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 |
|---|---|---|---|---|---|
Forward propagation for the basic Recurrent Neural Network RNN cell | # GRADED FUNCTION: rnn_cell_forward
def rnn_cell_forward(xt, a_prev, parameters):
# Retrieve parameters from "parameters"
Wax = parameters["Wax"]
Waa = parameters["Waa"]
Wya = parameters["Wya"]
ba = parameters["ba"]
by = parameters["by"]
# compute next activation state using the formu... | a_next[4] =
[ 0.59584544 0.18141802 0.61311866 0.99808218 0.85016201 0.99980978
-0.18887155 0.99815551 0.6531151 0.82872037]
a_next.shape =
(5, 10)
yt_pred[1] =
[ 0.9888161 0.01682021 0.21140899 0.36817467 0.98988387 0.88945212
0.36920224 0.9966312 0.9982559 0.17746526]
yt_pred.shape =
(2,... | MIT | .ipynb_checkpoints/Building_a_Recurrent_Neural_Network_Step_by_Step_v3a-checkpoint.ipynb | aadimangla/Custom-Trained-Recurrent-Neural-Network |
1.2 - RNN forward pass | # GRADED FUNCTION: rnn_forward
def rnn_forward(x, a0, parameters):
# Initialize "caches" which will contain the list of all caches
caches = []
# Retrieve dimensions from shapes of x and parameters["Wya"]
n_x, m, T_x = x.shape
n_y, n_a = parameters["Wya"].shape
# initialize "a"... | a[4][1] =
[-0.99999375 0.77911235 -0.99861469 -0.99833267]
a.shape =
(5, 10, 4)
y_pred[1][3] =
[ 0.79560373 0.86224861 0.11118257 0.81515947]
y_pred.shape =
(2, 10, 4)
caches[1][1][3] =
[-1.1425182 -0.34934272 -0.20889423 0.58662319]
len(caches) =
2
| MIT | .ipynb_checkpoints/Building_a_Recurrent_Neural_Network_Step_by_Step_v3a-checkpoint.ipynb | aadimangla/Custom-Trained-Recurrent-Neural-Network |
Long Short-Term Memory (LSTM) network Overview of gates and states - Forget gate $\mathbf{\Gamma}_{f}$* Let's assume we are reading words in a piece of text, and plan to use an LSTM to keep track of grammatical structures, such as whether the subject is singular ("puppy") or plural ("puppies"). * If the subject chang... | # GRADED FUNCTION: lstm_cell_forward
def lstm_cell_forward(xt, a_prev, c_prev, parameters):
# Retrieve parameters from "parameters"
Wf = parameters["Wf"] # forget gate weight
bf = parameters["bf"]
Wi = parameters["Wi"] # update gate weight (notice the variable name)
bi = parameters["bi"] # (notice ... | a_next[4] =
[-0.66408471 0.0036921 0.02088357 0.22834167 -0.85575339 0.00138482
0.76566531 0.34631421 -0.00215674 0.43827275]
a_next.shape = (5, 10)
c_next[2] =
[ 0.63267805 1.00570849 0.35504474 0.20690913 -1.64566718 0.11832942
0.76449811 -0.0981561 -0.74348425 -0.26810932]
c_next.shape = (5, 10... | MIT | .ipynb_checkpoints/Building_a_Recurrent_Neural_Network_Step_by_Step_v3a-checkpoint.ipynb | aadimangla/Custom-Trained-Recurrent-Neural-Network |
Forward pass for LSTM | # GRADED FUNCTION: lstm_forward
def lstm_forward(x, a0, parameters):
# Initialize "caches", which will track the list of all the caches
caches = []
# Retrieve dimensions from shapes of x and Wy (≈2 lines)
n_x, m, T_x = x.shape
n_y, n_a = parameters["Wy"].shape
# initialize "a", "... | a[4][3][6] = 0.172117767533
a.shape = (5, 10, 7)
y[1][4][3] = 0.95087346185
y.shape = (2, 10, 7)
caches[1][1][1] =
[ 0.82797464 0.23009474 0.76201118 -0.22232814 -0.20075807 0.18656139
0.41005165]
c[1][2][1] -0.855544916718
len(caches) = 2
| MIT | .ipynb_checkpoints/Building_a_Recurrent_Neural_Network_Step_by_Step_v3a-checkpoint.ipynb | aadimangla/Custom-Trained-Recurrent-Neural-Network |
**Expected Output**:```Pythona[4][3][6] = 0.172117767533a.shape = (5, 10, 7)y[1][4][3] = 0.95087346185y.shape = (2, 10, 7)caches[1][1][1] = [ 0.82797464 0.23009474 0.76201118 -0.22232814 -0.20075807 0.18656139 0.41005165]c[1][2][1] -0.855544916718len(caches) = 2``` Backpropagation in recurrent neural networks ... | def rnn_cell_backward(da_next, cache):
# Retrieve values from cache
(a_next, a_prev, xt, parameters) = cache
# Retrieve values from parameters
Wax = parameters["Wax"]
Waa = parameters["Waa"]
Wya = parameters["Wya"]
ba = parameters["ba"]
by = parameters["by"]
# compute the gr... | gradients["dxt"][1][2] = -1.3872130506
gradients["dxt"].shape = (3, 10)
gradients["da_prev"][2][3] = -0.152399493774
gradients["da_prev"].shape = (5, 10)
gradients["dWax"][3][1] = 0.410772824935
gradients["dWax"].shape = (5, 3)
gradients["dWaa"][1][2] = 1.15034506685
gradients["dWaa"].shape = (5, 5)
gradients["dba"][4]... | MIT | .ipynb_checkpoints/Building_a_Recurrent_Neural_Network_Step_by_Step_v3a-checkpoint.ipynb | aadimangla/Custom-Trained-Recurrent-Neural-Network |
**Expected Output**: **gradients["dxt"][1][2]** = -1.3872130506 **gradients["dxt"].shape** = (3, 10) **gradients["da_prev"][2][3]** = -0.1523... | def rnn_backward(da, caches):
# Retrieve values from the first cache (t=1) of caches (≈2 lines)
(caches, x) = caches
(a1, a0, x1, parameters) = caches[0]
# Retrieve dimensions from da's and x1's shapes (≈2 lines)
n_a, m, T_x = da.shape
n_x, m = x1.shape
# initialize ... | gradients["dx"][1][2] = [-2.07101689 -0.59255627 0.02466855 0.01483317]
gradients["dx"].shape = (3, 10, 4)
gradients["da0"][2][3] = -0.314942375127
gradients["da0"].shape = (5, 10)
gradients["dWax"][3][1] = 11.2641044965
gradients["dWax"].shape = (5, 3)
gradients["dWaa"][1][2] = 2.30333312658
gradients["dWaa"].shape ... | MIT | .ipynb_checkpoints/Building_a_Recurrent_Neural_Network_Step_by_Step_v3a-checkpoint.ipynb | aadimangla/Custom-Trained-Recurrent-Neural-Network |
**Expected Output**: **gradients["dx"][1][2]** = [-2.07101689 -0.59255627 0.02466855 0.01483317] **gradients["dx"].shape** = (3, 10, 4) **gradients["da0"][2][3]** = ... | def lstm_cell_backward(da_next, dc_next, cache):
# Retrieve information from "cache"
(a_next, c_next, a_prev, c_prev, ft, it, cct, ot, xt, parameters) = cache
# Retrieve dimensions from xt's and a_next's shape (≈2 lines)
n_x, m = xt.shape
n_a, m = a_next.shape
# Compute gates related d... | gradients["dxt"][1][2] = 3.23055911511
gradients["dxt"].shape = (3, 10)
gradients["da_prev"][2][3] = -0.0639621419711
gradients["da_prev"].shape = (5, 10)
gradients["dc_prev"][2][3] = 0.797522038797
gradients["dc_prev"].shape = (5, 10)
gradients["dWf"][3][1] = -0.147954838164
gradients["dWf"].shape = (5, 8)
gradients["... | MIT | .ipynb_checkpoints/Building_a_Recurrent_Neural_Network_Step_by_Step_v3a-checkpoint.ipynb | aadimangla/Custom-Trained-Recurrent-Neural-Network |
**Expected Output**: **gradients["dxt"][1][2]** = 3.23055911511 **gradients["dxt"].shape** = (3, 10) **gradients["da_prev"][2][3]** = -0.0639... | def lstm_backward(da, caches):
