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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Below I'm running images through the VGG network in batches.> **Exercise:** Below, build the VGG network. Also get the codes from the first fully connected layer (make sure you get the ReLUd values). | # 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:
# TODO: Build the vgg network here
vgg = vgg16.Vgg16()
input_ = tf.placeholder(tf.float32, [None,224,224,3])
with tf.name_scope("content... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning.ipynb | skagrawal/Deep-Learning-Udacity-ND |
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.ipynb | skagrawal/Deep-Learning-Udacity-ND |
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... | from sklearn.preprocessing import LabelBinarizer
lb = LabelBinarizer()
lb.fit(labels)
labels_vecs = lb.transform(labels) | _____no_output_____ | MIT | transfer-learning/Transfer_Learning.ipynb | skagrawal/Deep-Learning-Udacity-ND |
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)
X = codes
y = labels_vecs
for train_index, test_index in ss.split(X, y):
train_x, train_y = X[train_index], y[train_index]
test_x, test_y = X[test_index], y[test_index]
ss = StratifiedShuffleSpli... | 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.ipynb | skagrawal/Deep-Learning-Udacity-ND |
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]])
print(labels_vecs.shape)
# TODO: Classifier layers and operations
fc = tf.contrib.layers.fully_connected(inputs_, 256)
logits = tf.contrib.layers.fully_connected(fc, labels_vecs.sha... | (3670, 5)
| MIT | transfer-learning/Transfer_Learning.ipynb | skagrawal/Deep-Learning-Udacity-ND |
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.ipynb | skagrawal/Deep-Learning-Udacity-ND |
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. Use the `get_batches` function I wrote befo... | saver = tf.train.Saver()
epochs = 10
iteration = 0
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, opti... | Epoch: 1/10 Iteration: 0 Training loss: 6.09479
Epoch: 1/10 Iteration: 1 Training loss: 19.38938
Epoch: 1/10 Iteration: 2 Training loss: 14.50047
Epoch: 1/10 Iteration: 3 Training loss: 13.24159
Epoch: 1/10 Iteration: 4 Training loss: 7.22328
Epoch: 0/10 Iteration: 5 Validation Acc: 0.6866
Epoch: 1/10 Iteration: 5 Trai... | MIT | transfer-learning/Transfer_Learning.ipynb | skagrawal/Deep-Learning-Udacity-ND |
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.ipynb | skagrawal/Deep-Learning-Udacity-ND |
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
if 'vgg' in globals():
print('"vgg" object already exists. Will not create again.')
else:
#create vgg
with tf.Session() as sess:
... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning.ipynb | skagrawal/Deep-Learning-Udacity-ND |
Introduction to ProgrammingTopics for today will include:- Mozilla Developer Network [(MDN)](https://developer.mozilla.org/en-US/)- Python Documentation [(Official Documentation)](https://docs.python.org/3/)- Importance of Design- Functions- Built in Functions Mozilla Developer Network [(MDN)](https://developer.mozil... | def char_finder(character, string):
total = 0
for char in string:
if char == character:
total += 1
return total
if __name__ == "__main__":
output = char_finder('z', 'Quick brown fox jumped over the lazy dog')
print(output)
| 1
| MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
Functions---This is a intergral piece of how we do things in any programming language. This allows us to repeat instances of code that we've seen and use them at our preferance.We'll often be using functions similar to how we use variables and our data types. Making Our Own Functions---So to make a functions we'll be ... | def exampleName(exampleParameter1: any, exampleParameter2: any) -> any:
print(exampleParameter1, exampleParameter2)
exampleName("Hello", 5) | Hello 5
| MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
Using functions---Using functions is fairly simple. To use a function all we have to do is give the function name followed by parenthesis. This should seem familiar. Functions In Classes---Now we've mentioned classes before, classes can have functions but they're used a little differently. Functions that stem from cl... | class Person:
def __init__(self, weight: int, height: int, name: str):
self.weight = weight
self.height = height
self.name = name
def who_is_this(self):
print("This person's name is " + self.name)
print("This person's weight is " + str(self.weight) + " pounds")
p... | This person's name is Aaron Kippins
This person's weight is 225 pounds
This person's height is 70 inches
| MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
Built in Functions and Modules---With the talk of dot notation those are often used with built in functions. Built in function are functions that come along with the language. These tend to be very useful because as we start to visit more complex issues they allow us to do complexs thing with ease in some cases.We hav... | string = "I want to go home!"
print(string[0:12], "to Cancun!")
