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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Copyright 2018 The TensorFlow 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/2/tutorials/eager/custom_layers.ipynb | allenlavoie/docs |
Custom layers View on TensorFlow.org Run in Google Colab View source on GitHub We recommend using `tf.keras` as a high-level API for building neural networks. That said, most TensorFlow APIs are usable with eager execution. | !pip install tf-nightly-2.0-preview
import tensorflow as tf | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/eager/custom_layers.ipynb | allenlavoie/docs |
Layers: common sets of useful operationsMost of the time when writing code for machine learning models you want to operate at a higher level of abstraction than individual operations and manipulation of individual variables.Many machine learning models are expressible as the composition and stacking of relatively simp... | # In the tf.keras.layers package, layers are objects. To construct a layer,
# simply construct the object. Most layers take as a first argument the number
# of output dimensions / channels.
layer = tf.keras.layers.Dense(100)
# The number of input dimensions is often unnecessary, as it can be inferred
# the first time t... | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/eager/custom_layers.ipynb | allenlavoie/docs |
The full list of pre-existing layers can be seen in [the documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers). It includes Dense (a fully-connected layer),Conv2D, LSTM, BatchNormalization, Dropout, and many others. | # To use a layer, simply call it.
layer(tf.zeros([10, 5]))
# Layers have many useful methods. For example, you can inspect all variables
# in a layer by calling layer.variables. In this case a fully-connected layer
# will have variables for weights and biases.
layer.variables
# The variables are also accessible through... | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/eager/custom_layers.ipynb | allenlavoie/docs |
Implementing custom layersThe best way to implement your own layer is extending the tf.keras.Layer class and implementing: * `__init__` , where you can do all input-independent initialization * `build`, where you know the shapes of the input tensors and can do the rest of the initialization * `call`, where you do ... | class MyDenseLayer(tf.keras.layers.Layer):
def __init__(self, num_outputs):
super(MyDenseLayer, self).__init__()
self.num_outputs = num_outputs
def build(self, input_shape):
self.kernel = self.add_variable("kernel",
shape=[int(input_shape[-1]),
... | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/eager/custom_layers.ipynb | allenlavoie/docs |
Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`.Overall code is easier to read and maintain if it uses standard layers whenever possible, as other readers will be familiar with the behavior of standard layers. If you want to use a layer which is ... | class ResnetIdentityBlock(tf.keras.Model):
def __init__(self, kernel_size, filters):
super(ResnetIdentityBlock, self).__init__(name='')
filters1, filters2, filters3 = filters
self.conv2a = tf.keras.layers.Conv2D(filters1, (1, 1))
self.bn2a = tf.keras.layers.BatchNormalization()
self.conv2b = tf.... | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/eager/custom_layers.ipynb | allenlavoie/docs |
Much of the time, however, models which compose many layers simply call one layer after the other. This can be done in very little code using tf.keras.Sequential | my_seq = tf.keras.Sequential([tf.keras.layers.Conv2D(1, (1, 1)),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Conv2D(2, 1,
padding='same'),
tf.keras.layers.BatchN... | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/eager/custom_layers.ipynb | allenlavoie/docs |
Next stepsNow you can go back to the previous notebook and adapt the linear regression example to use layers and models to be better structured. | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/eager/custom_layers.ipynb | allenlavoie/docs | |
check_list = [1,1,5,7,9,6,4]
sub_list = [1,1,5]
print("original list : " +str (check_list))
print("original sublist : " +str (sub_list))
flag=0
if (set (sub_list).issubset(set (check_list))):
flag = 1
if (flag):
print("Its a Match.")
else :
rint("Its Gone") | original list : [1, 1, 5, 7, 9, 6, 4]
original sublist : [1, 1, 5]
Its a Match.