# Retrieve values from the first cache (t=1) of caches.
(caches, x) = caches
(a1, c1, a0, c0, f1, i1, cc1, o1, x1, parameters) = caches[0]
# Retrieve dimensions from da's and x1's shapes (≈2 lines)
n_a, m, T_x = da.shape
n_x, m = x1.shape
# initial... | gradients["dx"][1][2] = [ 0.00433841 0.01182645 -0.01648453 0.20649992]
gradients["dx"].shape = (3, 10, 4)
gradients["da0"][2][3] = 0.176564564406
gradients["da0"].shape = (5, 10)
gradients["dWf"][3][1] = -0.0698198561274
gradients["dWf"].shape = (5, 8)
gradients["dWi"][1][2] = 0.102371820249
gradients["dWi"].shape =... | MIT | .ipynb_checkpoints/Building_a_Recurrent_Neural_Network_Step_by_Step_v3a-checkpoint.ipynb | aadimangla/Custom-Trained-Recurrent-Neural-Network |
Based on http://scikit-learn.org/stable/auto_examples/text/document_clustering.html | from sklearn.datasets import fetch_20newsgroups
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.sv... | Linear Support Vector classifier
precision recall f1-score support
comp.graphics 0.91 0.97 0.94 389
sci.med 0.95 0.90 0.92 396
soc.religion.christian 0.96 0.97 0.96 398
talk.politics.misc 0... | MIT | Document classify - KMeans.ipynb | heolin123/text_analysis |
pandasはpdという名前でインポートされることがほとんどです | import pandas as pd | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
本書第1章で使うデータを読み込みましょう read_csv関数を使うことでcsvをpandasのDataFrameという形式で読み込むことができます | df = pd.read_csv('../data/ch1_sport_test.csv')
df | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
抽出 今回はDataFrameが10行と比較的小規模ですが、数百行・数千行のDataFrameを扱うことも珍しくありません そのようなDataFrameの概観を知るためにDataFrameのheadメソッドがよく使われます headメソッドはDataFrameの先頭の数行を抽出するメソッドです ここでは3行抽出してみましょう | df.head(3) | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
何も指定しないと5行抽出されます | df.head() | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
同様にtailメソッドはDataFrameの末尾を抽出します | df.tail() | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
スライスで抽出することもできます | df[2:5] | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
列は列名によって抽出できます | df['握力'] | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
複数列を同時に指定することもできます | df[['学年', '点数']] | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
行と列を同時に指定して抽出したいときはlocメソッドが便利です たとえば1行目の学年は次のように抽出できます | df.loc[1, '学年'] | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
スライスによる指定も可能です | df.loc[1:3, '握力'] | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
条件に合致する行を抽出したい場合は次のように書きます ここでは握力が30より下の行を抽出しています | df[df['握力'] < 30] | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
データの概要 データの大きさはshapeでわかります | df.shape | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
列名はcolumnsです | df.columns
df.index
df.describe() | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
データの追加 dfにデータを追加してみましょう まずは列の追加です | df['腕立て伏せ'] = [15, 12, 10, 4, 20,
24, 9, 11, 11, 17]
df | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
次は行を追加してみます | new_row = pd.Series([11, 3, 42.4, 30, 15, 11, 12], index=df.columns, name=10)
new_row
df.append(new_row) | _____no_output_____ | BSD-3-Clause | tutorial/pandas.ipynb | tsahara/python_stat |
Data description: I'm going to solve the International Airline Passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. The data ranges from January 1949 to December 1960 or 12 years, with 144 observations.... | import keras
import sklearn
import tensorflow as tf
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from sklearn import preprocessing
import random as rn
import math
%matplotlib inline
from keras import backend as K
sessio... | _____no_output_____ | MIT | Deep Learning with Keras_TensorFlow/Airlline Passengers prediction (with LSTM RNN in Keras-TensorFlow).ipynb | RooHashemi/Data-Science-Projects-with-Python-Spark-Keras_TensorFlow-AWS |
Chemistry for Technology Students at GGC Measurement Uncertainty, Accuracy, and Precision [Textbook: Chapter 1, section 5](https://openstax.org/books/chemistry-2e/pages/1-5-measurement-uncertainty-accuracy-and-precision) Measurement Uncertainty Most measurements are very much _not_ like that. You can easily ima... | d = 1.00 # inches
pi = 3.1415926
c = pi*d
print ("The circumference of the circle is {:.2f} inches.".format(c)) | The circumference of the circle is 3.14 inches.
