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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Connect to trinoThe following cell creates a trino api connection.It assumes that your `credentials.env` file has been edited so that`TRINO_PASSWD` has a JWT token obtained from:https://das-odh-trino.apps.odh-cl1.apps.os-climate.org/Your `TRINO_USER` value should be your github username. | import trino
conn = trino.dbapi.connect(
host=os.environ['TRINO_HOST'],
port=int(os.environ['TRINO_PORT']),
user=os.environ['TRINO_USER'],
http_scheme='https',
auth=trino.auth.JWTAuthentication(os.environ['TRINO_PASSWD']),
verify=True,
)
cur = conn.cursor() | _____no_output_____ | FTL | notebooks/test-trino-access.ipynb | os-climate/data-platform-demo |
Test your trino connectionThis cell shows all the catalogs visible to you.If your trino api connection initialized correctly above,this `show catalogs` command should always succeed. | cur.execute('show catalogs')
cur.fetchall() | _____no_output_____ | FTL | notebooks/test-trino-access.ipynb | os-climate/data-platform-demo |
Gaussian discriminant analysis con stessa matrice di covarianza per le distribuzioni delle due classi e conseguente separatore lineare. Implementata in scikit-learn. Valutazione con cross validation. | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import pandas as pd
import numpy as np
import scipy.stats as st
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_val_score
import sklearn.metrics as mt
import matplotlib.pyplot as plt
impo... | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Leggiamo i dati da un file csv in un dataframe pandas. I dati hanno 3 valori: i primi due corrispondono alle features e sono assegnati alle colonne x1 e x2 del dataframe; il terzo è il valore target, assegnato alla colonna t. Vengono poi creati una matrice X delle features e un vettore target t | # legge i dati in dataframe pandas
data = pd.read_csv("../../data/ex2data1.txt", header= None,delimiter=',', names=['x1','x2','t'])
# calcola dimensione dei dati
n = len(data)
n0 = len(data[data.t==0])
# calcola dimensionalità delle features
features = data.columns
nfeatures = len(features)-1
X = np.array(data[featu... | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Visualizza il dataset. | fig = plt.figure(figsize=(16,8))
ax = fig.gca()
ax.scatter(data[data.t==0].x1, data[data.t==0].x2, s=40, color=colors[0], alpha=.7)
ax.scatter(data[data.t==1].x1, data[data.t==1].x2, s=40,c=colors[1], alpha=.7)
plt.xlabel('$x_1$', fontsize=12)
plt.ylabel('$x_2$', fontsize=12)
plt.xticks(fontsize=10)
plt.yticks(fontsize... | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Definisce un classificatore basato su GDA quadratica ed effettua il training sul dataset. | clf = LinearDiscriminantAnalysis(store_covariance=True)
clf.fit(X, t) | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Definiamo la griglia 100x100 da utilizzare per la visualizzazione delle varie distribuzioni. | # insieme delle ascisse dei punti
u = np.linspace(min(X[:,0]), max(X[:,0]), 100)
# insieme delle ordinate dei punti
v = np.linspace(min(X[:,1]), max(X[:,1]), 100)
# deriva i punti della griglia: il punto in posizione i,j nella griglia ha ascissa U(i,j) e ordinata V(i,j)
U, V = np.meshgrid(u, v) | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Calcola sui punti della griglia le probabilità delle classi $p(x|C_0), p(x|C_1)$ e le probabilità a posteriori delle classi $p(C_0|x), p(C_1|x)$ | # probabilità a posteriori delle due distribuzioni sulla griglia
Z = clf.predict_proba(np.c_[U.ravel(), V.ravel()])
pp0 = Z[:, 0].reshape(U.shape)
pp1 = Z[:, 1].reshape(V.shape)
# rapporto tra le probabilità a posteriori delle classi per tutti i punti della griglia
z=pp0/pp1
# probabilità per le due classi sulla gr... | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Visualizzazione della distribuzione di $p(x|C_0)$ | fig = plt.figure(figsize=(16,8))
ax = fig.gca()
# inserisce una rappresentazione della probabilità della classe C0 sotto forma di heatmap
imshow_handle = plt.imshow(p0, origin='lower', extent=(min(X[:,0]), max(X[:,0]), min(X[:,1]), max(X[:,1])), alpha=.7)
plt.contour(U, V, p0, linewidths=[.7], colors=[colors[6]])
# rap... | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Visualizzazione della distribuzione di $p(x|C1)$ | fig = plt.figure(figsize=(16,8))
ax = fig.gca()
# inserisce una rappresentazione della probabilità della classe C0 sotto forma di heatmap
imshow_handle = plt.imshow(p1, origin='lower', extent=(min(X[:,0]), max(X[:,0]), min(X[:,1]), max(X[:,1])), alpha=.7)
plt.contour(U, V, p1, linewidths=[.7], colors=[colors[6]])
# rap... | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Visualizzazione di $p(C_0|x)$ | fig = plt.figure(figsize=(8,8))
ax = fig.gca()
imshow_handle = plt.imshow(pp0, origin='lower', extent=(min(X[:,0]), max(X[:,0]), min(X[:,1]), max(X[:,1])), alpha=.7)
ax.scatter(data[data.t==0].x1, data[data.t==0].x2, s=40, c=colors[0], alpha=.7)
ax.scatter(data[data.t==1].x1, data[data.t==1].x2, s=40,c=colors[1], alpha... | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Visualizzazione di $p(C_1|x)$ | fig = plt.figure(figsize=(8,8))
ax = fig.gca()
imshow_handle = plt.imshow(pp1, origin='lower', extent=(min(X[:,0]), max(X[:,0]), min(X[:,1]), max(X[:,1])), alpha=.7)
ax.scatter(data[data.t==0].x1, data[data.t==0].x2, s=40, c=colors[0], alpha=.7)
