markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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위의 그림에서 0~4는 레이블 컬럼에 들어가 있는 것!!마스크 방식은 `for문` + `if문````pythonfor ...: if ...: for ...: if (~(df['label'] ==0) | (df['label'] == 4)) : 0도 아니고 4도 아니고``` | dfx = df[(~(df['label'] ==0) | (df['label'] == 4))]
df.shape, dfx.shape
dfx.plot(kind='scatter', x='Grocery',y='Frozen',c='label', cmap='Set1', figsize=(7,7))
df.to_excel('./wholesale.xls')
| _____no_output_____ | Apache-2.0 | 0702_ML19_clustering_kmeans.ipynb | msio900/minsung_machinelearning |
[Index](Index.ipynb) - [Next](Widget List.ipynb) Simple Widget Introduction What are widgets? Widgets are eventful python objects that have a representation in the browser, often as a control like a slider, textbox, etc. What can they be used for? You can use widgets to build **interactive GUIs** for your notebooks.... | import ipywidgets as widgets | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
repr Widgets have their own display `repr` which allows them to be displayed using IPython's display framework. Constructing and returning an `IntSlider` automatically displays the widget (as seen below). Widgets are displayed inside the output area below the code cell. Clearing cell output will also remove the widg... | widgets.IntSlider() | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
display() You can also explicitly display the widget using `display(...)`. | from IPython.display import display
w = widgets.IntSlider()
display(w) | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
Multiple display() calls If you display the same widget twice, the displayed instances in the front-end will remain in sync with each other. Try dragging the slider below and watch the slider above. | display(w) | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
Why does displaying the same widget twice work? Widgets are represented in the back-end by a single object. Each time a widget is displayed, a new representation of that same object is created in the front-end. These representations are called views. Closing ... | display(w)
w.close() | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
Widget properties All of the IPython widgets share a similar naming scheme. To read the value of a widget, you can query its `value` property. | w = widgets.IntSlider()
display(w)
w.value | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
Similarly, to set a widget's value, you can set its `value` property. | w.value = 100 | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
Keys In addition to `value`, most widgets share `keys`, `description`, and `disabled`. To see the entire list of synchronized, stateful properties of any specific widget, you can query the `keys` property. | w.keys | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
Shorthand for setting the initial values of widget properties While creating a widget, you can set some or all of the initial values of that widget by defining them as keyword arguments in the widget's constructor (as seen below). | widgets.Text(value='Hello World!', disabled=True) | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
Linking two similar widgets If you need to display the same value two different ways, you'll have to use two different widgets. Instead of attempting to manually synchronize the values of the two widgets, you can use the `link` or `jslink` function to link two properties together (the difference between these is dis... | a = widgets.FloatText()
b = widgets.FloatSlider()
display(a,b)
mylink = widgets.jslink((a, 'value'), (b, 'value')) | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
Unlinking widgets Unlinking the widgets is simple. All you have to do is call `.unlink` on the link object. Try changing one of the widgets above after unlinking to see that they can be independently changed. | # mylink.unlink() | _____no_output_____ | BSD-3-Clause | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets |
Data format conversion for WEASEL_MUSE===---Input---Two file types, each **data file** represents a single sample; the **label file** contains labels of all samples***Note:*** *both training and testing data should do the conversion***data files**: - file name: "sample_id.csv"- file contents: L * D, L is the MTS length... | import numpy as np
| _____no_output_____ | MIT | Baselines/mtsc_weasel_muse/.ipynb_checkpoints/Preprocess_weasel_muse-checkpoint.ipynb | JingweiZuo/SMATE |
On country (only MS) | df.fund= df.fund=='TRUE'
df.gre= df.gre=='TRUE'
df.highLevelBachUni= df.highLevelBachUni=='TRUE'
df.highLevelMasterUni= df.highLevelMasterUni=='TRUE'
df.uniRank.fillna(294,inplace=True)
df.columns
oldDf=df.copy()
df=df[['countryCoded','degreeCoded','engCoded', 'fieldGroup','fund','gpaBachelors','gre', 'highLevelBachUni... | ('ball_tree', 'braycurtis', 55.507529507529512)
('ball_tree', 'canberra', 44.839072039072036)
('ball_tree', 'chebyshev', 53.738054538054541)
('ball_tree', 'cityblock', 55.735775335775337)
('ball_tree', 'euclidean', 55.793080993080991)