| MIT | chapters/ch01/sections/chem4tech01_05.ipynb | pscoppock/chem4tech |
Inject activity patterns without plasticity | np.random.seed(7)
input_patterns=S[np.nonzero(U[:,13])[0],:]
input_patterns=zscore(input_patterns,axis=1)
v_lst=[]
w=np.random.normal(loc=0,size=(656,))
v_lst=[np.dot(w,input_patterns[:,0])]
for j in range(1,30560):
v_lst.append(np.dot(w,input_patterns[:,j]))
plt.plot(zscore(v_lst[:100]))
plt.show()
plt.plot(i... | _____no_output_____ | MIT | EnsemblePursuitSynapseExploration.ipynb | mariakesa/DayanAbbottExplorations |
Inject patterns with plasticity | #Using Euler's method to calculate the weight increments
h=0.001
input_patterns=S[np.nonzero(U[:,13])[0],:]
input_patterns=zscore(input_patterns,axis=1)
print(input_patterns.shape)
v_lst=[]
w_lst=[]
w=np.random.normal(loc=0,size=(656,))
v_lst=[np.dot(w,input_patterns[:,1])]
for j in range(0,1000):
v_lst.append(np.d... | _____no_output_____ | MIT | EnsemblePursuitSynapseExploration.ipynb | mariakesa/DayanAbbottExplorations |
Comparing soundings from NCEP Reanalysis and various models We are going to plot the global, annual mean sounding (vertical temperature profile) from observations.Read in the necessary NCEP reanalysis data from the online server.The catalog is here: | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
ncep_url = "https://psl.noaa.gov/thredds/dodsC/Datasets/ncep.reanalysis.derived/"
ncep_air = xr.open_dataset( ncep_url + "pressure/air.mon.1981-2010.ltm.nc", decode_times=False)
level = ncep_air.level
lat = ncep_air.lat | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Take global averages and time averages. | Tzon = ncep_air.air.mean(dim=('lon','time'))
weight = np.cos(np.deg2rad(lat)) / np.cos(np.deg2rad(lat)).mean(dim='lat')
Tglobal = (Tzon * weight).mean(dim='lat') | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Here is code to make a nicely labeled sounding plot. | fig = plt.figure( figsize=(10,8) )
ax = fig.add_subplot(111)
ax.plot( Tglobal + 273.15, np.log(level/1000))
ax.invert_yaxis()
ax.set_xlabel('Temperature (K)', fontsize=16)
ax.set_ylabel('Pressure (hPa)', fontsize=16 )
ax.set_yticks( np.log(level/1000) )
ax.set_yticklabels( level.values )
ax.set_title('Global, annual me... | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Now compute the Radiative Equilibrium solution for the grey-gas column model | import climlab
from climlab import constants as const
col = climlab.GreyRadiationModel()
print(col)
col.subprocess['LW'].diagnostics
col.integrate_years(1)
print("Surface temperature is " + str(col.Ts) + " K.")
print("Net energy in to the column is " + str(col.ASR - col.OLR) + " W / m2.") | Integrating for 365 steps, 365.2422 days, or 1 years.
Total elapsed time is 0.9993368783782377 years.
Surface temperature is [287.84577808] K.
Net energy in to the column is [0.00165505] W / m2.
| BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Plot the radiative equilibrium temperature on the same plot with NCEP reanalysis | pcol = col.lev
fig = plt.figure( figsize=(10,8) )
ax = fig.add_subplot(111)
ax.plot( Tglobal + 273.15, np.log(level/1000), 'b-', col.Tatm, np.log( pcol/const.ps ), 'r-' )
ax.plot( col.Ts, 0, 'ro', markersize=20 )
ax.invert_yaxis()
ax.set_xlabel('Temperature (K)', fontsize=16)
ax.set_ylabel('Pressure (hPa)', fontsize=1... | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Now use convective adjustment to compute a Radiative-Convective Equilibrium temperature profile | dalr_col = climlab.RadiativeConvectiveModel(adj_lapse_rate='DALR')
print(dalr_col)
dalr_col.integrate_years(2.)
print("After " + str(dalr_col.time['days_elapsed']) + " days of integration:")
print("Surface temperature is " + str(dalr_col.Ts) + " K.")
print("Net energy in to the column is " + str(dalr_col.ASR - dalr_co... | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Now plot this "Radiative-Convective Equilibrium" on the same graph: | fig = plt.figure( figsize=(10,8) )
ax = fig.add_subplot(111)
ax.plot( Tglobal + 273.15, np.log(level/1000), 'b-', col.Tatm, np.log( pcol/const.ps ), 'r-' )
ax.plot( col.Ts, 0, 'ro', markersize=16 )
ax.plot( dalr_col.Tatm, np.log( pcol / const.ps ), 'k-' )
ax.plot( dalr_col.Ts, 0, 'ko', markersize=16 )
ax.invert_yaxis()... | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
The convective adjustment gets rid of the unphysical temperature difference between the surface and the overlying air.But now the surface is colder! Convection acts to move heat upward, away from the surface.Also, we note that the observed lapse rate (blue) is always shallower than $\Gamma_d$ (temperatures decrease mor... | rce_col = climlab.RadiativeConvectiveModel(adj_lapse_rate=6, abs_coeff=1.7E-4)
print(rce_col)
rce_col.integrate_years(2.)
print("After " + str(rce_col.time['days_elapsed']) + " days of integration:")
print("Surface temperature is " + str(rce_col.Ts) + " K.")
print("Net energy in to the column is " + str(rce_col.ASR - ... | Integrating for 730 steps, 730.4844 days, or 2.0 years.
Total elapsed time is 1.9986737567564754 years.
After 730.0 days of integration:
Surface temperature is [287.9049635] K.
Net energy in to the column is [2.14745046e-06] W / m2.
| BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Now add this new temperature profile to the graph: | fig = plt.figure( figsize=(10,8) )
ax = fig.add_subplot(111)
ax.plot( Tglobal + 273.15, np.log(level/1000), 'b-', col.Tatm, np.log( pcol/const.ps ), 'r-' )
ax.plot( col.Ts, 0, 'ro', markersize=16 )
ax.plot( dalr_col.Tatm, np.log( pcol / const.ps ), 'k-' )
ax.plot( dalr_col.Ts, 0, 'ko', markersize=16 )
ax.plot( rce_col.... | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Adding stratospheric ozone Our model has no equivalent of the stratosphere, where temperature increases with height. That's because our model has been completely transparent to shortwave radiation up until now.We can load some climatogical ozone data: | # Put in some ozone
import xarray as xr
ozonepath = "http://thredds.atmos.albany.edu:8080/thredds/dodsC/CLIMLAB/ozone/apeozone_cam3_5_54.nc"
ozone = xr.open_dataset(ozonepath)
ozone | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Take the global average of the ozone climatology, and plot it as a function of pressure (or height) | # Taking annual, zonal, and global averages of the ozone data
O3_zon = ozone.OZONE.mean(dim=("time","lon"))
weight_ozone = np.cos(np.deg2rad(ozone.lat)) / np.cos(np.deg2rad(ozone.lat)).mean(dim='lat')
O3_global = (O3_zon * weight_ozone).mean(dim='lat')
O3_global.shape
ax = plt.figure(figsize=(10,8)).add_subplot(111)
a... | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
This shows that most of the ozone is indeed in the stratosphere, and peaks near the top of the stratosphere.Now create a new column model object **on the same pressure levels as the ozone data**. We are also going set an adjusted lapse rate of 6 K / km, and tune the longwave absorption | oz_col = climlab.RadiativeConvectiveModel(lev = ozone.lev,
abs_coeff=1.82E-4,
adj_lapse_rate=6,
albedo=0.315) | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Now we will do something new: let the column absorb some shortwave radiation. We will assume that the shortwave absorptivity is proportional to the ozone concentration we plotted above. We need to weight the absorptivity by the pressure (mass) of each layer. | ozonefactor = 75
dp = oz_col.Tatm.domain.axes['lev'].delta
sw_abs = O3_global * dp * ozonefactor
oz_col.subprocess.SW.absorptivity = sw_abs
oz_col.compute()
oz_col.compute()
print(oz_col.SW_absorbed_atm) | [0.01521244 0.00239547 0.00294491 0.00359022 0.00437158 0.0053308
0.006518 0.00801322 0.00983974 0.0122112 0.01517438 0.01896033
0.02378877 0.03006126 0.03813404 0.04839947 0.06121356 0.07689825
0.0956929 0.11774895 0.14311224 0.17200325 0.20535155 0.24420577
0.28873387 0.33942617 0.39635885 0.45785715 0.515456... | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
Now run it out to Radiative-Convective Equilibrium, and plot | oz_col.integrate_years(2.)
print("After " + str(oz_col.time['days_elapsed']) + " days of integration:")
print("Surface temperature is " + str(oz_col.Ts) + " K.")
print("Net energy in to the column is " + str(oz_col.ASR - oz_col.OLR) + " W / m2.")
pozcol = oz_col.lev
fig = plt.figure( figsize=(10,8) )
ax = fig.add_sub... | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
And we finally have something that looks looks like the tropopause, with temperature increasing above at about the correct rate. Though the tropopause temperature is off by 15 degrees or so. Greenhouse warming in the RCE model with ozone | oz_col2 = climlab.process_like( oz_col )
oz_col2.subprocess['LW'].absorptivity *= 1.2
oz_col2.integrate_years(2.)
fig = plt.figure( figsize=(10,8) )
ax = fig.add_subplot(111)
ax.plot( Tglobal + const.tempCtoK, np.log(level/const.ps), 'b-' )
ax.plot( oz_col.Tatm, np.log( pozcol / const.ps ), 'c-' )
ax.plot( oz_col.Ts, ... | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
And we find that the troposphere warms, while the stratosphere cools! Vertical structure of greenhouse warming in CESM model | datapath = "http://thredds.atmos.albany.edu:8080/thredds/dodsC/CESMA/"
atmstr = ".cam.h0.clim.nc"
cesm_ctrl = xr.open_dataset(datapath + 'som_1850_f19/clim/som_1850_f19' + atmstr)
cesm_2xCO2 = xr.open_dataset(datapath + 'som_1850_2xCO2/clim/som_1850_2xCO2' + atmstr)
cesm_ctrl.T
T_cesm_ctrl_zon = cesm_ctrl.T.mean(dim=(... | _____no_output_____ | BSD-3-Clause | docs/source/courseware/Soundings_from_Observations_and_RCE_Models.ipynb | cjcardinale/climlab |
1.multiples of 3 and 5 | sum1=0
x=int(input())
for i in range(1,x):
if(i%3==0 or i%5==0):
sum1=sum1+i
print(sum1) | 1000
233168
| MIT | euler's.ipynb | boopathiviky/Eulers-Project |
2.even fib numbers | a=0
b=1
c=0
sum2=0
for i in range(0,10):
c=a+b
a=b
b=c
print(c)
if(c%2==0):
sum2=sum2+c
print('sum',sum2) | 1
2
3
5
8
13
21
34
55
89
sum 44
| MIT | euler's.ipynb | boopathiviky/Eulers-Project |
3.prime fact of | def isprime(n):
pr=1
for i in range(2,int(n/2)):
if(n%i==0):
pr=0
break
if(pr==1):
print(n)
x=int(input())
for i in range(2,x):
if(x%i==0):
isprime(i) | 15
3
5
| MIT | euler's.ipynb | boopathiviky/Eulers-Project |
4.Largest palindrome product | li=[]
def ispalindrome(c):
if(str(c) == str(c)[::-1]):
li.append(c)
#print(list)
li.sort()
for i in range(100,1000):
for k in range(i,1000):
a=i*k
ispalindrome(a)
print("Largest palindrome is:", li[-1]) | Largest palindrome is: 906609
| MIT | euler's.ipynb | boopathiviky/Eulers-Project |
5.Smallest multiple | n=1
while (n>=1):
n=n+1
c=1
for i in range(1,11):
if (n%i==0):
c=c+1
if (c>10):
print(n)
break
| 2520
| MIT | euler's.ipynb | boopathiviky/Eulers-Project |
6.Sum square difference | sq1=0
sq=0
# i insiate start,stop 10 1,9 1,11
for i in range(1,100+1):
sq=i**2
sq1=sq1+sq
sqr=0
for j in range(1,100+1):
sqr=sqr+j
s1=sqr**2
final=s1-sq1
print(sq1)
print(s1)
print(final)
# sq1=0
# for i in range(1,10+1):
# sq=i**2
# sq1=sq1+sq
# print(sq)
# print('\t',sq1) | _____no_output_____ | MIT | euler's.ipynb | boopathiviky/Eulers-Project |
7.10001st prime | def isprime(n):
pr=1
for i in range(2,int(n/2)):
if(n%i==0):
pr=0
break
if(pr==1):
return 1
c=0
for i in range(3,1000000):
if (isprime(i)==1):
c=c+1
if (c>=10001):
print (i)
break
| 104743
| MIT | euler's.ipynb | boopathiviky/Eulers-Project |
8.Largest product in a series | n='731671765313306249192251196744265747423553491949349698352031277450632623957831801698480186947885184385861560789112949495459501737958331952853208805511\
125406987471585238630507156932909632952274430435576689664895044524452316173185640309871112172238311362229893423380308135336276614282806444486645238749\
3035890729629... | 23514624000
| MIT | euler's.ipynb | boopathiviky/Eulers-Project |
9.Special Pythagorean triplet | for i in range(500):
