mapping string keys to those (in the multi task
setup). The string keys correspond to the task name, allowing different
tasks ... | make_accuracy_metric_fn | python | google/model_search | model_search/metric_fns.py | https://github.com/google/model_search/blob/master/model_search/metric_fns.py | Apache-2.0 |
def _metric_fn(labels, predictions, weights=None):
"""Metrics for tensorboard.
Args:
labels: A int64 Tensor or a string Tensor; or a dictionary mapping task
names (strings) to those. If a task name maps to a string Tensor, then
label_vocabulary needs to contain that task name as a key as ... | Metrics for tensorboard.
Args:
labels: A int64 Tensor or a string Tensor; or a dictionary mapping task
names (strings) to those. If a task name maps to a string Tensor, then
label_vocabulary needs to contain that task name as a key as well,
otherwise the task name would not have metri... | _metric_fn | python | google/model_search | model_search/metric_fns.py | https://github.com/google/model_search/blob/master/model_search/metric_fns.py | Apache-2.0 |
def _make_auc_metric_fn(curve, label_vocabulary):
"""Makes a metric_fn to compute AUC-ROC or AUC-PR.
Wraps around tf.metrics.auc(), so that it is easier to keep track of the
metric name with the string key. This only works in the single-task
binary-classification setup.
Args:
curve: "ROC" or "PR".
l... | Makes a metric_fn to compute AUC-ROC or AUC-PR.
Wraps around tf.metrics.auc(), so that it is easier to keep track of the
metric name with the string key. This only works in the single-task
binary-classification setup.
Args:
curve: "ROC" or "PR".
label_vocabulary: A 1-D string Tensor or string list. If... | _make_auc_metric_fn | python | google/model_search | model_search/metric_fns.py | https://github.com/google/model_search/blob/master/model_search/metric_fns.py | Apache-2.0 |
def _metric_fn(labels, predictions, weights=None):
"""Metrics for tensorboard.
Args:
labels: A 1-D Tensor castable to bool, where True means that the label for
that instance is class 1, and False means that the label for that
instance is class 0.
predictions: A dictionary mapping st... | Metrics for tensorboard.
Args:
labels: A 1-D Tensor castable to bool, where True means that the label for
that instance is class 1, and False means that the label for that
instance is class 0.
predictions: A dictionary mapping strings to Tensors. This dictionary
contains a `pred... | _metric_fn | python | google/model_search | model_search/metric_fns.py | https://github.com/google/model_search/blob/master/model_search/metric_fns.py | Apache-2.0 |
def create_num_parameters_metric_fn(tower_name=None):
"""Makes the function to count the number of trainable parameters.
Args:
tower_name: The name of the tower that contains variables we want to count.
If it is None, then use all variables.
Returns:
A function that returns a dict with a single st... | Makes the function to count the number of trainable parameters.
Args:
tower_name: The name of the tower that contains variables we want to count.
If it is None, then use all variables.
Returns:
A function that returns a dict with a single string key `num_parameters`
that maps to a tuple containi... | create_num_parameters_metric_fn | python | google/model_search | model_search/metric_fns.py | https://github.com/google/model_search/blob/master/model_search/metric_fns.py | Apache-2.0 |
def _metric_fn(labels, predictions, weights=None):
"""Counts the number of trainable parameters.
Args:
labels: Unused.
predictions: Unused.
weights: Unused.
Returns:
dict with a single string key `num_parameters` that maps to a tuple
containing two int32 0-D Tensors, both con... | Counts the number of trainable parameters.
Args:
labels: Unused.
predictions: Unused.
weights: Unused.
Returns:
dict with a single string key `num_parameters` that maps to a tuple
containing two int32 0-D Tensors, both containing the number of trainable
parameters.
| _metric_fn | python | google/model_search | model_search/metric_fns.py | https://github.com/google/model_search/blob/master/model_search/metric_fns.py | Apache-2.0 |
def combine_metric_fns(metric_fn_list):
"""Returns a single metric_fn that combines the outputs of metric_fn_list.
Args:
metric_fn_list: A list of functions that each takes arguments `labels` and
`predictions` and returns a dictionary mapping string keys to (tensor,
update_op) tuples.
Returns:
... | Returns a single metric_fn that combines the outputs of metric_fn_list.
Args:
metric_fn_list: A list of functions that each takes arguments `labels` and
`predictions` and returns a dictionary mapping string keys to (tensor,
update_op) tuples.