ax.scatter(data[data.t==1].x1, data[data.t==1].x2, s=40,c=colors[1], alpha... | _____no_output_____ | MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Applica la cross validation (5-fold) per calcolare l'accuracy effettuando la media sui 5 valori restituiti. | print("Accuracy: {0:5.3f}".format(cross_val_score(clf, X, t, cv=5, scoring='accuracy').mean())) | Accuracy: 0.870
| MIT | codici/.ipynb_checkpoints/gda-lin-sk-cv-checkpoint.ipynb | tvml/fo2021 |
Parte 04Nessa parte os modelos criados anteriormente serão utilizados para realizar predições. Para isso, eles devem serregistrados no TFX. Para efetuar as predições, os dados utilizados no treinamento desses modelos serão inseridosno SAVIME, o qual ficará encarregado de enviar e receber os dados para/de TFX. | import os
import sys
# Necessário mudar o diretório de trabalho para o nível mais acima
if not 'notebooks' in os.listdir('.'):
current_dir = os.path.abspath(os.getcwd())
parent_dir = os.path.dirname(current_dir)
os.chdir(parent_dir)
# Inserir aqui o caminho do arquivo de dados: um json contendo informaçõe... | _____no_output_____ | MIT | notebooks-pt/Tutorial - Parte 04.ipynb | dnasc/savime-notebooks |
A primeira etapa a ser realizada é converter os dados para um formato processável para o SAVIME. | with h5py.File(dataset_path, 'r') as in_:
array = in_['real'][...]
# Especificar dimensões
time_series = ('time_series', range(array.shape[0]))
time_step = ('time_step', range(array.shape[1]))
pos_x = ('pos_x', range(array.shape[2]))
pos_y = ('pos_y', range(array.shape[3]))
# Remover última dimensão espúria
s... | _____no_output_____ | MIT | notebooks-pt/Tutorial - Parte 04.ipynb | dnasc/savime-notebooks |
Também é nessário fazer a divisão do conjunto de dados de entrada em x e y. Como dito na parte anterior, cada série temporal possuí 10 instantes de tempo. Além disso, os modelos foram treinados a prever o décimo instante de tempo a partir dos nove anteriores. A critério de exemplo, selecionamos abaixo um grupo de série... | # Seleciona-se apenas um grupo para predição.
chosen_model_name = data['model']
chosen_group_ix = 0
x = squeezed_array[[chosen_group_ix], :-1]
y = squeezed_array[[chosen_group_ix], 1:]
pc = PredictionConsumer(host=tfx_host, port=tfx_port, model_name=chosen_model_name)
y_hat = pc.predict(x)
anim_y = animate_heat_map(n... | _____no_output_____ | MIT | notebooks-pt/Tutorial - Parte 04.ipynb | dnasc/savime-notebooks |
Abaixo verificamos se os dados foram corretamente registrados no SAVIME. | with pysavime.Client(host=savime_host, port=savime_port, raise_silent_error=True) as client:
response = client.execute(pysavime.operator.select(tar))[0]
is_the_same = np.isclose(response.attrs['temperature'].reshape(squeezed_array.shape),squeezed_array).all()
print('Checagem:', is_the_same) | Checagem: True
| MIT | notebooks-pt/Tutorial - Parte 04.ipynb | dnasc/savime-notebooks |
O próximo passo é executar o comando PREDICT. | # Vamos selecionar apenas os 9 primeiros instantes de tempo
cmd = pysavime.operator.subset(tar, time_step_dim.name, 0, 8)
# Definir as dimensões de entrada e saída do nosso modelo
input_dims_spec = [(group_dim.name, num_groups),
(time_step_dim.name, num_time_steps - 1),
(pos_x_... | _____no_output_____ | MIT | notebooks-pt/Tutorial - Parte 04.ipynb | dnasc/savime-notebooks |
Convolutional Neural Networks: ApplicationWelcome to Course 4's second assignment! In this notebook, you will:- Implement helper functions that you will use when implementing a TensorFlow model- Implement a fully functioning ConvNet using TensorFlow **After this assignment you will be able to:**- Build and train a Con... | import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *
%matplotlib inline
np.random.seed(1) | _____no_output_____ | MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
Run the next cell to load the "SIGNS" dataset you are going to use. | # Loading the data (signs)
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset() | _____no_output_____ | MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
As a reminder, the SIGNS dataset is a collection of 6 signs representing numbers from 0 to 5.The next cell will show you an example of a labelled image in the dataset. Feel free to change the value of `index` below and re-run to see different examples. | # Example of a picture
index = 6
plt.imshow(X_train_orig[index])
print ("y = " + str(np.squeeze(Y_train_orig[:, index]))) | y = 2
| MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
In Course 2, you had built a fully-connected network for this dataset. But since this is an image dataset, it is more natural to apply a ConvNet to it.To get started, let's examine the shapes of your data. | X_train = X_train_orig/255.
X_test = X_test_orig/255.
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
... | number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 6)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 6)
| MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
1.1 - Create placeholdersTensorFlow requires that you create placeholders for the input data that will be fed into the model when running the session.**Exercise**: Implement the function below to create placeholders for the input image X and the output Y. You should not define the number of training examples for the m... | # GRADED FUNCTION: create_placeholders
def create_placeholders(n_H0, n_W0, n_C0, n_y):
"""
Creates the placeholders for the tensorflow session.