for j in range(500):
for k in range(500):
if i+j+k == 1000:
if i<j<k:
if i**2+j**2==k**2:
print (i,j,k)
| 200 375 425
| MIT | euler's.ipynb | boopathiviky/Eulers-Project |
10.Summation of primes | num1=[]
pr=0
for num in range(0,11):
if num>=1:
for i in range(2,num):
if(num%i==0):
pr=0
break
else:
pr=1
if(pr==1):
num1.append(num)
print(sum(num1)) | _____no_output_____ | MIT | euler's.ipynb | boopathiviky/Eulers-Project |
11.Largest product in a grid | import numpy as np
lst1=[]
lst2=[]
lst3=[]
lst4=[]
dt=np.genfromtxt('data.txt', delimiter=' ')
rows, cols=dt.shape
print(dt[0])
su=1
for i in range(0,rows):
for j in range(0,cols):
# forward
if(j+4<=cols):
su=dt[i,j]*dt[i,j+1]*dt[i,j+2]*dt[i,j+3]
lst1.append(su)
su=1
... | _____no_output_____ | MIT | euler's.ipynb | boopathiviky/Eulers-Project |
`numpy`မင်္ဂလာပါ၊ welcome to the week 07 of Data Science Using Python. We will go into details of `numpy` this week (as well as do some linear algebra stuffs). `numpy` အကြောင်း သိပြီးသမျှ* `numpy` ဟာ array library ဖြစ်တယ်၊ * efficient ဖြစ်တယ်၊ * vector နဲ့ matrix တွေကို လွယ်လွယ်ကူကူ ကိုင်တွယ်နိုင်တယ်၊ * tens... | # tensor ဆိုတာ 2 ထက်များတဲ့ dimension ရှိတဲ့ array ကြီးတွေကို သင်္ချာဆန်ဆန် ခေါ်တာပါပဲ။
import numpy as np
tensor_3d = np.array(
[
[
[1, 2],
[3, 4]
],
[
[10, 20],
[30, 40]
],
]
)
print (tensor_3d.ndim, tensor_3d.shape, tensor_3... | _____no_output_____ | Apache-2.0 | week07/01_numpy.ipynb | neolaw84/data_science_using_python_labs |
* **array indexing** `numpy` array တွေကို index လုပ်ဖို့ square bracket '[]' ထဲမှာ comma ',' ခံပြီး ရေးရတယ်။ dimension အရေအတွက် ရှိတဲ့အတိုင်း အကုန်ရေးရတယ်။ ချန်ထားရင် ကျန်တဲ့ dimensional တွေအတွက် အကုန်လို့ ယူဆတယ်။ * **slicing/dicing** `numpy` array တွေရဲ့ view တခုကို dimension အတွက် index ဂဏန်း နေရာမှာ start:end:step s... | from utils import my_data, np_data
print (my_data)
print ('---')
print (np_data)
# so, how to get the id, length, width view and height view ?
| _____no_output_____ | Apache-2.0 | week07/01_numpy.ipynb | neolaw84/data_science_using_python_labs |
`numpy` array creation`numpy` မှာ array ဖန်တီးဖို့ short-cut function တွေရှိတယ်။ | import numpy as np
a = np.zeros((2,2)) # Create an array of all zeros
print(a) # Prints "[[ 0. 0.]
# [ 0. 0.]]"
b = np.ones((1,2)) # Create an array of all ones
print(b) # Prints "[[ 1. 1.]]"
c = np.full((2,2), 7) # Create a constant array
print(c) ... | [[0. 0.]
[0. 0.]]
[[1. 1.]]
[[7 7]
[7 7]]
[[1. 0.]
[0. 1.]]
[[0.12793776 0.2217626 ]
[0.03490039 0.66624149]]
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
24 25 26 27 28 29 30 31 32 33 34]
| Apache-2.0 | week07/01_numpy.ipynb | neolaw84/data_science_using_python_labs |
အသုံးများတာကတော့ ... * `arange`* `zeros`* `ones`* `full`* `eye` နဲ့* `random.random` တို့ ဖြစ်တယ်။အပြည့်အစုံကို [Numpy official documentation](https://numpy.org/doc/stable/reference/routines.array-creation.html) မှာ ကြည့်နိုင်တယ်။ `numpy` array manipulationအသုံးအများဆုံး array manipulation ကတော့ ... * `copy`* `reshape... | v1 = np_data[::2]
v2 = np_data[1::2,0]
print (v1.shape, v2.shape)
print (v1)
print (v2)
v2 =v2.reshape(-1,1) # here, -1 means "ကိုယ့်ဟာကိုယ် ကြည့်ဖြည့်လိုက်"
print (v2.shape)
np.hstack((v1, v2))
A = np.zeros((3,3)) # သတိထားစရာက tuple တွေကို parameter pass တာပဲ
print (A)
print ("---")
B = np.eye(2, 2)
print (B)
print ("... | _____no_output_____ | Apache-2.0 | week07/01_numpy.ipynb | neolaw84/data_science_using_python_labs |
နောက်ထပ် အသုံးများတဲ့ property တခုကတော့ `T` ပဲ။ transpose လုပ်တာ။ | print (D.T)
print (E.T) | _____no_output_____ | Apache-2.0 | week07/01_numpy.ipynb | neolaw84/data_science_using_python_labs |
vector/matrix calculations`dot` function ဟာ matrix/vector တွေကို လွယ်ကူစွာ မြှောက်နိုင်စေတယ်။ | x = np.array(
[
[1,2],
[3,4]
])
y = np.array(
[
[5,6],
[7,8]
])
v = np.array([9,10])
w = np.array([11, 12])
# Inner product of vectors; both produce 219
print(v.dot(w))
print(np.dot(v, w))
# Matrix / vector product; both produce the rank 1 array [29 67]
print(x.dot(v))... | 219
219
[29 67]
[29 67]
[[19 22]
[43 50]]
[[19 22]
[43 50]]
| Apache-2.0 | week07/01_numpy.ipynb | neolaw84/data_science_using_python_labs |
Special Array indexing`numpy` မှာ array တခုရဲ့ dimension တခုစီအတွက် list တခုနှုန်းနဲ့ (အဲဒီ list ထဲမှာ ကိုယ်လိုချင်တဲ့ element အရေအတွက်အတိုင်း ပါရ) index လုပ်တဲ့ special indexing method လဲ ရှိတယ်။ | r = np.arange(35).reshape(5, 7)
print (r)
index_x0 = [3, 4, 4]
index_x1 = [6, 2, 3]
print (r[index_x0, index_x1]) | [[ 0 1 2 3 4 5 6]
[ 7 8 9 10 11 12 13]
[14 15 16 17 18 19 20]
[21 22 23 24 25 26 27]
[28 29 30 31 32 33 34]]
[27 30 31]
| Apache-2.0 | week07/01_numpy.ipynb | neolaw84/data_science_using_python_labs |
`numpy` special boolean ability and boolean indexing | r_index = r % 2 == 0
print (r_index) | [[ True False True False True False True]
[False True False True False True False]
[ True False True False True False True]
[False True False True False True False]
[ True False True False True False True]]
| Apache-2.0 | week07/01_numpy.ipynb | neolaw84/data_science_using_python_labs |
အဲဒီလို boolean array (size တူရမယ်) ကို အသုံးပြုပြီး မူလ array ကနေ data retrieval လုပ်နိုင်တယ်။ | r_selected = r[r_index]
print (r_selected) | [ 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34]
| Apache-2.0 | week07/01_numpy.ipynb | neolaw84/data_science_using_python_labs |
Building your Deep Neural Network: Step by StepWelcome to your week 4 assignment (part 1 of 2)! Previously you trained a 2-layer Neural Network with a single hidden layer. This week, you will build a deep neural network with as many layers as you want!- In this notebook, you'll implement all the functions required to ... | import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases import *
from dnn_utils import sigmoid, sigmoid_backward, relu, relu_backward
from public_tests import *
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'neares... | _____no_output_____ | MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
2 - OutlineTo build your neural network, you'll be implementing several "helper functions." These helper functions will be used in the next assignment to build a two-layer neural network and an L-layer neural network. Each small helper function will have detailed instructions to walk you through the necessary steps. H... | # GRADED FUNCTION: initialize_parameters
def initialize_parameters(n_x, n_h, n_y):
"""
Argument:
n_x -- size of the input layer
n_h -- size of the hidden layer
n_y -- size of the output layer
Returns:
parameters -- python dictionary containing your parameters:
W1 --... | W1 = [[ 0.01624345 -0.00611756 -0.00528172]
[-0.01072969 0.00865408 -0.02301539]]
b1 = [[0.]