('ball_tree', 'dice', 46.14798534798534)
('ball_tree', 'hamming', 47.408547008547011)... | MIT | 08_AfterAcceptance/06_KNN/knn.ipynb | yazdipour/DM17 |
On Fund (only MS) | bestAvg=[]
for alg in algorithm:
for dis in dist:
k_fold = KFold(n=len(df), n_folds=5)
scores = []
try:
clf = KNeighborsClassifier(n_neighbors=3, weights='distance',algorithm=alg, metric=dis)
except Exception as err:
continue
for train_indices, test_in... | ('ball_tree', 'braycurtis', 76.495400895400905)
('ball_tree', 'canberra', 75.354008954008961)
('ball_tree', 'chebyshev', 75.584533984533977)
('ball_tree', 'cityblock', 77.293935693935694)
('ball_tree', 'euclidean', 76.496703296703302)
('ball_tree', 'dice', 74.383557183557173)
('ball_tree', 'hamming', 76.152706552706562... | MIT | 08_AfterAcceptance/06_KNN/knn.ipynb | yazdipour/DM17 |
Best : ('kd_tree', 'cityblock', 77.692144892144896) | me=[0,2,0,2.5,False,False,1.5,400]
n=bestClf.kneighbors([me])
n
for i in n[1]:
print(xtr.iloc[i]) | countryCoded engCoded fieldGroup gpaBachelors gre highLevelBachUni \
664 0 2 0 2.5 False False
767 0 2 0 3.0 False False
911 0 2 0 3.0 False False... | MIT | 08_AfterAcceptance/06_KNN/knn.ipynb | yazdipour/DM17 |
Periodo de los datos: 6 ciclos y medio (+ 3 ciclos-0) | df['date'].hist(bins=51, figsize=(10,5))
plt.xlim(df['date'].min(), df['date'].max())
plt.title('Histograma de la Fecha de Envío del Mensaje')
plt.ylabel('Número de Mensajes')
plt.xlabel('Año-Mes')
plt.show()
#plt.savefig('hist_fecha.svg', format='svg')
df['month'] = df['date'].dt.month
df['dayofweek'] = df['date'].dt.... | _____no_output_____ | MIT | deep_learning/models/combine_processes/Data_Cleaning_NLP.ipynb | Claudio9701/mailbot |
Email pairing algorithm1. Extrae los mensajes enviados por alumno y los mensajes enviados por usuarios internos a cada alumno, respectivamente2. Extrae el asunto de cada mensaje del punto 1. Si el asunto del mensaje es igual al asunto enviado en el mensaje anterior aumenta el contador de mensajes con el mismo asunto.3... | # Separate mails sended to each alumn
dfs = [send_by_internals[send_by_internals.recipient_email == alumn] for alumn in send_by_alumns.sender_email.unique()]
unique_alumns = send_by_alumns.sender_email.unique()
n = len(unique_alumns)
# Count causes to not being able to process a text
resp_date_bigger_than_input_date =... | 100%|██████████| 3781/3781 [00:57<00:00, 65.81it/s]
| MIT | deep_learning/models/combine_processes/Data_Cleaning_NLP.ipynb | Claudio9701/mailbot |
Format data | total_unpaired_mails = repited_id+resp_date_bigger_than_input_date+responses_with_same_subject_lower_than_counter+subject_equal_none+n_obs_less_than_0
print()
print('Filtros del algoritmo de emparejamiento')
print('resp_date_bigger_than_input_date:',resp_date_bigger_than_input_date)
print('subject_equal_none:',subject_... | _____no_output_____ | MIT | deep_learning/models/combine_processes/Data_Cleaning_NLP.ipynb | Claudio9701/mailbot |
NLP | ## Tokenization using NLTK
# Define input (x) and target (y) sequences variables
x = [word_tokenize(msg, language='spanish') for msg in paired_mails['input_body'].values]
y = [word_tokenize(msg, language='spanish') for msg in paired_mails['resp_body'].values]
# Variables to store lenghts
hist_len_inp = []
hist_len_out... | _____no_output_____ | MIT | deep_learning/models/combine_processes/Data_Cleaning_NLP.ipynb | Claudio9701/mailbot |
Matplotlib Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell, web application servers, and six graphical user interface toolkits.Mat... | # needed to display the graphs
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 5, 10)
y = x ** 2
fig = plt.figure()
# left, bottom, width, height (range 0 to 1)
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes.plot(x, y, 'r')
axes.set_xlabel('x')
axes.set_ylabel('y')
axes.set_... | _____no_output_____ | MIT | Matplotlib-BEst.ipynb | imamol555/Machine-Learning |
Copyright 2018 The TF-Agents Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
Environments View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook Introduction The goal of Reinforcement Learning (RL) is to design agents that learn by interacting with an environment. In the standard RL setting, the agent receives an ... | !pip install tf-agents
!pip install 'gym==0.10.11'
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import tensorflow as tf
import numpy as np
from tf_agents.environments import py_environment
from tf_agents.environments import tf_environment
from... | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
Python Environments Python environments have a `step(action) -> next_time_step` method that applies an action to the environment, and returns the following information about the next step:1. `observation`: This is the part of the environment state that the agent can observe to choose its actions at the next step.2. `r... | class PyEnvironment(object):
def reset(self):
"""Return initial_time_step."""
self._current_time_step = self._reset()
return self._current_time_step
def step(self, action):
"""Apply action and return new time_step."""