# print(string[0:1]) | I want to go to Cancun!
| MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
toUpper toLower--- | alpha_sentence = 'Quick brown fox jumped over the lazy dog'
print(alpha_sentence.title())
print(alpha_sentence.upper())
print(alpha_sentence.lower())
if alpha_sentence.lower().islower():
print("sentence is all lowercase")
| Quick Brown Fox Jumped Over The Lazy Dog
QUICK BROWN FOX JUMPED OVER THE LAZY DOG
quick brown fox jumped over the lazy dog
sentence is all lowercase
| MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
Exponents--- | print(2 ** 3) | 8
| MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
math.sqrt()--- | import math
math.sqrt(4) | _____no_output_____ | MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
Integer Division vs Float Division--- | print(4//2)
print(4/2) | 2
2.0
| MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
Abs()--- | abs(-10) | _____no_output_____ | MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
String Manipulation--- | dummy_string = "Hey there I'm just a string for the example about to happen."
print(dummy_string.center(70, "-"))
print(dummy_string.partition(" "))
print(dummy_string.swapcase())
print(dummy_string.split(" ")) | -----Hey there I'm just a string for the example about to happen.-----
('Hey', ' ', "there I'm just a string for the example about to happen.")
hEY THERE i'M JUST A STRING FOR THE EXAMPLE ABOUT TO HAPPEN.
['Hey', 'there', "I'm", 'just', 'a&... | MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
Array Manipulation--- | arr = [2, 5, 6, 1, 4, 3]
arr.sort()
print(arr)
print(arr[3])
# sorted(arr)
print(arr[1:3])
| [1, 2, 3, 4, 5, 6]
4
| MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
Insert and Pop, Append and Remove--- | arr.append(7)
print(arr)
arr.pop()
print(arr) | [1, 2, 3, 4, 5, 6, 7, 7]
[1, 2, 3, 4, 5, 6, 7]
| MIT | JupyterNotebooks/Lessons/Lesson 4.ipynb | emilekhoury/CMPT-120L-910-20F |
Add MollWeide Plotting to gwylm class(L. London 2017) Related: positive_dev/examples/plotting_spherical_harmonics.ipynb Setup Environment | # Setup ipython environment
%load_ext autoreload
%autoreload 2
%matplotlib inline
# Import usefuls
from nrutils import scsearch,gwylm
from matplotlib.pyplot import *
from numpy import array | The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
| MIT | issues/closed/issue2_add_mollweide_plotting_to_gwylm.ipynb | llondon6/nrutils_dev |
Find a simulation and load data | # Find sim
A = scsearch(q=[10,20],keyword='hr',verbose=True,institute='gt')
# Load data
y = gwylm(A[0],lmax=4,verbose=False,clean=True) | [1m([0;33mvalidate![0m)>> [0mMultiple catalog directories found. We will scan through the related list, and then store first the catalog_dir that the OS can find.
[1m([0;33mvalidate![0m)>> [0mSelecting "[0;36m/Volumes/athena/bradwr/[0m"
| MIT | issues/closed/issue2_add_mollweide_plotting_to_gwylm.ipynb | llondon6/nrutils_dev |
Plot Mollweide |
#
kind = 'strain'
# Make mollweide plot -- NOTE that the time input is relative to the peak in h22
ax0,real_time = y.mollweide_plot(time=0,form='abs',kind=kind)
ax0.set_title('$l_{max} = %i$'%max([l for l,m in y.lm]),size=20)
# Make time domain plot for reference
axarr,fig = y.lm[2,2][kind].plot()
for ax in axarr:
... | _____no_output_____ | MIT | issues/closed/issue2_add_mollweide_plotting_to_gwylm.ipynb | llondon6/nrutils_dev |
Try to put everything in the same figure |