| Apache-2.0 | Day5 Assignment1.ipynb | Tulasi-ummadipolu/LetsUpgrade-Python-B7 | |
Planewave propagation in a Whole-space (frequency-domain) PurposeWe visualizae downward propagating planewave in the homogeneous earth medium. With the three apps: a) Plane wave app, b) Profile app, and c) Polarization ellipse app, we understand fundamental concepts of planewave propagation. Set upPlanewave EM equa... | ax = plotObj3D() | _____no_output_____ | MIT | notebooks/em/FDEM_Planewave_Wholespace.ipynb | jcapriot/geosci-labs |
Planewave app Parameters:- Field: Type of EM fields ("Ex": electric field, "Hy": magnetic field)- AmpDir: Type of the vectoral EM fields None: $F_x$ or $F_y$ or $F_z$ Amp: $\mathbf{F} \cdot \mathbf{F}^* = |\mathbf{F}|^2$ Dir: Real part of a vectoral EM fields, $\Re[\mathbf{F}]$ - ComplexNumber: Ty... | dwidget = PlanewaveWidget()
Q = dwidget.InteractivePlaneWave()
display(Q) | _____no_output_____ | MIT | notebooks/em/FDEM_Planewave_Wholespace.ipynb | jcapriot/geosci-labs |
Profile appWe visualize EM fields at vertical profile (marked as red dots in the above app). Parameters:- **Field**: Ex, Hy, and Impedance - ** $\sigma$ **: Conductivity (S/m)- **Scale**: Log10 or Linear scale- **Fixed**: Fix the scale or not- **$f$**: Frequency- **$t$**: Time | display(InteractivePlaneProfile()) | _____no_output_____ | MIT | notebooks/em/FDEM_Planewave_Wholespace.ipynb | jcapriot/geosci-labs |
Polarization Ellipse app | Polarwidget = PolarEllipse();
Polarwidget.Interactive() | _____no_output_____ | MIT | notebooks/em/FDEM_Planewave_Wholespace.ipynb | jcapriot/geosci-labs |
The 1cycle policy | from fastai.gen_doc.nbdoc import *
from fastai import *
from fastai.vision import * | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
What is 1cycle? This Callback allows us to easily train a network using Leslie Smith's 1cycle policy. To learn more about the 1cycle technique for training neural networks check out [Leslie Smith's paper](https://arxiv.org/pdf/1803.09820.pdf) and for a more graphical and intuitive explanation check out [Sylvain Gugger... | path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
model = simple_cnn((3,16,16,2))
learn = Learner(data, model, metrics=[accuracy]) | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
First lets find the optimum learning rate for our comparison by doing an LR range test. | learn.lr_find()
learn.recorder.plot() | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
Here 5e-2 looks like a good value, a tenth of the minimum of the curve. That's going to be the highest learning rate in 1cycle so let's try a constant training at that value. | learn.fit(2, 5e-2) | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
We can also see what happens when we train at a lower learning rate | model = simple_cnn((3,16,16,2))
learn = Learner(data, model, metrics=[accuracy])
learn.fit(2, 5e-3) | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
Training with the 1cycle policy Now to do the same thing with 1cycle, we use [`fit_one_cycle`](/train.htmlfit_one_cycle). | model = simple_cnn((3,16,16,2))
learn = Learner(data, model, metrics=[accuracy])
learn.fit_one_cycle(2, 5e-2) | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
This gets the best of both world and we can see how we get a far better accuracy and a far lower loss in the same number of epochs. It's possible to get to the same amazing results with training at constant learning rates, that we progressively diminish, but it will take a far longer time.Here is the schedule of the lr... | learn.recorder.plot_lr(show_moms=True)
show_doc(OneCycleScheduler, doc_string=False) | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
Create a [`Callback`](/callback.htmlCallback) that handles the hyperparameters settings following the 1cycle policy for `learn`. `lr_max` should be picked with the [`lr_find`](/train.htmllr_find) test. In phase 1, the learning rates goes from `lr_max/div_factor` to `lr_max` linearly while the momentum goes from `moms[0... | show_doc(OneCycleScheduler.steps, doc_string=False) | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
Build the [`Stepper`](/callback.htmlStepper) for the [`Callback`](/callback.htmlCallback) according to `steps_cfg`. | show_doc(OneCycleScheduler.on_train_begin, doc_string=False) | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
Initiate the parameters of a training for `n_epochs`. | show_doc(OneCycleScheduler.on_batch_end, doc_string=False) | _____no_output_____ | Apache-2.0 | docs_src/callbacks.one_cycle.ipynb | fmgonzales/fastai |
Maskinlæring med Python Michael Gfeller, Computasdag 3.2.2018----_(Notebook basert på https://www.kaggle.com/futurist/pima-data-visualisation-and-machine-learning, [Apache 2.0 license](http://www.apache.org/licenses/LICENSE-2.0))_ Definer og forstå oppgaven | from IPython.display import YouTubeVideo
YouTubeVideo("pN4HqWRybwk") | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Innsikt og forutsigelse om en kvinne fra Pima-folkestammen får diabetes innen 5 år. Last inn biblioteker | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Last inn og utforsk data | pima = pd.read_csv("diabetes.csv") # pandas.core.frame.DataFrame
pima.head(4)
pima.shape
pima.info()
pima.describe()
pima.groupby("Outcome").size() | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Visualiser data Histogram | pima.hist(figsize=(10,10)) | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Boxplot | pima.plot(kind= 'box' , subplots=True, layout=(3,3), sharex=False, sharey=False, figsize=(8,8))
X_columns = pima.columns[0:len(pima.columns) - 1]
pima[X_columns].plot(kind= 'box', subplots=False, figsize=(20,8)) | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Korrelasjon mellom variablene | correlations = pima[pima.columns].corr()
sns.heatmap(correlations, annot = True) | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Velg input-variabler (features, givens, independent) | from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
X = pima.iloc[:,0:8]
Y = pima.iloc[:,8]
select_top_4 = SelectKBest(score_func=chi2, k = 4)
fit = select_top_4.fit(X,Y)
features = fit.transform(X)
feature_cols = pima.columns[fit.get_support('indices')]
feature_cols
features[0:... | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Forbered data med standardisering | from sklearn.preprocessing import StandardScaler # En av flere scalers.