[0.]]
W2 = [[ 0.01744812 -0.00761207]]
b2 = [[0.]]
[92m All tests passed.
| MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
***Expected output***```W1 = [[ 0.01624345 -0.00611756 -0.00528172] [-0.01072969 0.00865408 -0.02301539]]b1 = [[0.] [0.]]W2 = [[ 0.01744812 -0.00761207]]b2 = [[0.]]``` 3.2 - L-layer Neural NetworkThe initialization for a deeper L-layer neural network is more complicated because there are many more weight matrices and... | # GRADED FUNCTION: initialize_parameters_deep
def initialize_parameters_deep(layer_dims):
"""
Arguments:
layer_dims -- python array (list) containing the dimensions of each layer in our network
Returns:
parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
... | W1 = [[ 0.01788628 0.0043651 0.00096497 -0.01863493 -0.00277388]
[-0.00354759 -0.00082741 -0.00627001 -0.00043818 -0.00477218]
[-0.01313865 0.00884622 0.00881318 0.01709573 0.00050034]
[-0.00404677 -0.0054536 -0.01546477 0.00982367 -0.01101068]]
b1 = [[0.]
[0.]
[0.]
[0.]]
W2 = [[-0.01185047 -0.0020565 ... | MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
***Expected output***```W1 = [[ 0.01788628 0.0043651 0.00096497 -0.01863493 -0.00277388] [-0.00354759 -0.00082741 -0.00627001 -0.00043818 -0.00477218] [-0.01313865 0.00884622 0.00881318 0.01709573 0.00050034] [-0.00404677 -0.0054536 -0.01546477 0.00982367 -0.01101068]]b1 = [[0.] [0.] [0.] [0.]]W2 = [[-0.011850... | # GRADED FUNCTION: linear_forward
def linear_forward(A, W, b):
"""
Implement the linear part of a layer's forward propagation.
Arguments:
A -- activations from previous layer (or input data): (size of previous layer, number of examples)
W -- weights matrix: numpy array of shape (size of current la... | Z = [[ 3.26295337 -1.23429987]]
[92m All tests passed.
| MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
***Expected output***```Z = [[ 3.26295337 -1.23429987]]``` 4.2 - Linear-Activation ForwardIn this notebook, you will use two activation functions:- **Sigmoid**: $\sigma(Z) = \sigma(W A + b) = \frac{1}{ 1 + e^{-(W A + b)}}$. You've been provided with the `sigmoid` function which returns **two** items: the activation va... | # GRADED FUNCTION: linear_activation_forward
def linear_activation_forward(A_prev, W, b, activation):
"""
Implement the forward propagation for the LINEAR->ACTIVATION layer
Arguments:
A_prev -- activations from previous layer (or input data): (size of previous layer, number of examples)
W -- weigh... | With sigmoid: A = [[0.96890023 0.11013289]]
With ReLU: A = [[3.43896131 0. ]]
[92m All tests passed.
| MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
***Expected output***```With sigmoid: A = [[0.96890023 0.11013289]]With ReLU: A = [[3.43896131 0. ]]``` **Note**: In deep learning, the "[LINEAR->ACTIVATION]" computation is counted as a single layer in the neural network, not two layers. 4.3 - L-Layer Model For even *more* convenience when implementing the $L... | # GRADED FUNCTION: L_model_forward
def L_model_forward(X, parameters):
"""
Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation
Arguments:
X -- data, numpy array of shape (input size, number of examples)
parameters -- output of initialize_parameters_deep()
... | AL = [[0.03921668 0.70498921 0.19734387 0.04728177]]
[92m All tests passed.
| MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
***Expected output***```AL = [[0.03921668 0.70498921 0.19734387 0.04728177]]``` **Awesome!** You've implemented a full forward propagation that takes the input X and outputs a row vector $A^{[L]}$ containing your predictions. It also records all intermediate values in "caches". Using $A^{[L]}$, you can compute the cost... | # GRADED FUNCTION: compute_cost
def compute_cost(AL, Y):
"""
Implement the cost function defined by equation (7).