#
R,C = 6,3
#
fig = figure( figsize=3*array([C,1.0*R]) )
#
ax4 = subplot2grid( (R,C), (0, 0), colspan=C, rowspan=3, projection='mollweide' )
ax1 = subplot2grid( (R,C), (3, 0), colspan=C)
ax2 = subplot2grid( (R,C), (4, 0), colspan=C, sharex=ax1)
ax3 = subplot2grid( (R,C), (5, 0), colspan=C, sharex=ax1)
#
kind = 'st... | _____no_output_____ | MIT | issues/closed/issue2_add_mollweide_plotting_to_gwylm.ipynb | llondon6/nrutils_dev |
Now perhaps write an external script that animates frames for select time samples? | from os.path import join
range(0,100,10) | _____no_output_____ | MIT | issues/closed/issue2_add_mollweide_plotting_to_gwylm.ipynb | llondon6/nrutils_dev |
Variables | x = 2
y = '3'
print(x+int(y))
z = [1, 2, 3] #List
w = (2, 3, 4) #Tuple
import numpy as np
q = np.array([1, 2, 3]) #numpy.ndarray
type(q) | 5
| MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Console input and output | MyName = input('My name is: ')
print('Hello, '+MyName) | My name is: david
Hello, david
| MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
File input and output | fid = open('msg.txt','w')
fid.write('demo of writing.\n')
fid.write('Second line')
fid.close()
fid = open('msg.txt','r')
msg = fid.readline()
print(msg)
msg = fid.readline()
print(msg)
fid.close()
fid = open('msg.txt','r')
msg = fid.readlines()
print(msg)
fid = open('msg.txt','r')
msg = fid.read()
print(msg)
import n... | _____no_output_____ | MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Functions, Conditions, Loop | import numpy as np
def f(x):
return x**2
x = np.linspace(0,5,10)
y = f(x)
print(y)
import numpy as np
def f(x): #這是個奇怪的練習用函數
res = x
if res < 3:
res = np.nan #<3就傳 Not a Number
elif res < 15:
res = x**3
else:
res = x**4
return res
x = np.linspace(0,10,20)
y = np.emp... | [ nan nan nan nan nan
nan 31.49147106 50.00728969 74.64644992 106.28371483
145.7938475 194.05161102 251.93176848 320.30908296 400.05831754
492.05423531 597.17159936 716.28517277 850.26971862 1000. ]
| MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Matrices, linear equations | A = np.array([[1,2],[3,2]])
B = np.array([1,0])
# x = A^-1 * b
sol1 = np.dot(np.linalg.inv(A),B)
print(sol1)
sol2 = np.linalg.solve(A,B)
print(sol2)
import sympy as sym
sym.init_printing()
#This will automatically enable the best printer available in your environment.
x,y = sym.symbols('x y')
z = sym.linsolve([3*x... | [-0.5 0.75]
[-0.5 0.75]
| MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Non-linear equation | from scipy.optimize import fsolve
def f(z): #用z參數來表示x和y，做函數運算
x = z[0]
y = z[1]
return [x+2*y, x**2+y**2-1]
z0 = [0,1]
z = fsolve(f,z0)
print(z)
print(f(z)) | [-0.89442719 0.4472136 ]
[0.0, -1.1102230246251565e-16]
| MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Integration | from scipy.integrate import quad
def f(x):
return x**2
quad(f,0,2) #計算積分值
import sympy as sym
sym.init_printing()
x = sym.Symbol('x')
f = sym.integrate(x**2,x)
f.subs(x,2) #將值帶入函數中
f | _____no_output_____ | MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Derivative | from scipy.misc import derivative
def f(x):
return x**2
print(derivative(f,2,dx=0.01)) #dx表示精確程度
import sympy as sym
sym.init_printing()
x = sym.Symbol('x')
f = sym.diff(x**3,x)
f.subs(x,2) #將值帶入函數中，得解
f | 4.0
| MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Interpolation | from scipy.interpolate import interp1d #中間的字是1不是L喔!!!