Arguments:
n_H0 -- scalar, height of an input image
n_W0 -- scalar, width of an input image
n_C0 -- scalar, number of channels of the input
n_... | X = Tensor("Placeholder:0", shape=(?, 64, 64, 3), dtype=float32)
Y = Tensor("Placeholder_1:0", shape=(?, 6), dtype=float32)
| MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
**Expected Output** X = Tensor("Placeholder:0", shape=(?, 64, 64, 3), dtype=float32) Y = Tensor("Placeholder_1:0", shape=(?, 6), dtype=float32) 1.2 - Initialize parametersYou will initialize weights/filters $W1$ and $W2$ using `tf.contrib.layers.xavier_initializer(seed = 0)`. You don't need to worry about bias ... | # GRADED FUNCTION: initialize_parameters
def initialize_parameters():
"""
Initializes weight parameters to build a neural network with tensorflow. The shapes are:
W1 : [4, 4, 3, 8]
W2 : [2, 2, 8, 16]
Note that we will hard code the shape values in the functio... | W1[1,1,1] =
[ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394
-0.06847463 0.05245192]
W1.shape: (4, 4, 3, 8)
W2[1,1,1] =
[-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058
-0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228
-0.22779644 -0.1601823 -0.... | MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
** Expected Output:**```W1[1,1,1] = [ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394 -0.06847463 0.05245192]W1.shape: (4, 4, 3, 8)W2[1,1,1] = [-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058 -0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228 -0.22779644 ... | # GRADED FUNCTION: forward_propagation
def forward_propagation(X, parameters):
"""
Implements the forward propagation for the model:
CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
Note that for simplicity and grading purposes, we'll hard-code some values
su... | Z3 =
[[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064]
[-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]]
| MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
**Expected Output**:```Z3 = [[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064] [-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]]``` 1.4 - Compute costImplement the compute cost function below. Remember that the cost function helps the neural network see how much the mod... | # GRADED FUNCTION: compute_cost
def compute_cost(Z3, Y):
"""
Computes the cost
Arguments:
Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (number of examples, 6)
Y -- "true" labels vector placeholder, same shape as Z3
Returns:
cost - Tensor of the c... | cost = 2.91034
| MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
**Expected Output**: ```cost = 2.91034``` 1.5 Model Finally you will merge the helper functions you implemented above to build a model. You will train it on the SIGNS dataset. **Exercise**: Complete the function below. The model below should:- create placeholders- initialize parameters- forward propagate- compute the ... | # GRADED FUNCTION: model
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009,
num_epochs = 100, minibatch_size = 64, print_cost = True):
"""
Implements a three-layer ConvNet in Tensorflow:
CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
A... | _____no_output_____ | MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
Run the following cell to train your model for 100 epochs. Check if your cost after epoch 0 and 5 matches our output. If not, stop the cell and go back to your code! | _, _, parameters = model(X_train, Y_train, X_test, Y_test) | Cost after epoch 0: 1.917929
Cost after epoch 5: 1.506757
Cost after epoch 10: 0.955359
Cost after epoch 15: 0.845802
Cost after epoch 20: 0.701174
Cost after epoch 25: 0.571977
Cost after epoch 30: 0.518435
Cost after epoch 35: 0.495806
Cost after epoch 40: 0.429827
Cost after epoch 45: 0.407291
Cost after epoch 50: 0... | MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
**Expected output**: although it may not match perfectly, your expected output should be close to ours and your cost value should decrease. **Cost after epoch 0 =** 1.917929 **Cost after epoch 5 =** 1.506757 **Train Accuracy =** 0.940741 ... | fname = "images/thumbs_up.jpg"
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(64,64))
plt.imshow(my_image) | _____no_output_____ | MIT | Convolution_model_Application_v1a.ipynb | Yfyangd/Deep_Learning |
Imports | import requests
import getpass
import pickle
import io
import time
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import itertools | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Loginhttps://www.statistikdaten.bayern.de/genesis/online?Menu=Anmeldungabreadcrumb | username = input()
password = getpass.getpass() | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Test login | class GenesisApi:
def __init__(self, username, password, polling_rate=5):
self.username = username
self.password = password
self.polling_rate = polling_rate
self.__base_url = 'https://www.statistikdaten.bayern.de/genesisWS/rest/2020/'
self.__base_params... | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Download dataNote: This takes a long time | responses_demographic = {}
for year in range(1980, 2020 + 1):
print('Requesting table for the year ' + str(year))
response = genesis.get_table('12411-003r', year)