X_features_scaled = StandardScaler().fit_transform(X_features)
X = pd.DataFrame(data = X_features_scaled, columns= X_features.columns)
X.head(3)
X.hist()
X.plot(kind= 'box', subplots=False, figsize=(20,8)) | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Prøv ut forskjellige modeller - binærklassifisering | from sklearn.model_selection import train_test_split
random_seed = 22
X_train,X_test,Y_train,Y_test = train_test_split(X,Y, random_state = random_seed, test_size = 0.2)
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from ... | LR : 77.69% +/- 5.23%
NB : 76.05% +/- 5.94%
KNN : 74.59% +/- 4.68%
DT : 70.36% +/- 3.79%
SVM : 77.69% +/- 5.15%
LSVM : 77.85% +/- 5.24%
| Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Visualiser resultatene | ax = sns.boxplot(data=results)
ax.set_xticklabels(names) | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Tren og valider de beste modellerLogistisk regresjon og (L)SVM ga de beste resultatene. | X_train.describe()
Y_train_df = pd.DataFrame(data = Y_train, columns = ['Outcome'])
Y_train_df.groupby("Outcome").size()
X_test.describe()
Y_test_df = pd.DataFrame(data = Y_test, columns = ['Outcome'])
Y_test_df.groupby("Outcome").size() | _____no_output_____ | Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Logistisk regresjon | lr = LogisticRegression()
lr.fit(X_train,Y_train)
predictions = lr.predict(X_test)
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
print("%-5s: %.2f%%" % ("LR", accuracy_score(Y_test,predictions)*100)) | LR : 71.43%
| Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Support Vector Classifier | svm = SVC()
svm.fit(X_train,Y_train)
predictions = svm.predict(X_test)
print("%-5s: %.2f%%" % ("SVM", accuracy_score(Y_test,predictions)*100))
print(classification_report(Y_test,predictions))
# https://en.wikipedia.org/wiki/Confusion_matrix
confusion = confusion_matrix(Y_test,predictions)
# print(confusion)
tn, fp, fn,... | True negatives: 92
True positives: 21
False negatives: 33
False positives: 8
Accuracy: 73%
Precision: 72%
Recall: 39%
| Apache-2.0 | ml-presentation/cx-pima-diabetes.ipynb | mgfeller/tensorflow |
Table of Contents1 Load Data2 Demo of Cleaning Functions2.1 Columns2.2 Outliers2.3 Transformations | import datetime as dt
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
print(sys.executable)
print(sys.version)
print(f"Pandas {pd.__version__}")
print(f"Seaborn {sns.__version__}")
sys.path.append(str(Path.cwd().parent / 'src' / 'codebook... | _____no_output_____ | MIT | demo/dev_clean.ipynb | rbuerki/codebook |
Load Data | df = pd.read_csv("../data/realWorldTestData.csv",
low_memory=False,
nrows=1000,
usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18]
)
df.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 target_event 1000 non-null object
1 NUM_CONSEC_SERVICES ... | MIT | demo/dev_clean.ipynb | rbuerki/codebook |
Demo of Cleaning Functions Columns | # Prettify the column names
df = clean.prettify_column_names(df)
# Check result
df.columns
# Delete columns
df_del = clean.delete_columns(df, cols_to_delete=["target_event", "first_evt"])
assert df_del.shape[1] == (df.shape[1] - 2)
# Downcast dtypes
df_lean = clean.downcast_dtypes(df)
# Check result
df_lean.dtyp... | Original df size before downcasting: 0.46 MB
New df size after downcasting:0.16 MB
| MIT | demo/dev_clean.ipynb | rbuerki/codebook |
A word of Warning: Downcasting the numerical dtypes this way can lead to problems with the power transforms that are demonstrated below. That's why we continue with the original frame here. Outliers | # Count Outliers using the IQR-Method, with a distance of X
clean.count_outliers_IQR_method(df, iqr_dist=2)
# Remove outliers in two selected columns
outlier_cols=["avg_diff_mnth", "mean_mileage_per_mnth"]
df_outliers, deleted_idx = clean.remove_outliers_IQR_method(
df,
outlier_cols=outlier_cols,
iqr_dis... | [(0.0, 4889.35), (1296.0, 171053.0)]
| MIT | demo/dev_clean.ipynb | rbuerki/codebook |
Transformations | df_transform = df[["last_mileage", "sum_invoice_amount", "mean_mileage_per_mnth"]]. copy()
EDA.plot_distr_histograms(df_transform)
df_log = clean.transform_data(df_transform, method="log")
EDA.plot_distr_histograms(df_log)
df_log10 = clean.transform_data(df_transform, method="log10")
EDA.plot_distr_histograms(df_log... | _____no_output_____ | MIT | demo/dev_clean.ipynb | rbuerki/codebook |
Getting DataFirst, we want to grab some graphs and subject covariates from a web-accessible url. We've given this to you on google drive rather than having you set up aws s3 credentials in the interest of saving time. The original data is hosted at m2g.ioBelow, you will be getting the following dataset:| Property | V... | !pip install networkx==1.9 #networkx broke backwards compatibility with these graph files