if self._current_time_step is None:
return self.reset()
self._cu... | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
In addition to the `step()` method, environments also provide a `reset()` method that starts a new sequence and provides an initial `TimeStep`. It is not necessary to call the `reset` method explicitly. We assume that environments reset automatically, either when they get to the end of an episode or when step() is call... | environment = suite_gym.load('CartPole-v0')
print('action_spec:', environment.action_spec())
print('time_step_spec.observation:', environment.time_step_spec().observation)
print('time_step_spec.step_type:', environment.time_step_spec().step_type)
print('time_step_spec.discount:', environment.time_step_spec().discount)
... | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
So we see that the environment expects actions of type `int64` in [0, 1] and returns `TimeSteps` where the observations are a `float32` vector of length 4 and discount factor is a `float32` in [0.0, 1.0]. Now, let's try to take a fixed action `(1,)` for a whole episode. | action = np.array(1, dtype=np.int32)
time_step = environment.reset()
print(time_step)
while not time_step.is_last():
time_step = environment.step(action)
print(time_step) | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
Creating your own Python EnvironmentFor many clients, a common use case is to apply one of the standard agents (see agents/) in TF-Agents to their problem. To do this, they have to frame their problem as an environment. So let us look at how to implement an environment in Python.Let's say we want to train an agent to ... | class CardGameEnv(py_environment.PyEnvironment):
def __init__(self):
self._action_spec = array_spec.BoundedArraySpec(
shape=(), dtype=np.int32, minimum=0, maximum=1, name='action')
self._observation_spec = array_spec.BoundedArraySpec(
shape=(1,), dtype=np.int32, minimum=0, name='observation')... | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
Let's make sure we did everything correctly defining the above environment. When creating your own environment you must make sure the observations and time_steps generated follow the correct shapes and types as defined in your specs. These are used to generate the TensorFlow graph and as such can create hard to debug p... | environment = CardGameEnv()
utils.validate_py_environment(environment, episodes=5) | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
Now that we know the environment is working as intended, let's run this environment using a fixed policy: ask for 3 cards and then end the round. | get_new_card_action = np.array(0, dtype=np.int32)
end_round_action = np.array(1, dtype=np.int32)
environment = CardGameEnv()
time_step = environment.reset()
print(time_step)
cumulative_reward = time_step.reward
for _ in range(3):
time_step = environment.step(get_new_card_action)
print(time_step)
cumulative_rewa... | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
Environment WrappersAn environment wrapper takes a python environment and returns a modified version of the environment. Both the original environment and the modified environment are instances of `py_environment.PyEnvironment`, and multiple wrappers can be chained together.Some common wrappers can be found in `enviro... | env = suite_gym.load('Pendulum-v0')
print('Action Spec:', env.action_spec())
discrete_action_env = wrappers.ActionDiscretizeWrapper(env, num_actions=5)
print('Discretized Action Spec:', discrete_action_env.action_spec()) | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
The wrapped `discrete_action_env` is an instance of `py_environment.PyEnvironment` and can be treated like a regular python environment. TensorFlow Environments The interface for TF environments is defined in `environments/tf_environment.TFEnvironment` and looks very similar to the Python environments. TF Environments... | class TFEnvironment(object):
def time_step_spec(self):
"""Describes the `TimeStep` tensors returned by `step()`."""
def observation_spec(self):
"""Defines the `TensorSpec` of observations provided by the environment."""
def action_spec(self):
"""Describes the TensorSpecs of the action expected by `... | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
The `current_time_step()` method returns the current time_step and initializes the environment if needed.The `reset()` method forces a reset in the environment and returns the current_step.If the `action` doesn't depend on the previous `time_step` a `tf.control_dependency` is needed in `Graph` mode.For now, let us look... | env = suite_gym.load('CartPole-v0')
tf_env = tf_py_environment.TFPyEnvironment(env)
print(isinstance(tf_env, tf_environment.TFEnvironment))
print("TimeStep Specs:", tf_env.time_step_spec())
print("Action Specs:", tf_env.action_spec()) | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
Note the specs are now of type: `(Bounded)TensorSpec`. Usage Examples Simple Example | env = suite_gym.load('CartPole-v0')
tf_env = tf_py_environment.TFPyEnvironment(env)
# reset() creates the initial time_step after resetting the environment.
time_step = tf_env.reset()
num_steps = 3
transitions = []
reward = 0
for i in range(num_steps):
action = tf.constant([i % 2])
# applies the action and returns... | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
Whole Episodes | env = suite_gym.load('CartPole-v0')
tf_env = tf_py_environment.TFPyEnvironment(env)
time_step = tf_env.reset()
rewards = []
steps = []
num_episodes = 5
for _ in range(num_episodes):
episode_reward = 0
episode_steps = 0
while not time_step.is_last():
action = tf.random.uniform([1], 0, 2, dtype=tf.int32)
... | _____no_output_____ | Apache-2.0 | site/en-snapshot/agents/tutorials/2_environments_tutorial.ipynb | secsilm/docs-l10n |
`Практикум по программированию на языке Python` `Занятие 2: Пользовательские и встроенные функции, итераторы и генераторы` `Мурат Апишев (mel-lain@yandex.ru)` `Москва, 2021` `Функции range и enumerate` | r = range(2, 10, 3)
print(type(r))
for e in r:
print(e, end=' ')
for index, element in enumerate(list('abcdef')):
print(index, element, end=' ') | 0 a 1 b 2 c 3 d 4 e 5 f | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Функция zip` | z = zip([1, 2, 3], 'abc')
print(type(z))
for a, b in z:
print(a, b, end=' ')
for e in zip('abcdef', 'abc'):
print(e)
for a, b, c, d in zip('abc', [1,2,3], [True, False, None], 'xyz'):
print(a, b, c, d) | a 1 True x
b 2 False y
c 3 None z
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Определение собственных функций` | def function(arg_1, arg_2=None):
print(arg_1, arg_2)
function(10)
function(10, 20) | 10 None
10 20
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