Arguments:
AL -- probability vector corresponding to your label predictions, shape (1, number of examples)
Y -- true "label" vector (for example: containing 0 if non-cat, 1 if cat), sh... | Cost: 0.2797765635793422
[92m All tests passed.
| MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
**Expected Output**: cost 0.2797765635793422 6 - Backward Propagation ModuleJust as you did for the forward propagation, you'll implement helper functions for backpropagation. Remember that backpropagation is used to calculate the gradient of the loss function with respect to the parameters. **Rem... | A = np.array([[1, 2], [3, 4]])
print('axis=1 and keepdims=True')
print(np.sum(A, axis=1, keepdims=True))
print('axis=1 and keepdims=False')
print(np.sum(A, axis=1, keepdims=False))
print('axis=0 and keepdims=True')
print(np.sum(A, axis=0, keepdims=True))
print('axis=0 and keepdims=False')
print(np.sum(A, axis=0, keepd... | axis=1 and keepdims=True
[[3]
[7]]
axis=1 and keepdims=False
[3 7]
axis=0 and keepdims=True
[[4 6]]
axis=0 and keepdims=False
[4 6]
| MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
6.1 - Linear BackwardFor layer $l$, the linear part is: $Z^{[l]} = W^{[l]} A^{[l-1]} + b^{[l]}$ (followed by an activation).Suppose you have already calculated the derivative $dZ^{[l]} = \frac{\partial \mathcal{L} }{\partial Z^{[l]}}$. You want to get $(dW^{[l]}, db^{[l]}, dA^{[l-1]})$.Figure 4The three outputs $(dW^{... | # GRADED FUNCTION: linear_backward
def linear_backward(dZ, cache):
"""
Implement the linear portion of backward propagation for a single layer (layer l)
Arguments:
dZ -- Gradient of the cost with respect to the linear output (of current layer l)
cache -- tuple of values (A_prev, W, b) coming from ... | dA_prev: [[-1.15171336 0.06718465 -0.3204696 2.09812712]
[ 0.60345879 -3.72508701 5.81700741 -3.84326836]
[-0.4319552 -1.30987417 1.72354705 0.05070578]
[-0.38981415 0.60811244 -1.25938424 1.47191593]
[-2.52214926 2.67882552 -0.67947465 1.48119548]]
dW: [[ 0.07313866 -0.0976715 -0.87585828 0.73763362 ... | MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
**Expected Output**:```dA_prev: [[-1.15171336 0.06718465 -0.3204696 2.09812712] [ 0.60345879 -3.72508701 5.81700741 -3.84326836] [-0.4319552 -1.30987417 1.72354705 0.05070578] [-0.38981415 0.60811244 -1.25938424 1.47191593] [-2.52214926 2.67882552 -0.67947465 1.48119548]]dW: [[ 0.07313866 -0.0976715 -0.8758... | # GRADED FUNCTION: linear_activation_backward
def linear_activation_backward(dA, cache, activation):
"""
Implement the backward propagation for the LINEAR->ACTIVATION layer.
Arguments:
dA -- post-activation gradient for current layer l
cache -- tuple of values (linear_cache, activation_cache)... | With sigmoid: dA_prev = [[ 0.11017994 0.01105339]
[ 0.09466817 0.00949723]
[-0.05743092 -0.00576154]]
With sigmoid: dW = [[ 0.10266786 0.09778551 -0.01968084]]
With sigmoid: db = [[-0.05729622]]
With relu: dA_prev = [[ 0.44090989 0. ]
[ 0.37883606 0. ]
[-0.2298228 0. ]]
With relu: dW = [... | MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
**Expected output:**```With sigmoid: dA_prev = [[ 0.11017994 0.01105339] [ 0.09466817 0.00949723] [-0.05743092 -0.00576154]]With sigmoid: dW = [[ 0.10266786 0.09778551 -0.01968084]]With sigmoid: db = [[-0.05729622]]With relu: dA_prev = [[ 0.44090989 0. ] [ 0.37883606 0. ] [-0.2298228 0. ]]Wi... | # GRADED FUNCTION: L_model_backward
def L_model_backward(AL, Y, caches):
"""
Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group
Arguments:
AL -- probability vector, output of the forward propagation (L_model_forward())
Y -- true "label" vector (contain... | dA0 = [[ 0. 0.52257901]
[ 0. -0.3269206 ]
[ 0. -0.32070404]
[ 0. -0.74079187]]
dA1 = [[ 0.12913162 -0.44014127]
[-0.14175655 0.48317296]
[ 0.01663708 -0.05670698]]
dW1 = [[0.41010002 0.07807203 0.13798444 0.10502167]
[0. 0. 0. 0. ]
[0.05283652 0.01... | MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
**Expected output:**```dA0 = [[ 0. 0.52257901] [ 0. -0.3269206 ] [ 0. -0.32070404] [ 0. -0.74079187]]dA1 = [[ 0.12913162 -0.44014127] [-0.14175655 0.48317296] [ 0.01663708 -0.05670698]]dW1 = [[0.41010002 0.07807203 0.13798444 0.10502167] [0. 0. 0. 0. ] [0... | # GRADED FUNCTION: update_parameters
def update_parameters(params, grads, learning_rate):
"""
Update parameters using gradient descent
Arguments:
params -- python dictionary containing your parameters
grads -- python dictionary containing your gradients, output of L_model_backward
Re... | W1 = [[-0.59562069 -0.09991781 -2.14584584 1.82662008]
[-1.76569676 -0.80627147 0.51115557 -1.18258802]
[-1.0535704 -0.86128581 0.68284052 2.20374577]]
b1 = [[-0.04659241]
[-1.28888275]
[ 0.53405496]]
W2 = [[-0.55569196 0.0354055 1.32964895]]
b2 = [[-0.84610769]]
[92m All tests passed.