import numpy as np
import networkx as nx
import scipy as sp
import matplotlib.pyplot as plt
import os
import csv
import networkx.algorithms.centrality as nac
from collections import OrderedDict
# Initializing dataset names
datas... | _____no_output_____ | Apache-2.0 | projects/graphexplorer/submissions/RonanDariusHamilton/graphexplore.ipynb | wrgr/intersession2018 |
ASSIGNMENT: (Code above used to get data in the correct format. Below is a simple example test string with kind of silly features) | #Combine features, separate training and test data
X = []
for i in range(len(g1)):
featvec = []
matrix = nx.to_numpy_matrix(g1[i], nodelist=sorted(g1[i].nodes())) #this is how you go to a matrix
logmatrix = np.log10(np.sum(matrix,0) + 1)
logmatrix = np.ravel(logmatrix)
covariate1 = n... | _____no_output_____ | Apache-2.0 | projects/graphexplorer/submissions/RonanDariusHamilton/graphexplore.ipynb | wrgr/intersession2018 |
Raumluftqualität 2.0 Zeitliche Entwicklung der CO_2-Konzentration in RäumenIn einem gut gelüfteten, leeren Raum wird sich zunächst genau so viel CO_2 befinden, wie in der Außenluft. Wenn sich dann Personen in den Raum begeben und CO_2 freisetzen, wird die CO_2-Konzentration langsam zunehmen. Auf welchen Wert sie sich... | import matplotlib.pyplot as plt
%config InlineBackend.figure_format = 'retina'
import pandas as pd
import numpy as np
lt = np.linspace(0,120,13) # 10-min Schritte
df = pd.DataFrame(
{
't': lt,
'k': 400 + 1600*lt/60 # 60min = 1h
}
)
display(df.T)
ax=df.plot(x='t',y='k', label='$k = k(t)$')
ax.a... | _____no_output_____ | MIT | Notebooks/Notebook_2.ipynb | w-meiners/rlt-rlq |
__Word Alignment Assignment__Your task is to learn word alignments for the data provided with this Python Notebook. Start by running the 'train' function below and implementing the assertions which will fail. Then consider the following improvements to the baseline model:* Is the TranslationModel parameterized efficien... | # This cell contains the generative models that you may want to use for word alignment.
# Currently only the TranslationModel is at all functional.
import numpy as np
from collections import defaultdict
class TranslationModel:
"Models conditional distribution over trg words given a src word."
def __init__(se... | _____no_output_____ | MIT | week09_mt/homework/word_alignment_assignment.ipynb | Holemar/nlp_course |
数据网站，http://quotes.money.163.com/stock下载交易历史数据：http://quotes.money.163.com/cjmx/2019/20191120/1300127.xls，获得一个SCV文件。结构如下：成交时间,成交价,价格变动,成交量（手）,成交额（元）,性质09:30:06,17.2,-0.05,50,86011,卖盘09:30:09,17.21,0.01,887,1525626,买盘大概每3秒一条记录。 Library | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler
from datetime import datetime
| _____no_output_____ | MIT | lstm-ashare-live.ipynb | sillyemperor/mypynotebook |
Data Plot | data = pd.read_csv('data/ashare/30012720191120.csv', usecols = [0, 1, 3], converters={
0:lambda x:datetime.strptime(x, '%H:%M:%S')
})
# print(data)
training_set = data.iloc[:,1].values
timeline = data.iloc[:,0].values
plt.plot(timeline, training_set, )
plt.show()
def local_price(file):
data = pd.read_csv(fil... | _____no_output_____ | MIT | lstm-ashare-live.ipynb | sillyemperor/mypynotebook |
1. Area plots are stacked by default. Ans: True. 2. The following code uses the artist layer to create a stacked area plot of the data in the pandas dataframe, area_df. | ax = series_df.plot(kind='area', figsize=(20, 10))
ax.title('Plot Title')
ax.ylabel('Vertical Axis Label')
ax.xlabel('Horizontal Axis Label') | _____no_output_____ | MIT | Coursera/Data Visualization with Python-IBM/Week-2/Quiz/Basic-Visualization-Tools.ipynb | manipiradi/Online-Courses-Learning |
Ans: False. 3. The following code will create an unstacked area plot of the data in the pandas dataframe, area_df, with a transparency value of 0.35? | import matplotlib.pyplot as plt
transparency = 0.35
area_df.plot(kind='area', alpha=transparency, figsize=(20, 10))
plt.title('Plot Title')
plt.ylabel('Vertical Axis Label')
plt.xlabel('Horizontal Axis Label')
plt.show() | _____no_output_____ | MIT | Coursera/Data Visualization with Python-IBM/Week-2/Quiz/Basic-Visualization-Tools.ipynb | manipiradi/Online-Courses-Learning |
Ans: False 4. The following code will create a histogram of a pandas series, series_data, and align the bin edges with the horizontal tick marks. | count, bin_edges = np.histogram(series_data)
series_data.plot(kind='hist', xticks = bin_edges) | _____no_output_____ | MIT | Coursera/Data Visualization with Python-IBM/Week-2/Quiz/Basic-Visualization-Tools.ipynb | manipiradi/Online-Courses-Learning |
Using fuzzy wuzzy | # identieke notes die meerdere keren voorkomen