Функция - это тоже объект, её имя - просто символическая ссылка: | f = function
f(10)
print(function is f) | 10 None
True
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Определение собственных функций` | retval = f(10)
print(retval)
def factorial(n):
return n * factorial(n - 1) if n > 1 else 1 # recursion
print(factorial(1))
print(factorial(2))
print(factorial(4)) | 1
2
24
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Передача аргументов в функцию`Параметры в Python всегда передаются по ссылке | def function(scalar, lst):
scalar += 10
print(f'Scalar in function: {scalar}')
lst.append(None)
print(f'Scalar in function: {lst}')
s, l = 5, []
function(s, l)
print(s, l) | Scalar in function: 15
Scalar in function: [None]
5 [None]
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Передача аргументов в функцию` | def f(a, *args):
print(type(args))
print([v for v in [a] + list(args)])
f(10, 2, 6, 8)
def f(*args, a):
print([v for v in [a] + list(args)])
print()
f(2, 6, 8, a=10)
def f(a, *args, **kw):
print(type(kw))
print([v for v in [a] + list(args) + [(k, v) for k, v in kw.items()]])
f(2, *(6, 8),... | <class 'dict'>
[2, 6, 8, ('arg1', 1), ('arg2', 2)]
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Области видимости переменных`В Python есть 4 основных уровня видимости:- Встроенная (buildins) - на этом уровне находятся все встроенные объекты (функции, классы исключений и т.п.)- Глобальная в рамках модуля (global) - всё, что определяется в коде модуля на верхнем уровне- Объемлюшей функции (enclosed) - всё, что оп... | def outer_func(x):
def inner_func(x):
return len(x)
return inner_func(x)
print(outer_func([1, 2])) | 2
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
Кто определил имя `len`?- на уровне вложенной функции такого имени нет, смотрим выше- на уровне объемлющей функции такого имени нет, смотрим выше- на уровне модуля такого имени нет, смотрим выше- на уровне builtins такое имя есть, используем его `На builtins можно посмотреть` | import builtins
counter = 0
lst = []
for name in dir(builtins):
if name[0].islower():
lst.append(name)
counter += 1
if counter == 5:
break
lst | _____no_output_____ | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
Кстати, то же самое можно сделать более pythonic кодом: | list(filter(lambda x: x[0].islower(), dir(builtins)))[: 5] | _____no_output_____ | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Локальные и глобальные переменные` | x = 2
def func():
print('Inside: ', x) # read
func()
print('Outside: ', x)
x = 2
def func():
x += 1 # write
print('Inside: ', x)
func() # UnboundLocalError: local variable 'x' referenced before assignment
print('Outside: ', x)
x = 2
def func():
x = 3
x += 1
print('Inside: ', x)
... | Inside: 4
Outside: 2
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Ключевое слово global` | x = 2
def func():
global x
x += 1 # write
print('Inside: ', x)
func()
print('Outside: ', x)
x = 2
def func(x):
x += 1
print('Inside: ', x)
return x
x = func(x)
print('Outside: ', x) | Inside: 3
Outside: 3
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Ключевое слово nonlocal` | a = 0
def out_func():
b = 10
def mid_func():
c = 20
def in_func():
global a
a += 100
nonlocal c
c += 100
nonlocal b
b += 100
print(a, b, c)
in_func()
mid_func()... | 100 110 120
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
__Главный вывод:__ не надо злоупотреблять побочными эффектами при работе с переменными верхних уровней `Пример вложенных функций: замыкания`- В большинстве случаев вложенные функции не нужны, плоская иерархия будет и проще, и понятнее- Одно из исключений - фабричные функции (замыкания) | def function_creator(n):
def function(x):
return x ** n
return function
f = function_creator(5)
f(2) | _____no_output_____ | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
Объект-функция, на который ссылается `f`, хранит в себе значение `n` `Анонимные функции`- `def` - не единственный способ объявления функции- `lambda` создаёт анонимную (lambda) функциюТакие функции часто используются там, где синтаксически нельзя записать определение через `def` | def func(x): return x ** 2
func(6)
lambda_func = lambda x: x ** 2 # should be an expression
lambda_func(6)
def func(x): print(x)
func(6)
lambda_func = lambda x: print(x ** 2) # as print is function in Python 3.*
lambda_func(6) | 36
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Встроенная функция sorted` | lst = [5, 2, 7, -9, -1]
def abs_comparator(x):
return abs(x)
print(sorted(lst, key=abs_comparator))
sorted(lst, key=lambda x: abs(x))
sorted(lst, key=lambda x: abs(x), reverse=True) | _____no_output_____ | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Встроенная функция filter` | lst = [5, 2, 7, -9, -1]
f = filter(lambda x: x < 0, lst) # True condition
type(f) # iterator
list(f) | _____no_output_____ | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Встроенная функция map` | lst = [5, 2, 7, -9, -1]
m = map(lambda x: abs(x), lst)
type(m) # iterator
list(m) | _____no_output_____ | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Ещё раз сравним два подхода`Напишем функцию скалярного произведения в императивном и функциональном стилях: | def dot_product_imp(v, w):
result = 0
for i in range(len(v)):
result += v[i] * w[i]
return result
dot_product_func = lambda v, w: sum(map(lambda x: x[0] * x[1], zip(v, w)))
print(dot_product_imp([1, 2, 3], [4, 5, 6]))
print(dot_product_func([1, 2, 3], [4, 5, 6])) | 32
32
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Функция reduce``functools` - стандартный модуль с другими функциями высшего порядка.Рассмотрим пока только функцию `reduce`: | from functools import reduce
lst = list(range(1, 10))
reduce(lambda x, y: x * y, lst) | _____no_output_____ | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Итерирование, функции iter и next` | r = range(3)
for e in r:
print(e)
it = iter(r) # r.__iter__() - gives us an iterator
print(next(it))
print(it.__next__())
print(next(it))
print(next(it)) | 0
1
2
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Итераторы часто используются неявно`Как выглядит для нас цикл `for`: | for i in 'seq':
print(i) | s
e
q
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
Как он работает на самом деле: | iterator = iter('seq')
while True:
try:
i = next(iterator)
print(i)
except StopIteration:
break | s
e
q
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Генераторы`- Генераторы, как и итераторы, предназначены для итерирования по коллекции, но устроены несколько иначе- Они определяются с помощью функций с оператором `yield` или генераторов списков, а не вызовов `iter()` и `next()`- В генераторе есть внутреннее изменяемое состояние в виде локальных переменных, которое ... | def my_range(n):
yield 'You really want to run this generator?'