x = np.arange(0,6,1)
y = np.array([0.2,0.3,0.5,1.0,0.9,1.1])
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(x,y,'bo')
xp = np.linspace(0,5,100) #為了顯示差別把點增加
y1 = interp1d(x,y,kind='linear') #一階
plt.plot(xp,y1(xp),'r-')
y2 = interp1d(x,y,kind='quadr... | _____no_output_____ | MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Linear regression | import numpy as np
x = np.array([0,1,2,3,4,5])
y = np.array([0.1,0.2,0.3,0.5,0.8,2.0 ])
#多項式逼近法，選擇階層
p1 = np.polyfit(x,y,1)
print(p1)
p2 = np.polyfit(x,y,2)
print(p2)
p3 = np.polyfit(x,y,3)
print(p3)
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(x,y,'ro')
# np.polyval表示多項式的值，把係數p_帶入多項式x求出來的值
xp = np.li... | [ 0.32857143 -0.17142857]
[ 0.1125 -0.23392857 0.20357143]
[ 0.04166667 -0.2 0.33690476 0.07857143]
| MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Nonlinear regression | import numpy as np
from scipy.optimize import curve_fit
x = np.array([0,1,2,3,4,5])
y = np.array([0.1,0.2,0.3,0.5,0.8,2.0 ])
#多項式逼近法，選擇階層
p1 = np.polyfit(x,y,1)
print(p1)
p2 = np.polyfit(x,y,2)
print(p2)
p3 = np.polyfit(x,y,3)
print(p3)
#使用指數對數
def f(x,a):
return 0.1 * np.exp(a*x)
a = curve_fit(f,x,y)[0] #非線性回歸，... | [ 0.32857143 -0.17142857]
[ 0.1125 -0.23392857 0.20357143]
[ 0.04166667 -0.2 0.33690476 0.07857143]
a=[ 0.58628748]
| MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Differential equation | from scipy.integrate import odeint
def dydt(y,t,a):
return -a * y
a = 0.5
t = np.linspace(0,20)
y0 = 5.0
y = odeint(dydt,y0,t,args=(a,))
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(t,y)
plt.xlabel('time')
plt.ylabel('y') | _____no_output_____ | MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
Nonlinear optimization | #概念:要有Objective、Constraint，然後初始猜想值
import numpy as np
from scipy.optimize import minimize
def objective(x): #此函數求最小值
x1 = x[0]
x2 = x[1]
x3 = x[2]
x4 = x[3]
return x1*x4*(x1+x2+x3)+x3
#用減法做比較
def constraint1(x):
return x[0]*x[1]*x[2]*x[3] - 25.0
#用減法做比較
def constraint2(x):
sum_sq = 40.0
... | [ 1. 4.7429961 3.82115462 1.37940765]
| MIT | Numeric and scientific python.ipynb | Pytoddler/Data-analysis-and-visualization |
PyFunc Model + Transformer ExampleThis notebook demonstrates how to deploy a Python function based model and a custom transformer. This type of model is useful as user would be able to define their own logic inside the model as long as it satisfy contract given in `merlin.PyFuncModel`. If the pre/post-processing steps... | !pip install --upgrade -r requirements.txt > /dev/null
import warnings
warnings.filterwarnings('ignore') | _____no_output_____ | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
1. Initialize Merlin 1.1 Set Merlin Server | import merlin
MERLIN_URL = "<MERLIN_HOST>/api/merlin"
merlin.set_url(MERLIN_URL) | _____no_output_____ | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
1.2 Set Active Project`project` represent a project in real life. You may have multiple model within a project.`merlin.set_project()` will set the active project into the name matched by argument. You can only set it to an existing project. If you would like to create a new project, please do so from the MLP UI. | PROJECT_NAME = "sample"
merlin.set_project(PROJECT_NAME) | /Users/ariefrahmansyah/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing... | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
1.3 Set Active Model`model` represents an abstract ML model. Conceptually, `model` in Merlin is similar to a class in programming language. To instantiate a `model` you'll have to create a `model_version`.Each `model` has a type, currently model type supported by Merlin are: sklearn, xgboost, tensorflow, pytorch, and ... | from merlin.model import ModelType
MODEL_NAME = "transformer-pyfunc"
merlin.set_model(MODEL_NAME, ModelType.PYFUNC) | _____no_output_____ | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
2. Train ModelIn this step, we are going to train a cifar10 model using PyToch and create PyFunc model class that does the prediction using trained PyTorch model. 2.1 Prepare Training Data | import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True,... | /Users/ariefrahmansyah/.pyenv/versions/3.7.3/lib/python3.7/site-packages/torchvision/datasets/lsun.py:8: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
from collections import Iterable
0it [00:00, ?it/s] | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
2.2 Create PyTorch Model | import torch.nn as nn
import torch.nn.functional as F
class PyTorchModel(nn.Module):
def __init__(self):
super(PyTorchModel, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)... | /Users/ariefrahmansyah/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing... | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
2.3 Train Model | import torch.optim as optim
net = PyTorchModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a li... | 170500096it [03:10, 1240089.84it/s] | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