| MIT | Course_By_Andrew_Ng/Building_your_Deep_Neural_Network_Step_by_Step.ipynb | alivara/deep_learning |
**Load training data** | train_df = pd.read_csv("/kaggle/input/house-prices-advanced-regression-techniques/train.csv") | _____no_output_____ | MIT | notebook04cb9a8032.ipynb | brendanmanning/Kaggle-Housing-Price-Activity |
**Remove categorical features, handle missing data, cast improper types** | train_df['MSSubClass'] = train_df['MSSubClass'].apply(str)
train_df['MoSold'] = train_df['MoSold'].astype(str)
train_df['YrSold'] = train_df['YrSold'].astype(str)
cat_features = train_df.select_dtypes(include = 'object').columns
train_df.drop(cat_features, axis=1, inplace=True)
for c in train_df.columns:
train_df... | _____no_output_____ | MIT | notebook04cb9a8032.ipynb | brendanmanning/Kaggle-Housing-Price-Activity |
**Split the data into X(test/train) and Y(test/train) datasets** | y = train_df['SalePrice']
x = train_df.loc[:, train_df.columns != 'SalePrice']
X_dict = x.to_dict(orient='records')
dv_X = DictVectorizer(sparse=False)
X_encoded = dv_X.fit_transform(X_dict)
X_Train, X_Test, Y_Train, Y_Test = train_test_split(X_encoded, y.to_numpy(), test_size=0.33, random_state=42) | _____no_output_____ | MIT | notebook04cb9a8032.ipynb | brendanmanning/Kaggle-Housing-Price-Activity |
**Train a Linear Regression Model** | # Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X_Train, Y_Train) | _____no_output_____ | MIT | notebook04cb9a8032.ipynb | brendanmanning/Kaggle-Housing-Price-Activity |
**Predict from the testing set** | # Make predictions using the testing set
preds = regr.predict(X_Test) | _____no_output_____ | MIT | notebook04cb9a8032.ipynb | brendanmanning/Kaggle-Housing-Price-Activity |
**Score the model** | regr.score(X_Test, Y_Test) | _____no_output_____ | MIT | notebook04cb9a8032.ipynb | brendanmanning/Kaggle-Housing-Price-Activity |
Isolation Forest (IF) outlier detector deployment Wrap a scikit-learn Isolation Forest python model for use as a prediction microservice in seldon-core and deploy on seldon-core running on minikube or a Kubernetes cluster using GCP. Dependencies - [helm](https://github.com/helm/helm)- [minikube](https://github.com/ku... | # define columns to keep
cols=['duration','protocol_type','flag','src_bytes','dst_bytes','land',
'wrong_fragment','urgent','hot','num_failed_logins','logged_in',
'num_compromised','root_shell','su_attempted','num_root','num_file_creations',
'num_shells','num_access_files','num_outbound_cmds','is_host_... | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Test using Kubernetes cluster on GCP or Minikube Run the outlier detector as a model or a transformer. If you want to run the anomaly detector as a transformer, change the SERVICE_TYPE variable from MODEL to TRANSFORMER [here](./.s2i/environment), set MODEL = False and change ```OutlierIsolationForest.py``` to:```pyth... | MODEL = True | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Pick Kubernetes cluster on GCP or Minikube. | MINIKUBE = True
if MINIKUBE:
!minikube start --memory 4096
else:
!gcloud container clusters get-credentials standard-cluster-1 --zone europe-west1-b --project seldon-demos | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Create a cluster-wide cluster-admin role assigned to a service account named “default” in the namespace “kube-system”. | !kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin \
--serviceaccount=kube-system:default
!kubectl create namespace seldon | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Add current context details to the configuration file in the seldon namespace. | !kubectl config set-context $(kubectl config current-context) --namespace=seldon | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Create tiller service account and give it a cluster-wide cluster-admin role. | !kubectl -n kube-system create sa tiller
!kubectl create clusterrolebinding tiller --clusterrole cluster-admin --serviceaccount=kube-system:tiller
!helm init --service-account tiller | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Check deployment rollout status and deploy seldon/spartakus helm charts. | !kubectl rollout status deploy/tiller-deploy -n kube-system
!helm install ../../../helm-charts/seldon-core-operator --name seldon-core --set usage_metrics.enabled=true --namespace seldon-system | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Check deployment rollout status for seldon core. | !kubectl rollout status deploy/seldon-controller-manager -n seldon-system | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Install Ambassador API gateway | !helm install stable/ambassador --name ambassador --set crds.keep=false
!kubectl rollout status deployment.apps/ambassador | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
If Minikube used: create docker image for outlier detector inside Minikube using s2i. Besides the transformer image and the demo specific model image, the general model image for the Isolation Forest outlier detector is also available from Docker Hub as ***seldonio/outlier-if-model:0.1***. | if MINIKUBE & MODEL:
!eval $(minikube docker-env) && \
s2i build . seldonio/seldon-core-s2i-python3:0.4 seldonio/outlier-if-model-demo:0.1
elif MINIKUBE:
!eval $(minikube docker-env) && \
s2i build . seldonio/seldon-core-s2i-python3:0.4 seldonio/outlier-if-transformer:0.1 | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Install outlier detector helm charts either as a model or transformer and set *threshold* hyperparameter value. | if MODEL:
!helm install ../../../helm-charts/seldon-od-model \
--name outlier-detector \
--namespace=seldon \
--set model.type=isolationforest \
--set model.isolationforest.image.name=seldonio/outlier-if-model-demo:0.1 \
--set model.isolationforest.threshold=0 \
--set... | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Port forward AmbassadorRun command in terminal: ```kubectl port-forward $(kubectl get pods -n seldon -l app.kubernetes.io/name=ambassador -o jsonpath='{.items[0].metadata.name}') -n seldon 8003:8080``` Import rest requests, load data and test requests | from utils import get_payload, rest_request_ambassador, send_feedback_rest, get_kdd_data, generate_batch
data = get_kdd_data(keep_cols=cols,percent10=True) # load dataset
print(data.shape) | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Generate a random batch from the data | import numpy as np
samples = 1
fraction_outlier = 0.
X, labels = generate_batch(data,samples,fraction_outlier)
print(X.shape)
print(labels.shape) | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Test the rest requests with the generated data. It is important that the order of requests is respected. First we make predictions, then we get the "true" labels back using the feedback request. If we do not respect the order and eg keep making predictions without getting the feedback for each prediction, there will be... | request = get_payload(X)
response = rest_request_ambassador("outlier-detector","seldon",request,endpoint="localhost:8003") | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
If the outlier detector is used as a transformer, the output of the anomaly detection is added as part of the metadata. If it is used as a model, we send model feedback to retrieve custom performance metrics. | if MODEL:
send_feedback_rest("outlier-detector","seldon",request,response,0,labels,endpoint="localhost:8003") | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Analytics Install the helm charts for prometheus and the grafana dashboard | !helm install ../../../helm-charts/seldon-core-analytics --name seldon-core-analytics \
--set grafana_prom_admin_password=password \
--set persistence.enabled=false \
--namespace seldon | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Port forward Grafana dashboard Run command in terminal: ```kubectl port-forward $(kubectl get pods -n seldon -l app=grafana-prom-server -o jsonpath='{.items[0].metadata.name}') -n seldon 3000:3000``` You can then view an analytics dashboard inside the cluster at http://localhost:3000/dashboard/db/prediction-analytics?... | import time
n_requests = 100
samples = 1
for i in range(n_requests):
fraction_outlier = .1
X, labels = generate_batch(data,samples,fraction_outlier)
request = get_payload(X)
response = rest_request_ambassador("outlier-detector","seldon",request,endpoint="localhost:8003")
if MODEL:
send_feedb... | _____no_output_____ | Apache-2.0 | components/outlier-detection/isolation-forest/isolation_forest.ipynb | OrthoDex/seldon-core |
Implementing logistic regression from scratchThe goal of this notebook is to implement your own logistic regression classifier. We will: * Extract features from Amazon product reviews. * Convert an SFrame into a NumPy array. * Implement the link function for logistic regression. * Write a function to compute the deriv... | import turicreate
## utils for this notebooks moved into its own package
import sys
sys.path.append("../common")
from utils import time_it, assert_all, get_numpy_data, load_important_words | _____no_output_____ | BSD-3-Clause | C03/w02/C03w02_nb_pa1.ipynb | pascal-p/ML_UW_Spec |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.