i = -1
while i < n:
i += 1
yield i
gen = my_range(3)
while True:
try:
print(next(gen), end=' ')
except StopIteration: # we want to catch this type of exceptions
break
for e in my_range(3):
print(e... | You really want to run this generator? 0 1 2 3 | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Особенность range``range` не является генератором, хотя и похож, поскольку не хранит всю последовательность | print('__next__' in dir(zip([], [])))
print('__next__' in dir(range(3))) | True
False
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
Полезные особенности:- объекты `range` неизменяемые (могут быть ключами словаря)- имеют полезные атрибуты (`len`, `index`, `__getitem__`)- по ним можно итерироваться многократно `Модуль itetools`- Модуль представляет собой набор инструментов для работы с итераторами и последовательностями- Содержит три основных типа и... | from itertools import count
for i in count(start=0):
print(i, end=' ')
if i == 5:
break
from itertools import cycle
count = 0
for item in cycle('XYZ'):
if count > 4:
break
print(item, end=' ')
count += 1 | X Y Z X Y | MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Модуль itetools: примеры` | from itertools import accumulate
for i in accumulate(range(1, 5), lambda x, y: x * y):
print(i)
from itertools import chain
for i in chain([1, 2], [3], [4]):
print(i) | 1
2
3
4
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
`Модуль itetools: примеры` | from itertools import groupby
vehicles = [('Ford', 'Taurus'), ('Dodge', 'Durango'),
('Chevrolet', 'Cobalt'), ('Ford', 'F150'),
('Dodge', 'Charger'), ('Ford', 'GT')]
sorted_vehicles = sorted(vehicles)
for key, group in groupby(sorted_vehicles, lambda x: x[0]):
for maker, model in group:... | Cobalt is made by Chevrolet
**** END OF THE GROUP ***
Charger is made by Dodge
Durango is made by Dodge
**** END OF THE GROUP ***
F150 is made by Ford
GT is made by Ford
Taurus is made by Ford
**** END OF THE GROUP ***
| MIT | lectures/02-functions.ipynb | sir-rois/mipt-python |
***Introduction to Radar Using Python and MATLAB*** Andy Harrison - Copyright (C) 2019 Artech House Coherent Detector*** The in-phase and quadrature signal components from a coherent detector may be written as (Equation 5.13)$$ x(t) = a(t) \cos(2\pi f_0 t) \cos(\phi(t)) - a(t) \sin(2 \pi f_0 t) \sin(\phi(t)) = ... | import lib_path | _____no_output_____ | Apache-2.0 | jupyter/Chapter05/coherent_detector.ipynb | miltondsantos/software |
Set the sampling frequency (Hz), the start frequency (Hz), the end frequency (Hz), the amplitude modulation frequency (Hz) and amplitude (relative) for the sample signal | sampling_frequency = 100
start_frequency = 4
end_frequency = 25
am_amplitude = 0.1
am_frequency = 9 | _____no_output_____ | Apache-2.0 | jupyter/Chapter05/coherent_detector.ipynb | miltondsantos/software |
Calculate the bandwidth (Hz) and center frequency (Hz) | bandwidth = end_frequency - start_frequency
center_frequency = 0.5 * bandwidth + start_frequency | _____no_output_____ | Apache-2.0 | jupyter/Chapter05/coherent_detector.ipynb | miltondsantos/software |
Set up the waveform | from numpy import arange, sin
from scipy.constants import pi
from scipy.signal import chirp
time = arange(sampling_frequency) / sampling_frequency
if_signal = chirp(time, start_frequency, time[-1], end_frequency)
if_signal *= (1.0 + am_amplitude * sin(2.0 * pi * am_frequency * time)) | _____no_output_____ | Apache-2.0 | jupyter/Chapter05/coherent_detector.ipynb | miltondsantos/software |
Set up the keyword args | kwargs = {'if_signal': if_signal,
'center_frequency': center_frequency,
'bandwidth': bandwidth,
'sample_frequency': sampling_frequency,
'time': time} | _____no_output_____ | Apache-2.0 | jupyter/Chapter05/coherent_detector.ipynb | miltondsantos/software |
Calculate the baseband in-phase and quadrature signals | from Libs.receivers import coherent_detector
i_signal, q_signal = coherent_detector.iq(**kwargs) | _____no_output_____ | Apache-2.0 | jupyter/Chapter05/coherent_detector.ipynb | miltondsantos/software |
Use the `matplotlib` routines to display the results | from matplotlib import pyplot as plt
from numpy import real, imag
# Set the figure size
plt.rcParams["figure.figsize"] = (15, 10)
# Display the results
plt.plot(time, real(i_signal), '', label='In Phase')
plt.plot(time, real(q_signal), '-.', label='Quadrature')
# Set the plot title and labels
plt.title('Cohe... | _____no_output_____ | Apache-2.0 | jupyter/Chapter05/coherent_detector.ipynb | miltondsantos/software |
Visualizing and Analyzing Jigsaw | import pandas as pd
import re
import numpy as np | _____no_output_____ | BSD-3-Clause | .ipynb_checkpoints/Visualizing and Analyzing Jigsaw-checkpoint.ipynb | dudaspm/LDA_Bias_Data |
In the previous section, we explored how to generate topics from a textual dataset using LDA. But how can this be used as an application? Therefore, in this section, we will look into the possible ways to read the topics as well as understand how it can be used. We will now import the preloaded data of the LDA result t... | df = pd.read_csv("https://raw.githubusercontent.com/dudaspm/LDA_Bias_Data/main/topics.csv")