2.4 Check Prediction | dataiter = iter(trainloader)
inputs, labels = dataiter.next()
predict_out = net(inputs[0:1])
predict_out | _____no_output_____ | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
2.5 Serialize Model | import os
model_dir = "pytorch-model"
model_path = os.path.join(model_dir, "model.pt")
model_class_path = os.path.join(model_dir, "model.py")
torch.save(net.state_dict(), model_path) | _____no_output_____ | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
2.6 Save PyTorchModel ClassWe also need to save the PyTorchModel class and upload it to Merlin alongside the serialized trained model. The next cell will write the PyTorchModel we defined above to `pytorch-model/model.py` file. | %%file pytorch-model/model.py
import torch.nn as nn
import torch.nn.functional as F
class PyTorchModel(nn.Module):
def __init__(self):
super(PyTorchModel, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc... | Overwriting pytorch-model/model.py
| Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
3. Create PyFunc ModelTo create a PyFunc model you'll have to extend `merlin.PyFuncModel` class and implement its `initialize` and `infer` method.`initialize` will be called once during model initialization. The argument to `initialize` is a dictionary containing a key value pair of artifact name and its URL. The arti... | import importlib
import sys
from merlin.model import PyFuncModel
MODEL_CLASS_NAME="PyTorchModel"
class CifarModel(PyFuncModel):
def initialize(self, artifacts):
model_path = artifacts["model_path"]
model_class_path = artifacts["model_class_path"]
# Load the python class into memo... | _____no_output_____ | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
Now, let's test it locally. | import json
with open(os.path.join("input-tensor.json"), "r") as f:
tensor_req = json.load(f)
m = CifarModel()
m.initialize({"model_path": model_path, "model_class_path": model_class_path})
m.infer(tensor_req) | _____no_output_____ | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
4. Deploy Model To deploy the model, we will have to create an iteration of the model (by create a `model_version`), upload the serialized model to MLP, and then deploy. 4.1 Create Model Version and Upload `merlin.new_model_version()` is a convenient method to create a model version and start its development process.... | with merlin.new_model_version() as v:
merlin.log_pyfunc_model(model_instance=CifarModel(),
conda_env="env.yaml",
artifacts={"model_path": model_path, "model_class_path": model_class_path}) | 2021/06/23 05:41:28 WARNING mlflow.models.model: Logging model metadata to the tracking server has failed, possibly due older server version. The model artifacts have been logged successfully under gs://<MERLIN_BUCKET>/mlflow/604/7b57180c051842fe815adbacfa282541/artifacts. In addition to exporting model artifacts, MLfl... | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
4.2 Deploy Model and TransformerTo deploy a model and its transformer, you must pass a `transformer` object to `deploy()` function. Each of deployed model version will have its own generated url. | from merlin.resource_request import ResourceRequest
from merlin.transformer import Transformer
# Create a transformer object and its resources requests
resource_request = ResourceRequest(min_replica=1, max_replica=1,
cpu_request="100m", memory_request="200Mi")
transformer = Transfor... | /Users/ariefrahmansyah/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing... | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
4.3 Send Test Request | import json
import requests
with open(os.path.join("input-raw-image.json"), "r") as f:
req = json.load(f)
resp = requests.post(endpoint.url, json=req)
resp.text | _____no_output_____ | Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
4. Clean Up 4.1 Delete Deployment | merlin.undeploy(v) | Deleting deployment of model transformer-pyfunc version 2 from enviroment id-staging
| Apache-2.0 | examples/transformer/custom-transformer/PyFunc-Transformer.ipynb | Omrisnyk/merlin |
CCI501 - Machine Learning Project Name: Samuel Mwamburi Mghendi Admission Number: P52/37621/2020 Email: mghendi@students.uonbi.ac.ke Course: Machine Learning – CCI 501 Applying Logistic Regression to Establish a Good Pricing Model for Mobile Phone Manufacturers in the Current Market Landscape using Technical Specif... | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from tqdm import trange | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
Import Data | df = pd.read_csv("productdata.csv")
df
from sklearn import preprocessing | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
Exploratory Data AnalysisGathering more information about the dataset in order to better understand it.The relationship and distribution between screen size, screen resolution, camera resolution, storage space, memory, rating and likes against the resultant price charged for each phone sold was plotted and analyzed. | df.describe()