from collections import Counter
c = Counter()
num_lines = 0
for note in notes:
c[note] += 1
repeated = []
ns = []
for k, v in c.most_common():
if v > 1:
num_lines += v
print(repr(k), v)
repeated.append(k)
else:
ns.append(k)
pr... | _____no_output_____ | Apache-2.0 | notebooks/dbnl_remove_notes.ipynb | KBNLresearch/ochre |
Using fuzzy wuzzy on all the notes at the same time | notes_text = ''.join(notes)
print(notes_text)
%%time
from fuzzywuzzy import fuzz
result = pd.DataFrame()
result['pratio'] = [fuzz.partial_ratio(l, notes_text) for l in lines]
result.head()
result.hist(bins=100)
n = 42
print(lines[n])
print(result.loc[n])
print(notes[0])
fuzz.partial_ratio(lines[42], notes[0])
fuzz.par... | _____no_output_____ | Apache-2.0 | notebooks/dbnl_remove_notes.ipynb | KBNLresearch/ochre |
Putting it all together | %%time
from nlppln.utils import create_dirs, out_file_name
in_file = '/home/jvdzwaan/data/dbnl_ocr/raw/ocr-with-title-page/_aio001jver01_01.txt'
# remove selected lines
with open(in_file) as f:
text = f.read()
for n in ns:
for idx in n['selected']:
#print(idx)
l = lines[idx]
... | _____no_output_____ | Apache-2.0 | notebooks/dbnl_remove_notes.ipynb | KBNLresearch/ochre |
用函數取代表格用簡單的神經網路來取代 V | import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from gridworld import GridWorld
blocks={(1,1), (3,3)}
gw = GridWorld(size=(5,5), start=(0,0), exit=(4,4), blocks=blocks)
from ipywidgets import widgets as W
from IPython.display import display
gw_html = W.HTML(value=gw._repr_html_())
... | _____no_output_____ | MIT | RL/Grid World-Function.ipynb | PinmanHuang/CrashCourseML |
使用 Q learningQ 用簡單的神經網路來定義 | Q = Sequential()
Q.add(Dense(128, input_shape=((gw.size[0]+2)*(gw.size[1]+2)+4,), activation="relu" )) # 輸入是 i, j 座標和 a
Q.add(Dense(1, activation="tanh")) # 因為輸出是 +-1
Q.compile(loss='mse',optimizer='sgd', metrics=['accuracy'])
avectors = [[0]* 4 for i in range(4)]
for i in range(4):
avectors[i][i]=1
def Qfunc(i,j... | _____no_output_____ | MIT | RL/Grid World-Function.ipynb | PinmanHuang/CrashCourseML |
Задача определения частей речи, Part-Of-Speech Tagger (POS) Мы будем решать задачу определения частей речи (POS-теггинга). | import nltk
import pandas as pd
import numpy as np
from nltk.corpus import brown
import matplotlib.pyplot as plt | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Вам в помощь http://www.nltk.org/book/ Загрузим brown корпус | nltk.download('brown') | [nltk_data] Downloading package brown to /root/nltk_data...
[nltk_data] Unzipping corpora/brown.zip.
| MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Существует не одна система тегирования, поэтому будьте внимательны, когда прогнозируете тег слов в тексте и вычисляете качество прогноза. Можете получить несправедливо низкое качество вашего решения. Cейчас будем использовать универсальную систему тегирования universal_tagset | nltk.download('universal_tagset') | [nltk_data] Downloading package universal_tagset to /root/nltk_data...
[nltk_data] Unzipping taggers/universal_tagset.zip.
| MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Мы имеем массив предложений пар (слово-тег) | brown_tagged_sents = brown.tagged_sents(tagset="universal")
brown_tagged_sents | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Первое предложение | brown_tagged_sents[0] | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Все пары (слово-тег) | brown_tagged_words = brown.tagged_words(tagset='universal')
brown_tagged_words | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Проанализируйте данные, с которыми Вы работаете. Используйте `nltk.FreqDist()` для подсчета частоты встречаемости тега и слова в нашем корпусе. Под частой элемента подразумевается кол-во этого элемента в корпусе. | # Приведем слова к нижнему регистру
brown_tagged_words = list(map(lambda x: (x[0].lower(), x[1]), brown_tagged_words))
print('Кол-во предложений: ', len(brown_tagged_sents))
tags = [tag for (word, tag) in brown_tagged_words] # наши теги
words = [word for (word, tag) in brown_tagged_words] # наши слова
tag_num = pd.Ser... | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Вопрос 1:* Кол-во слова `cat` в корпусе? **(0.5 балл)** | word_num["cat"] | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Вопрос 2:* Самое популярное слово с самым популярным тегом? **(0.5 балл)** | # Выбираем сначала слова с самым популярным тегом, а затем среди них выбираем самое популярное слово.
lst = [word for (word, tag) in brown_tagged_words if tag == "NOUN"]
popular = pd.Series(nltk.FreqDist(lst)).sort_values(ascending=False)
print(popular) # time - Самое популярное слово с самым популярным тегом "NOUN" | time 1597
man 1203
af 995
years 949
way 899
...