df.head() | _____no_output_____ | BSD-3-Clause | .ipynb_checkpoints/Visualizing and Analyzing Jigsaw-checkpoint.ipynb | dudaspm/LDA_Bias_Data |
We will visualize these results to understand what major themes are present in them. | %%html
<iframe src='https://flo.uri.sh/story/941631/embed' title='Interactive or visual content' class='flourish-embed-iframe' frameborder='0' scrolling='no' style='width:100%;height:600px;' sandbox='allow-same-origin allow-forms allow-scripts allow-downloads allow-popups allow-popups-to-escape-sandbox allow-top-navig... | _____no_output_____ | BSD-3-Clause | .ipynb_checkpoints/Visualizing and Analyzing Jigsaw-checkpoint.ipynb | dudaspm/LDA_Bias_Data |
An Overview of the analysis From the above visualization, an anomaly that we come across is that the dataset we are examining is supposed to be related to people with physical, mental and learning disability. But unfortunately based on the topics that were extracted, we notice just a small subset of words that are rel... | headers = {"Authorization": f"Bearer api_ZtUEFtMRVhSLdyTNrRAmpxXgMAxZJpKLQb"} | _____no_output_____ | BSD-3-Clause | .ipynb_checkpoints/Visualizing and Analyzing Jigsaw-checkpoint.ipynb | dudaspm/LDA_Bias_Data |
To get access to this software, you will need to get an API KEY at https://huggingface.co/unitary/toxic-bertHere is an example of what this would look like.```pythonheaders = {"Authorization": f"Bearer api_XXXXXXXXXXXXXXXXXXXXXXXXXXX"}``` | import requests
API_URL = "https://api-inference.huggingface.co/models/unitary/toxic-bert"
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
query({"inputs": "addict"}) | _____no_output_____ | BSD-3-Clause | .ipynb_checkpoints/Visualizing and Analyzing Jigsaw-checkpoint.ipynb | dudaspm/LDA_Bias_Data |
You can input words or sentences in \, in the code, to look at the results that are generated through this.This example can provide an idea as to how ML can be used for toxicity analysis. | query({"inputs": "<insert word here>"})
%%html
<iframe src='https://flo.uri.sh/story/941681/embed' title='Interactive or visual content' class='flourish-embed-iframe' frameborder='0' scrolling='no' style='width:100%;height:600px;' sandbox='allow-same-origin allow-forms allow-scripts allow-downloads allow-popups allow-... | _____no_output_____ | BSD-3-Clause | .ipynb_checkpoints/Visualizing and Analyzing Jigsaw-checkpoint.ipynb | dudaspm/LDA_Bias_Data |
The BiasThe visualization shows how contextually toxic words are derived as important words within various topics related to this dataset. This can lead to any Natural Language Processing kernel learning this dataset to provide skewed analysis for the population in consideration, i.e. people with mental, physical and ... | %%html
<iframe src='https://flo.uri.sh/visualisation/6867000/embed' title='Interactive or visual content' class='flourish-embed-iframe' frameborder='0' scrolling='no' style='width:100%;height:600px;' sandbox='allow-same-origin allow-forms allow-scripts allow-downloads allow-popups allow-popups-to-escape-sandbox allow-t... | _____no_output_____ | BSD-3-Clause | .ipynb_checkpoints/Visualizing and Analyzing Jigsaw-checkpoint.ipynb | dudaspm/LDA_Bias_Data |
It is hence important to be aware of the dataset that is being used to analyse a specific population. With LDA, we were able to understand that this dataset cannot be used as a good representation of the disabled community. To bring about a movement of unbiased AI, we need to perform such preliminary analysis and more,... | %%html
<iframe src='https://flo.uri.sh/visualisation/6856937/embed' title='Interactive or visual content' class='flourish-embed-iframe' frameborder='0' scrolling='no' style='width:100%;height:600px;' sandbox='allow-same-origin allow-forms allow-scripts allow-downloads allow-popups allow-popups-to-escape-sandbox allow-... | _____no_output_____ | BSD-3-Clause | .ipynb_checkpoints/Visualizing and Analyzing Jigsaw-checkpoint.ipynb | dudaspm/LDA_Bias_Data |
Figure 1 - Overview | df = pd.read_csv(datdir / 'fig_1.csv')
scores = df[list(map(str, range(20)))].values
selected = ~np.isnan(df['Selected'].values)
gens_sel = np.nonzero(selected)[0]
scores_sel = np.array([np.max(scores[g]) for g in gens_sel])
ims_sel = [plt.imread(str(datdir / 'images' / 'overview' / f'gen{gen:03d}.png'))
for... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Define Custom Violinplot | def violinplot2(data=None, x=None, y=None, hue=None,
palette=None, linewidth=1, orient=None,
order=None, hue_order=None, x_disp=None,
palette_per_violin=None, hline_at_1=True,
legend_palette=None, legend_kwargs=None,
width=0.7, control_widt... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Figure 3 - Compare Target Nets, Layers | df = pd.read_csv(datdir/'fig_2.csv')
df = df[~np.isnan(df['Rel_act'])] # remove invalid data