print('Got data')
responses_demographic[str(year)] = response
responses_area = {}
# 33111-201r 1980, 1984, 1988, 1992, 1996, 2000, 2004, 20... | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Convert to DataFrame | def convert_to_dataframe(response, start_at_line, date_line, header_line):
raw_content = response['Object']['Content']
content = raw_content.split('\n', start_at_line)
date = content[date_line].split(';',1)[0]
csv = io.StringIO(content[header_line] + '\n' + content[start_at_line].split('\n__________', 1... | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Demographic | dfs = list()
for year, response in responses_demographic.items():
df = convert_to_dataframe(response, start_at_line=6, date_line=4, header_line=5)
dfs.append(df)
df_demographic = pd.concat(dfs, axis=0, ignore_index=True)
column_names = df_demographic.columns.values
column_names[0] = 'AGS'
column_names[1] = 'G... | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Area | dfs = list()
for year, response in responses_area.items():
df = convert_to_dataframe(response, start_at_line=10, date_line=5, header_line=8)
column_names = df.columns.values
column_names[0] = 'AGS'
column_names[1] = 'Gemeinde'
df.columns = column_names
for column_name in column_names[2: len(co... | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Combined | df_all = pd.merge(df_area, df_demographic, how='left', on=['AGS', 'Gemeinde', 'date'])
df_all.rename(columns={'Insgesamt_x':'Insgesamt Fläche', 'Insgesamt_y':'Insgesamt Bewohner'}, inplace=True)
df_all | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Save and load data | df_demographic.to_pickle('df_demographic.pickle')
df_area.to_pickle('df_area.pickle')
with open('responses_demographic.pickle', 'wb') as f:
pickle.dump(responses_demographic, f, pickle.HIGHEST_PROTOCOL)
with open('responses_area.pickle', 'wb') as f:
pickle.dump(responses_area, f, pickle.HIGHEST_PROTOCOL)
df_d... | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Categorize | categories = {
"living": [
"Wohnen",
"11000 Wohnbaufläche",
],
"industry": [
"Gewerbe, Industrie",
"Betriebsfläche (ohne Abbauland)",
"Abbauland",
"12100 Industrie und Gewerbe",
"12200 Handel und Dienstleistung",
"12300 Versorgungsanlage",
... | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Rename columns | df_area.rename(columns={"Insgesamt": "total", "Gemeinde": "municipality"}, inplace=True) | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Filter unused municipalities | df_area = df_area[df_area["AGS"] <= 9999] | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Merge demographic data | df_area
df_demographic.drop(["männlich", "weiblich"], axis=1, inplace=True)
df_demographic.rename(columns={"Gemeinde": "municipality", "Insgesamt": "demographic"}, inplace=True)
df_area = pd.merge(df_area, df_demographic, how='left', on=['AGS', 'municipality', 'date']) | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Export to JSON | df_area
df_export = df_area.copy()
df_export['date'] = df_export['date'].dt.strftime('%d.%m.%Y')
with open("data.json", "w", encoding="utf-8") as f:
df_export.to_json(f, orient="records", force_ascii=False) | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Basic graphs | f, ax = plt.subplots(figsize=(7, 7))
ax.set(yscale="log")
g = sns.lineplot(data=df_demographic[(df_demographic['Gemeinde']=='Friedberg, St') | (df_demographic['Gemeinde']=='Augsburg (Krfr.St)') | (df_demographic['Gemeinde']=='Garmisch-Partenkirchen, M')], style='Gemeinde', x='date', y='Insgesamt', ax=ax)
g.set_title('E... | _____no_output_____ | MIT | data/GenesisGrabber.ipynb | yoki31/visualize |
Import data | pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 38)
df = pd.read_csv('zar_dataset.csv')
df.info()
df.head()
df.describe() | _____no_output_____ | MIT | BNN Model.ipynb | mdtycho/Zar-Currency-Prediction-Model |
Create Labels For Data, Classification and Regression Labels | from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
scaler = StandardScaler()
df['target_clf'] = df['target_return'].apply(lambda x: float(x/abs(x)) if x!=0 else -1)
df['target_clf'] = df['target_clf'].apply(lambda x: x if x==1 else 0)
df.rename(columns = {'target_retu... | _____no_output_____ | MIT | BNN Model.ipynb | mdtycho/Zar-Currency-Prediction-Model |
Function for Feeding Data In Batches | def next_batch(num, data, labels):
'''
Return a total of `num` random samples and labels.
'''
idx = np.arange(0 , len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[ i] for i in idx]
labels_shuffle = [labels[ i] for i in idx]
return np.asarray(data_shuffle), np.... | _____no_output_____ | MIT | BNN Model.ipynb | mdtycho/Zar-Currency-Prediction-Model |
Build Bayesian Model | N = 100 # number of rows in a minibatch.
D = 36 # number of features.
K = 2 # number of classes.
# Create a placeholder to hold the data (in minibatches) in a TensorFlow graph.
x = tf.placeholder(tf.float32, [None, D])
# Normal(0,1) priors for the variables. Note that the syntax assumes TensorFlow 1.1.
w = Norma... | _____no_output_____ | MIT | BNN Model.ipynb | mdtycho/Zar-Currency-Prediction-Model |
Compute The Accuracy Distribution For The Bayesian Neural Net | # Compute the accuracy of the model.