df.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 1148 entries, 0 to 1147
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Phone 1148 non-null object
1 Screen (inches) 1148 non-null float64
2 Resoluti... | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
The feature OS has missing values. | # check shape
df.shape | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
The dataset has 1,148 records and 12 features. | # remove duplicates, if any
df.drop_duplicates(inplace = True)
df.shape | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
No duplicate records available in the dataset. Mobile Phones by Screen Size Contrasted by User Rating | # previewing distribution of screen size by rating
df['Round Rating'] = df['Rating'].round(decimals=0)
plt.figure(figsize = (20, 6))
ax = sns.histplot(df, x="Screen (inches)", stat="count", hue="Round Rating", multiple="dodge", shrink=0.8)
for p in ax.patches:# histogram bar label
h = p.get_height()
if (h != 0): a... | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
The chart can be used to deduce a high-level inference on the phone industry consumer purchase preference. Phones with a larger screen size, which are inherently larger in size, between 5 to 7 inches are seen to be rated higher. | # changing the datatype of the 'OS' variable
df['OS'] = df['OS'].astype('str') | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
Mobile Phones by Camera Resolution contrasted by User Rating | # previewing distribution of camera resolution by rating
plt.figure(figsize = (20, 6))
ax = sns.histplot(df, x="Camera (MP)", hue="Round Rating", multiple="dodge", shrink=0.8)
for p in ax.patches:# label each bar in histogram
h = p.get_height()
if (h != 0): ax.text(x = p.get_x()+(p.get_width()/2), y = h+1, s = "{:.... | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
Mobile phones with cameras sporting high resolutions,15 and 32 Megapixels , based on the current offering in the market have significantly better relative ratings than mid-tier models between 20 to 30 Megapixels and low-tier models less than 5 megapixels. | # previewing distribution of Storage Capacity by rating
plt.figure(figsize = (20, 6))
ax = sns.histplot(df, x="Storage (GB)", hue="Round Rating", multiple="dodge", shrink=0.8)
for p in ax.patches:# label each bar in histogram
h = p.get_height()
if (h != 0): ax.text(x = p.get_x()+(p.get_width()/2), y = h+1, s = "{:.... | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
As anticipated, mobile phones with higher internal storage capacities, greater than or equal to 256 Gigabytes, recieve significantly better relative ratings than models with less than 128 gigabytes. Additionally, there are very few purchases of mobile phones equal to or greater than 512 gigabytes of storage. Mobile Ph... | #pairplot to investigate the relationship between all the variables
sns.pairplot(df)
plt.show() | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
In reference to the pair plot above, mid-tier phone models are significantly better rated and well recieved as compared to their much more expensive and budget counterparts in the local current market.Phones with mid-tier features such as an average storage capacity, such as a large display 5 to 7 inches, storage of be... | # creating categorigal variables for the battery type feature
df["Battery Type"].replace({"Li-Po": "0", "Li-Ion": "1"}, inplace=True)
print(df)
# creating categorigal variables for the battery type feature
df["Price Category"].replace({"Budget": "0", "Mid-Tier": "1", "Flagship": "2"}, inplace=True)
print(df)
df["Price ... | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
Creating Bag of Words models | df["OS"] = vectorizer.transform(df["OS"]).toarray()
print(df)
df["Resolution (pixels)"] = vectorizer.transform(df["Resolution (pixels)"]).toarray()
print (df) | Phone Screen (inches) Resolution (pixels) \
0 Gionee M7 Power 6.00 0
1 Gionee M7 6.01 0
2 Samsung Galaxy M21 6GB/128GB 6.40 0
3 Samsung G... | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
4. Data Modelling Data Modelling for Logistic Regression Feature Selection For this experiment, the mobile phone's technical specifications will be used as the independent variables. The ratings and likes which are subjective assessments will be dropped.Variables such as the Phone Name are not important in price po... | X = df.drop(columns = ['Phone','Price(Kshs)', 'Rating', 'Likes', 'OS', 'Battery Type', 'Resolution (pixels)', 'Round Rating']).values
y = df['Price Category'].values | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
Splitting Data | # splitting into 75% training and 25% test sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1000) | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
Feature Scaling | scaler = preprocessing.StandardScaler().fit(X_train)
scaler
scaler.mean_
scaler.scale_
X_scaled = scaler.transform(X_train)