df.head()
nets = ('caffenet', 'resnet-152-v2', 'resnet-269-v2', 'inception-v3', 'inception-v4', 'inception-resnet-v2', 'placesCNN')
layers = {'caffenet': ('conv2', 'conv4', 'fc6', 'fc8'),
'resnet-152-v2': ('res15_e... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Figure 5 - Compare Generators Compare representation "depth" | df = pd.read_csv(datdir / 'fig_5-repr_depth.csv')
df = df[~np.isnan(df['Rel_act'])]
df['Classifier, layer'] = [', '.join(tuple(a)) for a in df[['Classifier', 'Layer']].values]
df.head()
nets = ('caffenet', 'inception-resnet-v2')
layers = {'caffenet': ('conv2', 'fc6', 'fc8'),
'inception-resnet-v2': ('classifi... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Compare training dataset | df = pd.read_csv(datdir / 'fig_5-training_set.csv')
df = df[~np.isnan(df['Rel_act'])]
df['Classifier, layer'] = [', '.join(tuple(a)) for a in df[['Classifier', 'Layer']].values]
df.head()
nets = ('caffenet', 'inception-resnet-v2')
cs = ('caffenet', 'placesCNN', 'inception-resnet-v2')
layers = {c: ('conv2', 'conv4', 'fc... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Figure 4 - Compare Inits | layers = ('conv2', 'conv4', 'fc6', 'fc8')
layers_disp = tuple(v.capitalize() for v in layers) | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Rand inits, fraction change | df = pd.read_csv(datdir/'fig_4-rand_init.csv').set_index(['Layer', 'Unit', 'Init_seed'])
df = (df.drop(0, level='Init_seed') - df.xs(0, level='Init_seed')).mean(axis=0,level=('Layer','Unit'))
df = df.rename({'Rel_act': 'Fraction change'}, axis=1)
df = df.reset_index()
df.head()
palette = get_cmap('Blues')(np.linspace(0... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Rand inits, interpolation | df = pd.read_csv(datdir/'fig_4-rand_init_interp.csv').set_index(['Layer', 'Unit', 'Seed_i0', 'Seed_i1'])
df = df.mean(axis=0,level=('Layer','Unit'))
df2 = pd.read_csv(datdir/'fig_4-rand_init_interp-2.csv').set_index(['Layer', 'Unit']) # control conditions
df2_normed = df2.divide(df[['Rel_act_loc_0.0','Rel_act_loc_1.... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Per-neuron inits | df = pd.read_csv(datdir/'fig_4-per_neuron_init.csv')
df.head()
hue_order = ('rand', 'none', 'worst_opt', 'mid_opt', 'best_opt',
'worst_ivt', 'mid_ivt', 'best_ivt')
palette = [get_cmap(main_c)(np.linspace(0.3,0.8,4))
for main_c in ('Blues', 'Greens', 'Purples')]
palette = np.concatenate([[
pa... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Figure 6 - Compare Optimizers & Stoch Scales Compare optimizers | df = pd.read_csv(datdir/'fig_6-optimizers.csv')
df['OCL'] = ['_'.join(v) for v in df[['Optimizer','Classifier','Layer']].values]
df.head()
opts = ('genetic', 'FDGD', 'NES')
layers = {'caffenet': ('conv2', 'conv4', 'fc6', 'fc8'),
'inception-resnet-v2': ('classifier',)}
cls = [(c, l) for c in layers for l in la... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Compare varying amounts of noise | df = pd.read_csv(datdir/'fig_6-stoch_scales.csv')
df = df[~np.isnan(df['Rel_noise'])]
df['Stoch_scale_plot'] = [str(int(v)) if ~np.isnan(v) else 'None' for v in df['Stoch_scale']]
df.head()
layers = ('conv2', 'conv4', 'fc6', 'fc8')
stoch_scales = list(map(str, (5, 10, 20, 50, 75, 100, 250))) + ['None']
stoch_scales_dis... | _____no_output_____ | MIT | figure_data/Make Plots.ipynb | willwx/XDream |
Libraries and auxiliary functions | #load the libraries
from time import sleep
from kafka import KafkaConsumer
import datetime as dt
import pygeohash as pgh
#fuctions to check the location based on the geo hash (precision =5)
#function to check location between 2 data
def close_location (data1,data2):
print("checking location...of sender",data1.get("... | _____no_output_____ | MIT | Assignment_TaskC_Streaming_Application.ipynb | tonbao30/Parallel-dataprocessing-simulation |
Streaming Application | import os
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.3.0 pyspark-shell'
import sys
import time
import json
from pymongo import MongoClient
from pyspark import SparkContext, SparkConf
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka impo... | -------------------------------------------
Time: 2019-05-24 17:45:20
-------------------------------------------
sender_2
sender_3
sender_1
sender_1
-------------------------------------------
Time: 2019-05-24 17:45:30
-------------------------------------------
sender_3
sender_1
sender_1
---------------------------... | MIT | Assignment_TaskC_Streaming_Application.ipynb | tonbao30/Parallel-dataprocessing-simulation |
Text Summarization Sequenece to Sequence Modelling Attention Mechanism Import Libraries | #import all the required libraries
import numpy as np
import pandas as pd
import pickle
from statistics import mode
import nltk
from nltk import word_tokenize
from nltk.stem import LancasterStemmer
nltk.download('wordnet')
nltk.download('stopwords')
nltk.download('punkt')
from nltk.corpus import stopwords
from tensorfl... | [nltk_data] Downloading package wordnet to /usr/share/nltk_data...