anti-communists 1
peace-treaty 1
malinovsky 1
eleventh-floor 1
boucle 1
Length: 30246, dtype: int64
| MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Cделайте разбиение выборки на обучение и контроль в отношении 9:1. **(0.5 балл)** | brown_tagged_sents = brown.tagged_sents(tagset="universal")
# Приведем слова к нижнему регистру
my_brown_tagged_sents = []
for sent in brown_tagged_sents:
my_brown_tagged_sents.append(list(map(lambda x: (x[0].lower(), x[1]), sent)))
my_brown_tagged_sents = np.array(my_brown_tagged_sents)
from sklearn.model_selecti... | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
DefaultTagger Вопрос 3:* Какое качество вы бы получили, если бы предсказывали любой тег, как самый популярный тег на выборке train(округлите до одного знака после запятой)? **(0.5 балл)** Вы можете использовать DefaultTagger(метод tag для предсказания частей речи предложения). | from nltk.tag import DefaultTagger
default_tagger = DefaultTagger("NOUN")
true_pred = 0
num_pred = 0
for sent in test_sents:
tags = np.array([tag for (word, tag) in sent])
words = np.array([word for (word, tag) in sent])
tagged_sent = default_tagger.tag(words)
outputs = [tag for token, tag in tagged_s... | Accuracy: 23.47521651004238 %
| MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
если бы предсказывали любой тег, как самый популярный тег на выборке train: 15,86% - VERB LSTMTagger Подготовка данных Изменим структуру данных | pos_data = [list(zip(*sent)) for sent in brown_tagged_sents]
print(pos_data[0]) | [('The', 'Fulton', 'County', 'Grand', 'Jury', 'said', 'Friday', 'an', 'investigation', 'of', "Atlanta's", 'recent', 'primary', 'election', 'produced', '``', 'no', 'evidence', "''", 'that', 'any', 'irregularities', 'took', 'place', '.'), ('DET', 'NOUN', 'NOUN', 'ADJ', 'NOUN', 'VERB', 'NOUN', 'DET', 'NOUN', 'ADP', 'NOUN'... | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Пора эксплуатировать pytorch! | from torchtext.data import Field, BucketIterator
import torchtext
# наши поля
WORD = Field(lower=True)
TAG = Field(unk_token=None) # все токены нам извсетны
# создаем примеры
examples = []
for words, tags in pos_data:
examples.append(torchtext.data.Example.fromlist([list(words), list(tags)], fields=[('words', WOR... | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Вот один наш пример: | print(vars(examples[0])) | {'words': ['the', 'fulton', 'county', 'grand', 'jury', 'said', 'friday', 'an', 'investigation', 'of', "atlanta's", 'recent', 'primary', 'election', 'produced', '``', 'no', 'evidence', "''", 'that', 'any', 'irregularities', 'took', 'place', '.'], 'tags': ['DET', 'NOUN', 'NOUN', 'ADJ', 'NOUN', 'VERB', 'NOUN', 'DET', 'NOU... | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Теперь формируем наш датасет | # кладем примеры в наш датасет
dataset = torchtext.data.Dataset(examples, fields=[('words', WORD), ('tags', TAG)])
train_data, valid_data, test_data = dataset.split(split_ratio=[0.8, 0.1, 0.1])
print(f"Number of training examples: {len(train_data.examples)}")
print(f"Number of validation examples: {len(valid_data.exa... | Number of training examples: 45872
Number of validation examples: 5734
Number of testing examples: 5734
| MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Построим словари. Параметр `min_freq` выберете сами. При построении словаря испольузем только **train** **(0.5 балл)** | WORD.build_vocab(train_data, min_freq=10)
TAG.build_vocab(train_data)
print(f"Unique tokens in source (ru) vocabulary: {len(WORD.vocab)}")
print(f"Unique tokens in target (en) vocabulary: {len(TAG.vocab)}")
print(WORD.vocab.itos[::200])
print(TAG.vocab.itos) | Unique tokens in source (ru) vocabulary: 7316
Unique tokens in target (en) vocabulary: 13
['<unk>', 'number', 'available', 'miles', 'clearly', 'corps', 'quickly', 'b.', 'resolution', 'review', 'orchestra', 'occasionally', 'warfare', 'bread', "nation's", 'tested', 'visitors', 'accident', 'sovereign', 'gesture', 'sharpe'... | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Здесь вы увидете токен `unk` и `pad`. Первый служит для обозначения слов, которых у нас нет в словаре. Второй служит для того, что объекты в одном батче были одинакового размера. | print(vars(train_data.examples[9])) | {'words': ['there', 'was', 'a', 'contorted', 'ugliness', 'now', ';', ';'], 'tags': ['PRT', 'VERB', 'DET', 'VERB', 'NOUN', 'ADV', '.', '.']}
| MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Посмотрим с насколько большими предложениями мы имеем дело | length = map(len, [vars(x)['words'] for x in train_data.examples])
plt.figure(figsize=[8, 4])
plt.title("Length distribution in Train data")
plt.hist(list(length), bins=20); | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Для обучения `LSTM` лучше использовать colab | import torch
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Для более быстрого и устойчивого обучения сгруппируем наши данные по батчам | # бьем нашу выборку на батч, не забывая сначала отсортировать выборку по длине
def _len_sort_key(x):
return len(x.words)
BATCH_SIZE = 64
train_iterator, valid_iterator, test_iterator = BucketIterator.splits(
(train_data, valid_data, test_data),
batch_size = BATCH_SIZE,
device = device,