Returns:
A dictionary mapping string keys to (tenso... | combine_metric_fns | python | google/model_search | model_search/metric_fns.py | https://github.com/google/model_search/blob/master/model_search/metric_fns.py | Apache-2.0 |
def _metric_fn(labels, predictions, weights=None):
"""Returns a dictionary mapping string to (tensor, update_op) tuples."""
metrics_dict = {}
for child_metric_fn in metric_fn_list:
metrics_dict.update(child_metric_fn(labels, predictions, weights))
return metrics_dict | Returns a dictionary mapping string to (tensor, update_op) tuples. | _metric_fn | python | google/model_search | model_search/metric_fns.py | https://github.com/google/model_search/blob/master/model_search/metric_fns.py | Apache-2.0 |
def _set_model_dir_for_run_config(model_dir=None):
"""ContextManager for overwriting environment configuration for RunConfig."""
old_tf_config_str = os.environ.get(_TF_CONFIG_ENV)
new_tf_config = (
copy.deepcopy(json.loads(old_tf_config_str)) if old_tf_config_str else {})
if model_dir is not None:
n... | ContextManager for overwriting environment configuration for RunConfig. | _set_model_dir_for_run_config | python | google/model_search | model_search/oss_trainer_lib.py | https://github.com/google/model_search/blob/master/model_search/oss_trainer_lib.py | Apache-2.0 |
def get_dataset_provider():
"""Helper function to get the data provider."""
logging.info("Getting the registered data provider")
# Reigstration API
data_providers = registry.lookup_all(ms_data.Provider)
if len(data_providers) == 1:
return data_providers[0]
# Registering more than one data provider
el... | Helper function to get the data provider. | get_dataset_provider | python | google/model_search | model_search/oss_trainer_lib.py | https://github.com/google/model_search/blob/master/model_search/oss_trainer_lib.py | Apache-2.0 |
def loss_and_metric_and_predictions_fn(provider):
"""Helper function to create loss and metric fns."""
metric_fn = None
loss_fn = None
predictions_fn = None
if getattr(provider, "get_metric_fn", None) is not None:
metric_fn = provider.get_metric_fn()
if getattr(provider, "get_loss_fn", None) is not None... | Helper function to create loss and metric fns. | loss_and_metric_and_predictions_fn | python | google/model_search | model_search/oss_trainer_lib.py | https://github.com/google/model_search/blob/master/model_search/oss_trainer_lib.py | Apache-2.0 |
def make_run_config(model_dir=None, use_tpu=False):
"""Makes a RunConfig object with FLAGS.
Args:
model_dir: string - the model directory - to be used in the tpu run config
only.
use_tpu: boolean indicating if to use tpu run config or not.
Returns:
tf.estimator.RunConfig: Run config.
Raises... | Makes a RunConfig object with FLAGS.
Args:
model_dir: string - the model directory - to be used in the tpu run config
only.
use_tpu: boolean indicating if to use tpu run config or not.
Returns:
tf.estimator.RunConfig: Run config.
Raises:
ValueError: If not exactly one of `save_checkpoints... | make_run_config | python | google/model_search | model_search/oss_trainer_lib.py | https://github.com/google/model_search/blob/master/model_search/oss_trainer_lib.py | Apache-2.0 |
def get_trial_dir(model_dir, tuner_id):
"""Helper function to get trial directory."""
tuner_dir = os.path.join(model_dir, tuner_id)
if not tf.io.gfile.exists(tuner_dir):
tf.io.gfile.makedirs(tuner_dir)
existing_trials = tf.io.gfile.listdir(tuner_dir)
if not existing_trials:
trial_dir = os.path.join(tu... | Helper function to get trial directory. | get_trial_dir | python | google/model_search | model_search/oss_trainer_lib.py | https://github.com/google/model_search/blob/master/model_search/oss_trainer_lib.py | Apache-2.0 |
def aggregate_initial_architecture(hparams):
"""Helper function to aggregate initial architecture into an array hparam."""
output = hparams.copy()
initial_architecture_size = len(
[hp for hp in hparams.keys() if hp.startswith("initial_architecture")])
output["initial_architecture"] = [
hparams["init... | Helper function to aggregate initial architecture into an array hparam. | aggregate_initial_architecture | python | google/model_search | model_search/oss_trainer_lib.py | https://github.com/google/model_search/blob/master/model_search/oss_trainer_lib.py | Apache-2.0 |
def run_parameterized_train_and_eval(phoenix_instance, oracle, tuner_id,
root_dir, max_trials, data_provider,
train_steps, eval_steps, batch_size):
"""Train, getting parameters from a tuner.
Args:
phoenix_instance: a phoenix.Phoenix obje... | Train, getting parameters from a tuner.
Args:
phoenix_instance: a phoenix.Phoenix object.
oracle: a keras_tuner oracle.
tuner_id: identifier of the tuner (integer).
root_dir: the root directory to save the models.
max_trials: the maximal number of trials allowed.
data_provider: The data provi... | run_parameterized_train_and_eval | python | google/model_search | model_search/oss_trainer_lib.py | https://github.com/google/model_search/blob/master/model_search/oss_trainer_lib.py | Apache-2.0 |
def run_keras_parameterized_train_and_eval(phoenix_instance, oracle,
data_provider):
"""Train, getting parameters from a tuner.