[nltk_data] Package wordnet is already up-to-date!
[nltk_data] Downloading package stopwords to /usr/share/nltk_data...
[nltk_data] Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to /usr/share/nltk_data...
[nltk_data]... | MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
Parse the Data We’ll take a sample of 100,000 reviews to reduce the training time of our model. | #read the dataset file for text Summarizer
df=pd.read_csv("../input/amazon-fine-food-reviews/Reviews.csv",nrows=10000)
# df = pd.read_csv("../input/amazon-fine-food-reviews/Reviews.csv")
#drop the duplicate and na values from the records
df.drop_duplicates(subset=['Text'],inplace=True)
df.dropna(axis=0,inplace=True) #d... | _____no_output_____ | MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
Preprocessing Performing basic preprocessing steps is very important before we get to the model building part. Using messy and uncleaned text data is a potentially disastrous move. So in this step, we will drop all the unwanted symbols, characters, etc. from the text that do not affect the objective of our problem.Her... | contraction_mapping = {"ain't": "is not", "aren't": "are not","can't": "cannot", "'cause": "because", "could've": "could have", "couldn't": "could not",
"didn't": "did not", "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hasn't": "has not", "haven't": "have not",
... | _____no_output_____ | MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
We can use the contraction using two method, one we can use the above dictionary or we can keep the contraction file as a data set and import it. | input_texts=[] # Text column
target_texts=[] # summary column
input_words=[]
target_words=[]
# contractions=pickle.load(open("../input/contraction/contractions.pkl","rb"))['contractions']
contractions = contraction_mapping
#initialize stop words and LancasterStemmer
stop_words=set(stopwords.words('english'))
stemm=La... | _____no_output_____ | MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
Data Cleaning | def clean(texts,src):
texts = BeautifulSoup(texts, "lxml").text #remove the html tags
words=word_tokenize(texts.lower()) #tokenize the text into words
#filter words which contains \
#integers or their length is less than or equal to 3
words= list(filter(lambda w:(w.isalpha() and len(w)>=3),words))
#con... | number of input words : 10344
number of target words : 4169
maximum input length : 73
maximum target length : 17
| MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
Split it | #split the input and target text into 80:20 ratio or testing size of 20%.
x_train,x_test,y_train,y_test=train_test_split(input_texts,target_texts,test_size=0.2,random_state=0)
#train the tokenizer with all the words
in_tokenizer = Tokenizer()
in_tokenizer.fit_on_texts(x_train)
tr_tokenizer = Tokenizer()
tr_tokenizer.f... | _____no_output_____ | MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
Model Building | K.clear_session()
latent_dim = 500
#create input object of total number of encoder words
en_inputs = Input(shape=(max_in_len,))
en_embedding = Embedding(num_in_words+1, latent_dim)(en_inputs)
#create 3 stacked LSTM layer with the shape of hidden dimension for text summarizer using deep learning
#LSTM 1
en_lstm1= L... | _____no_output_____ | MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
Decoder | # Decoder.
dec_inputs = Input(shape=(None,))
dec_emb_layer = Embedding(num_tr_words+1, latent_dim)
dec_embedding = dec_emb_layer(dec_inputs)
#initialize decoder's LSTM layer with the output states of encoder
dec_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
dec_outputs, *_ = dec_lstm(dec_embed... | _____no_output_____ | MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
Attention Layer | #Attention layer
attention =Attention()
attn_out = attention([dec_outputs,en_outputs3])
#Concatenate the attention output with the decoder outputs
merge=Concatenate(axis=-1, name='concat_layer1')([dec_outputs,attn_out])
#Dense layer (output layer)
dec_dense = Dense(num_tr_words+1, activation='softmax')
dec_outputs =... | _____no_output_____ | MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
Train the Model | #Model class and model summary for text Summarizer
model = Model([en_inputs, dec_inputs], dec_outputs)
model.summary()
plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
model.compile(optimizer="rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"] )
history = model.... | _____no_output_____ | MIT | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism |
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