sort_key=... | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Модель и её обучение Инициализируем нашу модель. Прочитайте про dropout [тут](https://habr.com/ru/company/wunderfund/blog/330814/). **(3 балла)** | class LSTMTagger(nn.Module):
def __init__(self, input_dim, emb_dim, hid_dim, output_dim, dropout):
super().__init__()
self.embeddings = nn.Embedding(num_embeddings=input_dim, embedding_dim=emb_dim)
self.dropout = nn.Dropout(p=dropout)
self.rnn = nn.LSTM(emb_dim,... | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Подсчитаем количество обучаемых параметров нашей модели. Используйте метод `numel()`. **(1 балл)** | def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'The model has {count_parameters(model):,} trainable parameters') | The model has 37,403 trainable parameters
| MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Погнали обучать **(2 балла)** | PAD_IDX = TAG.vocab.stoi['<pad>']
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss(ignore_index = PAD_IDX)
def train(model, iterator, optimizer, criterion, clip, train_history=None, valid_history=None):
model.train()
epoch_loss = 0
history = []
for i, batch in enumerate(i... | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Применение модели **(1 балл)** | def accuracy_model(model, iterator):
model.eval()
true_pred = 0
num_pred = 0
with torch.no_grad():
for i, batch in enumerate(iterator):
words = batch.words
tags = batch.tags
output = model(words)
#output = [sent len, batch ... | Accuracy: 92.797 %
| MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Вы можете улучшить качество, изменяя параметры модели. Вам неоходимо добиться качества не меньше, чем `accuracy = 92 %`. | best_model = LSTMTagger(INPUT_DIM, EMB_DIM, HID_DIM, OUTPUT_DIM, DROPOUT).to(device)
best_model.load_state_dict(torch.load('/content/best-val-model.pt'))
assert accuracy_model(best_model, test_iterator) >= 92 | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
**Если качество сети меньше 92 процентов, то снимается половина от всех полученных баллов . То есть максимум в этом случае 5 баллов за работу.** Пример решение нашей задачи: | def print_tags(model, data):
model.eval()
with torch.no_grad():
words, _ = data
example = torch.LongTensor([WORD.vocab.stoi[elem] for elem in words]).unsqueeze(1).to(device)
output = model(example).argmax(dim=-1).cpu().numpy()
tags = [TAG.vocab.itos[int(elem)] for e... | From NOUN
what DET
I NOUN
was VERB
able ADJ
to PRT
gauge NOUN
in ADP
a DET
swift ADJ
, .
greedy NOUN
glance NOUN
, .
the DET
figure NOUN
inside ... | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 |
Вывод: **(0.5 балл)**Правильный подбор параметров дает большую точность, также достаточное количество эпох позволяет достичь хорошей точности, однако модель может переобучится | _____no_output_____ | MIT | FastStart/module_3_pos_tag.ipynb | Xrenya/RuCode2020 | |
UI for your Machine Learning model Install Gradio | pip install gradio | Requirement already satisfied: gradio in d:\anaconda3\lib\site-packages (1.2.3)
Requirement already satisfied: flask in d:\anaconda3\lib\site-packages (from gradio) (1.1.2)
Requirement already satisfied: numpy in d:\anaconda3\lib\site-packages (from gradio) (1.18.5)
Requirement already satisfied: analytics-python in d:... | MIT | ui-for-ml-using-gradio.ipynb | rajtilak82/how-machines-learn |
Import the required libraries | import gradio as gr # for creating the UI
import numpy as np # for preprocessing images
import requests # for downloading human readable labels
from keras.applications.vgg16 import VGG16 # VGG16 model
from keras.applications.vgg16 import preprocess_input # VGG16 preprocessing function | _____no_output_____ | MIT | ui-for-ml-using-gradio.ipynb | rajtilak82/how-machines-learn |
Loading the model | vgg_model = VGG16() | _____no_output_____ | MIT | ui-for-ml-using-gradio.ipynb | rajtilak82/how-machines-learn |
Download the human readable labels | response = requests.get("https://raw.githubusercontent.com/gradio-app/mobilenet-example/master/labels.txt")
labels = response.text.split("\n") | _____no_output_____ | MIT | ui-for-ml-using-gradio.ipynb | rajtilak82/how-machines-learn |
Creating the classification pipeline | # this pipeline returns a dictionary with key as label and
# values as the predicted confidence for that label
def classify_image(image):
image = image.reshape((-1, 224, 224, 3)) # reshaping the image
image = preprocess_input(image) # prepare the image for the VGG16 model
prediction = vgg_model.predict(i... | _____no_output_____ | MIT | ui-for-ml-using-gradio.ipynb | rajtilak82/how-machines-learn |
Initializing the input and output components | image = gr.inputs.Image(shape = (224, 224, 3))
label = gr.outputs.Label(num_top_classes = 3) # predicts the top 3 classes | _____no_output_____ | MIT | ui-for-ml-using-gradio.ipynb | rajtilak82/how-machines-learn |
Launching the Gradio interface with our VGG16 model | gr.Interface(fn = classify_image, inputs = image,
outputs = label, capture_session = True).launch() | Running locally at: http://127.0.0.1:7860/
To get a public link for a hosted model, set Share=True
Interface loading below...
| MIT | ui-for-ml-using-gradio.ipynb | rajtilak82/how-machines-learn |
Activation Function | # Previous lecture we learn about neuron on action, but what actually is neuron ?