# For each sample we compute the predicted class and compare with the test labels.
# Predicted class is defined as the one which as maximum proability.
# We perform this test for each (w,b) in the posterior giving us a set of accuracies
# Finally we make a histogram of accuracies f... | _____no_output_____ | MIT | BNN Model.ipynb | mdtycho/Zar-Currency-Prediction-Model |
Test The Model On Brazil Data, We Should Get A wider Distribution Of Accuracies Brazil | brazil = pd.read_csv('brazil_clean.csv')
brazil.head()
brazil.info()
brazil['Date'] = pd.to_datetime(brazil['Date'])
brazil.head()
brazil.info()
brazil['target_cl'] = brazil['target_cl'].apply(lambda x: x if x==1 else 0)
brazil.rename(columns = {'target_return':'target_reg'}, inplace = True)
brazil.head()
brazil.descri... | _____no_output_____ | MIT | BNN Model.ipynb | mdtycho/Zar-Currency-Prediction-Model |
Train your first modelThis is the second of our [beginner tutorial series](https://github.com/deepjavalibrary/djl/tree/master/jupyter/tutorial) that will take you through creating, training, and running inference on a neural network. In this tutorial, you will learn how to train an image classification model that can ... | // Add the snapshot repository to get the DJL snapshot artifacts
// %mavenRepo snapshots https://oss.sonatype.org/content/repositories/snapshots/
// Add the maven dependencies
%maven ai.djl:api:0.17.0
%maven ai.djl:basicdataset:0.17.0
%maven ai.djl:model-zoo:0.17.0
%maven ai.djl.mxnet:mxnet-engine:0.17.0
%maven org.sl... | _____no_output_____ | Apache-2.0 | jupyter/tutorial/02_train_your_first_model.ipynb | dandansamax/djl |
Step 1: Prepare MNIST dataset for trainingIn order to train, you must create a [Dataset class](https://javadoc.io/static/ai.djl/api/0.17.0/index.html?ai/djl/training/dataset/Dataset.html) to contain your training data. A dataset is a collection of sample input/output pairs for the function represented by your neural n... | int batchSize = 32;
Mnist mnist = Mnist.builder().setSampling(batchSize, true).build();
mnist.prepare(new ProgressBar()); | _____no_output_____ | Apache-2.0 | jupyter/tutorial/02_train_your_first_model.ipynb | dandansamax/djl |
Step 2: Create your ModelNext we will build a model. A [Model](https://javadoc.io/static/ai.djl/api/0.17.0/index.html?ai/djl/Model.html) contains a neural network [Block](https://javadoc.io/static/ai.djl/api/0.17.0/index.html?ai/djl/nn/Block.html) along with additional artifacts used for the training process. It posse... | Model model = Model.newInstance("mlp");
model.setBlock(new Mlp(28 * 28, 10, new int[] {128, 64})); | _____no_output_____ | Apache-2.0 | jupyter/tutorial/02_train_your_first_model.ipynb | dandansamax/djl |
Step 3: Create a TrainerNow, you can create a [`Trainer`](https://javadoc.io/static/ai.djl/api/0.17.0/index.html?ai/djl/training/Trainer.html) to train your model. The trainer is the main class to orchestrate the training process. Usually, they will be opened using a try-with-resources and closed after training is ove... | DefaultTrainingConfig config = new DefaultTrainingConfig(Loss.softmaxCrossEntropyLoss())
//softmaxCrossEntropyLoss is a standard loss for classification problems
.addEvaluator(new Accuracy()) // Use accuracy so we humans can understand how accurate the model is
.addTrainingListeners(TrainingListener.Default... | _____no_output_____ | Apache-2.0 | jupyter/tutorial/02_train_your_first_model.ipynb | dandansamax/djl |
Step 5: Initialize TrainingBefore training your model, you have to initialize all of the parameters with starting values. You can use the trainer for this initialization by passing in the input shape.* The first axis of the input shape is the batch size. This won't impact the parameter initialization, so you can use 1... | trainer.initialize(new Shape(1, 28 * 28)); | _____no_output_____ | Apache-2.0 | jupyter/tutorial/02_train_your_first_model.ipynb | dandansamax/djl |
Step 6: Train your modelNow, we can train the model.When training, it is usually organized into epochs where each epoch trains the model on each item in the dataset once. It is slightly faster than training randomly.Then, we will use the EasyTrain to, as the name promises, make the training easy. If you want to see mo... | // Deep learning is typically trained in epochs where each epoch trains the model on each item in the dataset once.
int epoch = 2;
EasyTrain.fit(trainer, epoch, mnist, null); | _____no_output_____ | Apache-2.0 | jupyter/tutorial/02_train_your_first_model.ipynb | dandansamax/djl |
Step 7: Save your modelOnce your model is trained, you should save it so that it can be reloaded later. You can also add metadata to it such as training accuracy, number of epochs trained, etc that can be used when loading the model or when examining it. | Path modelDir = Paths.get("build/mlp");
Files.createDirectories(modelDir);
model.setProperty("Epoch", String.valueOf(epoch));
model.save(modelDir, "mlp");
model | _____no_output_____ | Apache-2.0 | jupyter/tutorial/02_train_your_first_model.ipynb | dandansamax/djl |
HSV feature | train_x = []
train_y = []
train_dir = os.path.join(dest, "random_forest_train")
for img in os.listdir(train_dir):
img_id = int(img.split(".")[0])
train_y.append(int(img_id2class[img_id]))
train_x.append(extract_hist_feature(cv2.imread(os.path.join(train_dir, img)), rgb=False))
classifier = RandomForestClass... | _____no_output_____ | MIT | random_forest/color.ipynb | den8972/228 |
RGB feature | train_x = []
train_y = []
train_dir = os.path.join(dest, "random_forest_train")
for img in os.listdir(train_dir):
img_id = int(img.split(".")[0])
train_y.append(int(img_id2class[img_id]))
train_x.append(extract_hist_feature(cv2.imread(os.path.join(train_dir, img)), rgb=True))
classifier = RandomForestClassi... | _____no_output_____ | MIT | random_forest/color.ipynb | den8972/228 |
Transfer LearningMost of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this notebo... | from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
print(device_lib)
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't e... | Parameter file already exists!
| MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
Flower powerHere we'll be using VGGNet to classify images of flowers. To get the flower dataset, run the cell below. This dataset comes from the [TensorFlow inception tutorial](https://www.tensorflow.org/tutorials/image_retraining). | import tarfile
dataset_folder_path = 'flower_photos'
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile('flower_ph... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
ConvNet CodesBelow, we'll run through all the images in our dataset and get codes for each of them. That is, we'll run the images through the VGGNet convolutional layers and record the values of the first fully connected layer. We can then write these to a file for later when we build our own classifier.Here we're usi... | import os
import numpy as np
import tensorflow as tf
from tensorflow_vgg import vgg16
from tensorflow_vgg import utils
data_dir = 'flower_photos/'
contents = os.listdir(data_dir)
classes = [each for each in contents if os.path.isdir(data_dir + each)] | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
Below I'm running images through the VGG network in batches. | # Set the batch size higher if you can fit in in your GPU memory
batch_size = 10
codes_list = []
labels = []
batch = []
codes = None
with tf.Session() as sess:
vgg = vgg16.Vgg16()
input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
with tf.name_scope("content_vgg"):
vgg.build(input_)
for... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
Building the ClassifierNow that we have codes for all the images, we can build a simple classifier on top of them. The codes behave just like normal input into a simple neural network. Below I'm going to have you do most of the work. | # read codes and labels from file
import csv
with open('labels') as f:
reader = csv.reader(f, delimiter='\n')
labels = np.array([each for each in reader if len(each) > 0]).squeeze()
with open('codes') as f:
codes = np.fromfile(f, dtype=np.float32)
codes = codes.reshape((len(labels), -1)) | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
Data prepAs usual, now we need to one-hot encode our labels and create validation/test sets. First up, creating our labels!> **Exercise:** From scikit-learn, use [LabelBinarizer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html) to create one-hot encoded vectors from the label... | codes
from sklearn.preprocessing import LabelBinarizer
lb = LabelBinarizer()
lb.fit(labels)
labels_vecs = lb.transform(labels)
labels_vecs | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
Now you'll want to create your training, validation, and test sets. An important thing to note here is that our labels and data aren't randomized yet. We'll want to shuffle our data so the validation and test sets contain data from all classes. Otherwise, you could end up with testing sets that are all one class. Typic... | from sklearn.model_selection import StratifiedShuffleSplit
ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)
train_idx, val_idx = next(ss.split(codes, labels_vecs))
half_val_len = int(len(val_idx)/2)
val_idx, test_idx = val_idx[:half_val_len], val_idx[half_val_len:]
train_x, train_y = codes[train_idx], labels_... | Train shapes (x, y): (2936, 4096) (2936, 5)
Validation shapes (x, y): (367, 4096) (367, 5)
Test shapes (x, y): (367, 4096) (367, 5)
| MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
If you did it right, you should see these sizes for the training sets:```Train shapes (x, y): (2936, 4096) (2936, 5)Validation shapes (x, y): (367, 4096) (367, 5)Test shapes (x, y): (367, 4096) (367, 5)``` Classifier layersOnce you have the convolutional codes, you just need to build a classfier from some fully connec... | inputs_ = tf.placeholder(tf.float32, shape=[None, codes.shape[1]])
labels_ = tf.placeholder(tf.int64, shape=[None, labels_vecs.shape[1]])
fc = tf.contrib.layers.fully_connected(inputs_, 256)
logits = tf.contrib.layers.fully_connected(fc, labels_vecs.shape[1], activation_fn=None)
cross_entropy = tf.nn.softmax_cros... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
Batches!Here is just a simple way to do batches. I've written it so that it includes all the data. Sometimes you'll throw out some data at the end to make sure you have full batches. Here I just extend the last batch to include the remaining data. | def get_batches(x, y, n_batches=10):
""" Return a generator that yields batches from arrays x and y. """
batch_size = len(x)//n_batches
for ii in range(0, n_batches*batch_size, batch_size):
# If we're not on the last batch, grab data with size batch_size
if ii != (n_batches-1)*batch_siz... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
TrainingHere, we'll train the network.> **Exercise:** So far we've been providing the training code for you. Here, I'm going to give you a bit more of a challenge and have you write the code to train the network. Of course, you'll be able to see my solution if you need help. | epochs = 10
iteration = 0
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for e in range(epochs):
for x, y in get_batches(train_x, train_y):
feed = {inputs_: x,
labels_: y}
loss, _ = sess.run([cost, optimize... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
TestingBelow you see the test accuracy. You can also see the predictions returned for images. | with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
feed = {inputs_: test_x,
labels_: test_y}
test_acc = sess.run(accuracy, feed_dict=feed)
print("Test accuracy: {:.4f}".format(test_acc))
%matplotlib inline
import matplotlib.pyplot as plt
from scip... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
Below, feel free to choose images and see how the trained classifier predicts the flowers in them. | test_img_path = 'flower_photos/roses/10894627425_ec76bbc757_n.jpg'
test_img = imread(test_img_path)
plt.imshow(test_img)
# Run this cell if you don't have a vgg graph built
with tf.Session() as sess:
input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
vgg = vgg16.Vgg16()
vgg.build(input_)
with tf.Sessi... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning_Solution.ipynb | freedomkwok/deep-learning |
Set up AWS Panorama development environment on SageMaker NotebookThis notebook installs dependencies required for AWS Panorama application development. Run following cells just once, before starting labs. | %pip install panoramacli
%pip install mxnet
%pip install gluoncv
!./scripts/install-docker.sh
# for CPU build
!./scripts/install-dlr.sh
# for p2/p3/g4 instance, we could use pre-built package to skip long building time
#%pip install https://neo-ai-dlr-release.s3-us-west-2.amazonaws.com/v1.10.0/gpu/dlr-1.10.0-py3-none-... | _____no_output_____ | MIT-0 | setup-sm.ipynb | aws-samples/aws-panorama-immersion-day |
Transfer learning with Tensorflow在这个Notebook当中，我们将介绍如何实用Tensorflow框架实现迁移学习。简单的来说，迁移学习就是利用已经训练好的模型中学习到的特征（Features），再根据用户需要添加额外的网络层，进行快速的针对新的特定的数据集的模型训练。由于这样生成的模型大部分的模型参数已经训练好并且已经学习到一定数量的hidden features，在提供新的数据集的时候再进行训练就能有效利用已学习到的知识来进行预测。本notebook所使用的预训练好的模型是MobileNet V2，其具体的原理就留给负责这部分的同学在后续具体介绍。我们当前只需要了解其大致的网络结构即可（结构如... | import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
from tensorflow.keras.preprocessing import image_dataset_from_directory | _____no_output_____ | MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
1. 数据预处理 数据集分组在这里我们数据集已经按照路径分为Train集和Validation集。 | PATH = os.path.join('./data', 'cats_and_dogs_filtered')
train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')
BATCH_SIZE = 32
IMG_SIZE = (160, 160)
train_dataset = image_dataset_from_directory(train_dir,
shuffle=True,
... | Found 2000 files belonging to 2 classes.