Args:
phoenix_instance: a phoenix.Phoenix object.
oracle: a keras_tuner oracle.
data_provider: The data provider object.
Returns:... | Train, getting parameters from a tuner.
Args:
phoenix_instance: a phoenix.Phoenix object.
oracle: a keras_tuner oracle.
data_provider: The data provider object.
Returns:
True if the tuner provided a trial to run, False if the tuner
has run out of trials.
| run_keras_parameterized_train_and_eval | python | google/model_search | model_search/oss_trainer_lib.py | https://github.com/google/model_search/blob/master/model_search/oss_trainer_lib.py | Apache-2.0 |
def _default_predictions_fn(logits,
mode=tf.estimator.ModeKeys.TRAIN,
temperature=1.0):
"""Converts logits to predictions dict. Assumes classification."""
new_logits = logits
if mode == tf.estimator.ModeKeys.PREDICT and temperature != 1.0:
assert tempera... | Converts logits to predictions dict. Assumes classification. | _default_predictions_fn | python | google/model_search | model_search/phoenix.py | https://github.com/google/model_search/blob/master/model_search/phoenix.py | Apache-2.0 |
def __init__(self,
phoenix_spec,
input_layer_fn,
study_owner,
study_name,
head=None,
logits_dimension=None,
label_vocabulary=None,
loss_fn=None,
metric_fn=None,
predictio... | Constructs a Phoenix instance.
Args:
phoenix_spec: A `PhoenixSpec` proto with the spec for the run.
input_layer_fn: A function that converts feature Tensors to input layer.
See learning.autolx.model_search.data.Provider.get_input_layer_fn
for details.
study_owner: A string holding... | __init__ | python | google/model_search | model_search/phoenix.py | https://github.com/google/model_search/blob/master/model_search/phoenix.py | Apache-2.0 |
def keras_compile(self, towers, hparams):
"""Compiles the keras model based on hparams."""
optimizer_args = dict()
# Learning rate
lr = hparams.learning_rate
if getattr(hparams, "exponential_decay_rate", None) is not None:
max_times = self._phoenix_spec.learning_spec.max_decay_times
ste... | Compiles the keras model based on hparams. | keras_compile | python | google/model_search | model_search/phoenix.py | https://github.com/google/model_search/blob/master/model_search/phoenix.py | Apache-2.0 |
def keras_model_builder(self,
hparams,
run_config=None,
is_training=None,
input_layer_fn=None,
compile_model=True):
"""Builds a keras model based on hparams."""
if compile_model:
... | Builds a keras model based on hparams. | keras_model_builder | python | google/model_search | model_search/phoenix.py | https://github.com/google/model_search/blob/master/model_search/phoenix.py | Apache-2.0 |
def model_fn(features, labels, mode, params):
"""Model function that wraps the model specified."""
self._metric_fn = self._user_specified_metric_fn
self._default_metric_fn_list = []
if self._logits_dimension >= 2:
self._default_metric_fn_list.append(
metric_fns.make_accuracy_... | Model function that wraps the model specified. | model_fn | python | google/model_search | model_search/phoenix.py | https://github.com/google/model_search/blob/master/model_search/phoenix.py | Apache-2.0 |
def _increment_global_step(self, train_op, train_steps, tower_name):
"""Increments the global step based on the tower size.
N.B. if the tower size does not divide evenly into the train_steps, it will
train for longer than required.
Args:
train_op: The train_op to execute before incrementing the ... | Increments the global step based on the tower size.
N.B. if the tower size does not divide evenly into the train_steps, it will
train for longer than required.
Args:
train_op: The train_op to execute before incrementing the global_step.
train_steps: The total number of steps to train for.
... | _increment_global_step | python | google/model_search | model_search/phoenix.py | https://github.com/google/model_search/blob/master/model_search/phoenix.py | Apache-2.0 |
def get_estimator(self, run_config, hparams, train_steps):
"""Returns a Phoenix `Estimator` for train and evaluation.
Args:
run_config: `RunConfig` object to configure the runtime settings.
hparams: `HParams` instance defining custom hyperparameters.
train_steps: The total number of training ... | Returns a Phoenix `Estimator` for train and evaluation.
Args:
run_config: `RunConfig` object to configure the runtime settings.
hparams: `HParams` instance defining custom hyperparameters.
train_steps: The total number of training steps.
Returns:
Returns an `Estimator`.
Raises:
... | get_estimator | python | google/model_search | model_search/phoenix.py | https://github.com/google/model_search/blob/master/model_search/phoenix.py | Apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.