# Every neuron have a weight, and they calculate the weight using activation function.
# In this code, you will learn about 4 different activation function. | _____no_output_____ | Apache-2.0 | Day_2_Activation_Function.ipynb | LukasPurbaW/100_Days_of_Deep_Learning |
Threshold Function | import numpy as np
import matplotlib.pyplot as plt
import numpy as np
def binaryStep(x):
''' It returns '0' is the input is less then zero otherwise it returns one '''
return np.heaviside(x,1)
x = np.linspace(-10, 10)
plt.plot(x, binaryStep(x))
plt.axis('tight')
plt.title('Activation Function (Threshold Funct... | _____no_output_____ | Apache-2.0 | Day_2_Activation_Function.ipynb | LukasPurbaW/100_Days_of_Deep_Learning |
Sigmoid Function | def sigmoid(x):
''' It returns 1/(1+exp(-x)). where the values lies between zero and one '''
return 1/(1+np.exp(-x))
x = np.linspace(-10, 10)
plt.plot(x, sigmoid(x))
plt.axis('tight')
plt.title('Activation Function (Sigmoid)')
plt.show()
## The output is equal to 1/(1+np.exp(-x)). Unlike threshold functions, t... | _____no_output_____ | Apache-2.0 | Day_2_Activation_Function.ipynb | LukasPurbaW/100_Days_of_Deep_Learning |
Rectifier or Relu | def RELU(x):
''' It returns zero if the input is less than zero otherwise it returns the given input. '''
x1=[]
for i in x:
if i<0:
x1.append(0)
else:
x1.append(i)
return x1
x = np.linspace(-10, 10)
plt.plot(x, RELU(x))
plt.axis('tight')
plt.title('Activation Fun... | _____no_output_____ | Apache-2.0 | Day_2_Activation_Function.ipynb | LukasPurbaW/100_Days_of_Deep_Learning |
Hyperpolic or Tanh Function | def tanh(x):
''' It returns the value (1-exp(-2x))/(1+exp(-2x)) and the value returned will be lies in between -1 to 1.'''
return np.tanh(x)
x = np.linspace(-10, 10)
plt.plot(x, tanh(x))
plt.axis('tight')
plt.title('Activation Function (Tanh)')
plt.show()
## This return the minimum value of -1 and maximum value... | _____no_output_____ | Apache-2.0 | Day_2_Activation_Function.ipynb | LukasPurbaW/100_Days_of_Deep_Learning |
Softmax Function | def softmax(x):
''' Compute softmax values for each sets of scores in x. '''
return np.exp(x) / np.sum(np.exp(x), axis=0)
x = np.linspace(-10, 10)
plt.plot(x, softmax(x))
plt.axis('tight')
plt.title('Activation Function :Softmax')
plt.show()
## Sigmoid is a smooth graphic like sigmoid. Often used in multiclass ... | _____no_output_____ | Apache-2.0 | Day_2_Activation_Function.ipynb | LukasPurbaW/100_Days_of_Deep_Learning |
Model Evaluation and RefinementEstimated time needed: **30** minutes ObjectivesAfter completing this lab you will be able to:- Evaluate and refine prediction models Table of content Model Evaluation Over-fitting, Under-fitting and Model Selection Ridge Regression Grid Search This dataset was hoste... | import pandas as pd
import numpy as np
# Import clean data
path = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/module_5_auto.csv'
df = pd.read_csv(path)
df.to_csv('module_5_auto.csv') | _____no_output_____ | MIT | DA0101EN/.ipynb_checkpoints/model-evaluation-and-refinement-checkpoint.ipynb | alekhaya99/IBM-CLOUD-SQL-AND-PYTHON |
First lets only use numeric data | df=df._get_numeric_data()
df.head() | _____no_output_____ | MIT | DA0101EN/.ipynb_checkpoints/model-evaluation-and-refinement-checkpoint.ipynb | alekhaya99/IBM-CLOUD-SQL-AND-PYTHON |
Libraries for plotting | %%capture
! pip install ipywidgets
from ipywidgets import interact, interactive, fixed, interact_manual | _____no_output_____ | MIT | DA0101EN/.ipynb_checkpoints/model-evaluation-and-refinement-checkpoint.ipynb | alekhaya99/IBM-CLOUD-SQL-AND-PYTHON |
Functions for plotting | def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):
width = 12
height = 10
plt.figure(figsize=(width, height))
ax1 = sns.distplot(RedFunction, hist=False, color="r", label=RedName)
ax2 = sns.distplot(BlueFunction, hist=False, color="b", label=BlueName, ax=ax1)
plt.title(... | _____no_output_____ | MIT | DA0101EN/.ipynb_checkpoints/model-evaluation-and-refinement-checkpoint.ipynb | alekhaya99/IBM-CLOUD-SQL-AND-PYTHON |
Part 1: Training and TestingAn important step in testing your model is to split your data into training and testing data. We will place the target data price in a separate dataframe y: | y_data = df['price'] | _____no_output_____ | MIT | DA0101EN/.ipynb_checkpoints/model-evaluation-and-refinement-checkpoint.ipynb | alekhaya99/IBM-CLOUD-SQL-AND-PYTHON |
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