Found 1000 files belonging to 2 classes.
| MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
查看数据集中的样本 | class_names = train_dataset.class_names
plt.figure(figsize=(10, 10))
for images, labels in train_dataset.take(1):
for i in range(9):
ax = plt.subplot(3, 3, i+1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])
plt.axis("off") | _____no_output_____ | MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
将图片像素值归一化 | rescale_input = tf.keras.layers.experimental.preprocessing.Rescaling(1./255, offset=0) | _____no_output_____ | MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
2.组合模型 载入预训练好的模型（MobileNet-v2） | IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')
image_batch, label_batch = next(iter(train_dataset))
feature_batch = base_model(image_bat... | (32, 5, 5, 1280)
| MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
特征提取（Feature Extraction） | # Freeze the MobileNet
base_model.trainable = False
base_model.summary()
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
feature_batch_average = global_average_layer(feature_batch)
print(feature_batch_average.shape)
prediction_layer = tf.keras.layers.Dense(1)
prediction_batch = prediction_layer(feature_... | (32, 1)
| MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
将上述的各个层集合成新的 Model | inputs = tf.keras.Input(shape=(160, 160, 3))
x = rescale_input(inputs)
x = base_model(x, training=False)
x = tf.keras.layers.Dropout(0.2)(x)
outputs = prediction_layer(x)
model = tf.keras.Model(inputs, outputs) | _____no_output_____ | MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
编译模型 | lr = 0.0001
model.compile(optimizer=tf.keras.optimizers.Adam(lr=lr),
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics='accuracy')
model.summary() | Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) [(None, 160, 160, 3)] 0
_______________________________________... | MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
3.训练模型 迁移学习后的Validation准确率 | epochs = 10
history = model.fit(train_dataset,
epochs=initial_epochs,
validation_data=validation_dataset) | Epoch 1/10
63/63 [==============================] - 33s 501ms/step - loss: 0.6727 - accuracy: 0.5925 - val_loss: 0.5018 - val_accuracy: 0.7060
Epoch 2/10
63/63 [==============================] - 23s 360ms/step - loss: 0.4419 - accuracy: 0.7635 - val_loss: 0.3507 - val_accuracy: 0.8390
Epoch 3/10
63/63 [================... | MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
学习曲线 | acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.yl... | _____no_output_____ | MIT | TF-transfer.ipynb | HAXRD/TF2vsPTH |
基本程序设计- 一切代码输入，请使用英文输入法 编写一个简单的程序- 圆公式面积： area = radius \* radius \* 3.1415 在Python里面不需要定义数据的类型 控制台的读取与输入- input 输入进去的是字符串- eval - 在jupyter用shift + tab 键可以跳出解释文档 变量命名的规范- 由字母、数字、下划线构成- 不能以数字开头 \*- 标识符不能是关键词(实际上是可以强制改变的，但是对于代码规范而言是极其不适合)- 可以是任意长度- 驼峰式命名 变量、赋值语句和赋值表达式- 变量: 通俗理解为可以变化的量- x = 2 \* x + 1 在数学中是一个方程，而在语言... | C=input()
F=float((9/5))*float(C)+32
print(F) | 43
109.4
| Apache-2.0 | 7.16.ipynb | liangfhaott3/python |
- 2 | r=input()
h=input()
S=float(r)**2*3.14
V=float(S)*float(h)
print('底面积为：%.2f'%S)
print('体积为：%.2f'%V) | 5.5
12
底面积为：94.98
体积为：1139.82
| Apache-2.0 | 7.16.ipynb | liangfhaott3/python |
- 3 | feet=input()
meters=float(feet)*0.305
print(meters) | 16.5
5.0325
| Apache-2.0 | 7.16.ipynb | liangfhaott3/python |
- 4 | k=input()
t1=input()
t2=input()
q=float(k)*(float(t2)-float(t1))*4184
print(q) | 55.5
3.5
10.5
1625484.0
| Apache-2.0 | 7.16.ipynb | liangfhaott3/python |
- 5 | ce=input()
nll=input()
lx=float(ce)*(float(nll)/1200)
print('%.5f'%lx) | 1000
3.5
2.91667
| Apache-2.0 | 7.16.ipynb | liangfhaott3/python |
- 6 | v0=input()
v1=input()
t=input()
pj=(float(v1)-float(v0))/float(t)
print('%.4f'%pj) | 5.5
50.9
4.5
10.0889
| Apache-2.0 | 7.16.ipynb | liangfhaott3/python |
- 7 进阶 | amout=input()
money=0
for i in range(6):
money=(float(amout)+float(money))*(1+0.00417)
print('%.2f'%money)
| 100
608.82
| Apache-2.0 | 7.16.ipynb | liangfhaott3/python |
- 8 进阶 | number=int(input())
if (number>1000)or (number<=0):
print('false')
else:
gewei=number%10
shiwei=(number//10)%10
baiwei=number//100
sum=gewei+shiwei+baiwei
print(sum)
| 999
27
| Apache-2.0 | 7.16.ipynb | liangfhaott3/python |
PTN TemplateThis notebook serves as a template for single dataset PTN experiments It can be run on its own by setting STANDALONE to True (do a find for "STANDALONE" to see where) But it is intended to be executed as part of a *papermill.py script. See any of the experimentes with a papermill script to get started ... | %load_ext autoreload
%autoreload 2
%matplotlib inline
import os, json, sys, time, random
import numpy as np
import torch
from torch.optim import Adam
from easydict import EasyDict
import matplotlib.pyplot as plt
from steves_models.steves_ptn import Steves_Prototypical_Network
from steves_utils.lazy_iterable_wr... | _____no_output_____ | MIT | experiments/tuned_1v2/oracle.run1/trials/2/trial.ipynb | stevester94/csc500-notebooks |
Required ParametersThese are allowed parameters, not defaultsEach of these values need to be present in the injected parameters (the notebook will raise an exception if they are not present)Papermill uses the cell tag "parameters" to inject the real parameters below this cell.Enable tags to see what I mean | required_parameters = {
"experiment_name",
"lr",
"device",
"seed",
"dataset_seed",
"labels_source",
"labels_target",
"domains_source",
"domains_target",
"num_examples_per_domain_per_label_source",
"num_examples_per_domain_per_label_target",
"n_shot",
"n_way",
"n_q... | _____no_output_____ | MIT | experiments/tuned_1v2/oracle.run1/trials/2/trial.ipynb | stevester94/csc500-notebooks |
mentions of facebook | # Search for all tweets
# public_tweets = api.search(target_term, count=300, result_type="recent")
# Twitter API Keys
consumer_key = consumer_key
consumer_secret = consumer_secret
access_token = access_token
access_token_secret = access_token_secret
# Setup Tweepy API Authentication
auth = tweepy.OAuthHandler(consumer... | Fri Jun 15 00:10:34 +0000 2018
Thu Jun 14 18:50:53 +0000 2018
| MIT | Kara/.ipynb_checkpoints/grabbing_tweets_june_14-checkpoint.ipynb | rglukins/stock-tweet |
1.Importing the Relevant Libraries | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import Ridge, Lasso, LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn import metrics
from sklearn.ensemble import RandomFor... | _____no_output_____ | MIT | Wind_tunnel.ipynb | AcharyaRakesh/WindTunnel |
2.Reading Data | df = pd.read_csv("WindTunnel.csv")
df
df = pd.read_csv("WindTunnel.csv")
plt.xlabel('Freqency')
plt.ylabel('Velocity')
plt.plot(df.Freqency,df.Velocity)
reg = linear_model.LinearRegression()
reg.fit(df[["Freqency"]],df.Velocity)
reg.predict([[65.0]])
L=0.2
r=0.1
v=3.45
A=0.0323
CL = (2*L)/(r*(v**2)*A)
CL
df = pd.re... | _____no_output_____ | MIT | Wind_tunnel.ipynb | AcharyaRakesh/WindTunnel |
Data Pre-process |
X_train, X_valid, y_train, y_valid = train_test_split(X,y,test_size = 0.2,random_state = 10)
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_valid)
algos = [LinearRegression(), Ridge(), Lasso(),
KNeighborsRegressor(), DecisionTreeRegressor(),RandomForestRegressor()]
nam... | _____no_output_____ | MIT | Wind_tunnel.ipynb | AcharyaRakesh/WindTunnel |
Building Model |
clf = Ridge()
clf.fit(X_train,y_train)
clf.score(X_test,y_test)
clf.predict([[6.8,0.74,0.0323,0.27]]) | _____no_output_____ | MIT | Wind_tunnel.ipynb | AcharyaRakesh/WindTunnel |
Import packages | # import packages
import requests
from bs4 import BeautifulSoup
url = "https://www.makaan.com/hyderabad-residential-property/rent-property-in-hyderabad-city"
response = requests.get(url)
soup = BeautifulSoup(response.text,"html.parser")
s_tag = soup.find_all('span',attrs={'class' : 'seller-type'})
for each_owner in s... | _____no_output_____ | Apache-2.0 | makaan_webscraping.ipynb | akhila-sakinala/akhila-sakinala.github.io |
Enter State Farm | from theano.sandbox import cuda
cuda.use('gpu0')
%matplotlib inline
from __future__ import print_function, division
path = "data/state/"
#path = "data/state/sample/"
import utils; reload(utils)
from utils import *
from IPython.display import FileLink
batch_size=64 | _____no_output_____ | Apache-2.0 | deeplearning1/nbs/statefarm.ipynb | Fandekasp/fastai_courses |
Setup batches | batches = get_batches(path+'train', batch_size=batch_size)
val_batches = get_batches(path+'valid', batch_size=batch_size*2, shuffle=False)
(val_classes, trn_classes, val_labels, trn_labels,
val_filenames, filenames, test_filenames) = get_classes(path) | Found 18946 images belonging to 10 classes.
Found 3478 images belonging to 10 classes.
Found 79726 images belonging to 1 classes.
| Apache-2.0 | deeplearning1/nbs/statefarm.ipynb | Fandekasp/fastai_courses |
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