X_scaled
X_scaled.mean(axis=0)
X_scaled.std(axis=0) | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
Logistic Regression | from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
X, y = make_classifica... | _____no_output_____ | MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
5. Performance Evaluation | print("Accuracy:", (score)*100, "%") | Accuracy: 100.0 %
| MIT | CCI_501_ML_Project.ipynb | mghendi/smartphonepriceclassifier |
PROYECTO CIFAR-10 CARLOS CABAÑÓ 1. Librerias Descargamos la librería para los arrays en preprocesamiento de Keras
| from tensorflow import keras as ks
from matplotlib import pyplot as plt
import numpy as np
import time
import datetime
import random
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.regularizers import l2
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.imag... | _____no_output_____ | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
2. Arquitectura de red del modelo Adoptamos la arquitectura del modelo 11 con los ajustes en Batch Normalization, Kernel Regularizer y Kernel Initializer. Añadimos Batch normalization a las capas de convolución. | model = ks.Sequential()
model.add(ks.layers.Conv2D(64, (3, 3), strides=1, activation='relu', kernel_regularizer=l2(0.0005), kernel_initializer="he_uniform", padding='same', input_shape=(32,32,3)))
model.add(ks.layers.BatchNormalization())
model.add(ks.layers.Conv2D(64, (3, 3), strides=1, activation='relu', kernel_regu... | Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 32, 32, 64) 1792
____________________________________... | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
3. Optimizador, función error Añadimos el learning rate al optimizador | from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.001, momentum=0.9),
loss='sparse_categorical_crossentropy',
metrics=['accuracy']) | _____no_output_____ | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
4. Preparamos los datos | cifar10 = ks.datasets.cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
cifar10_labels = [
'airplane', # id 0
'automobile',
'bird',
'cat',
'deer',
'dog',
'frog',
'horse',
'ship',
'truck',
]
print('Number of labels: %s' % len(cifar10_labels)) | Number of labels: 10
| MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
Pintemos una muestra de las imagenes del dataset CIFAR10: | # Pintemos una muestra de las las imagenes del dataset MNIST
print('Train: X=%s, y=%s' % (x_train.shape, y_train.shape))
print('Test: X=%s, y=%s' % (x_test.shape, y_test.shape))
for i in range(9):
plt.subplot(330 + 1 + i)
plt.imshow(x_train[i], cmap=plt.get_cmap('gray'))
plt.title(cifar10_labels[y_train[... | Train: X=(50000, 32, 32, 3), y=(50000, 1)
Test: X=(10000, 32, 32, 3), y=(10000, 1)
| MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
Hacemos la validación al mismo tiempo que el entrenamiento: | x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]
| _____no_output_____ | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
Hacemos el OHE para la clasificación | le = LabelEncoder()
le.fit(y_train.ravel())
y_train_encoded = le.transform(y_train.ravel())
y_val_encoded = le.transform(y_val.ravel())
y_test_encoded = le.transform(y_test.ravel()) | _____no_output_____ | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
5. Ajustes: Early Stopping Definimos un early stopping con base en el loss de validación y con el parámetro de "patience" a 10, para tener algo de margen. Con el Early Stopping lograremos parar el entrenamiento en el momento óptimo para evitar que siga entrenando a partir del overfitting. | callback_val_loss = EarlyStopping(monitor="val_loss", patience=5)
callback_val_accuracy = EarlyStopping(monitor="val_accuracy", patience=10) | _____no_output_____ | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
6. Transformador de imágenes 6.1 Imágenes de entrenamiento | train_datagen = ImageDataGenerator(
horizontal_flip=True,
width_shift_range=0.2,
height_shift_range=0.2,
)
train_generator = train_datagen.flow(
x_train,
y_train_encoded,
batch_size=64
) | _____no_output_____ | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
6.2 Imágenes de validación y testeo | validation_datagen = ImageDataGenerator(
horizontal_flip=True,
width_shift_range=0.2,
height_shift_range=0.2,
)
validation_generator = validation_datagen.flow(
x_val,
y_val_encoded,
batch_size=64
)
test_datagen = ImageDataGenerator(
horizontal_flip=True,
width_shift... | _____no_output_____ | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
6.3 Generador de datos | sample = random.choice(range(0,1457))
image = x_train[sample]
plt.imshow(image, cmap=plt.cm.binary)
sample = random.choice(range(0,1457))
example_generator = train_datagen.flow(
x_train[sample:sample+1],
y_train_encoded[sample:sample+1],
batch_size=64
)
plt.figure(figsize=(12, 12))
for i in ra... | _____no_output_____ | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
7. Entrenamiento | t = time.perf_counter()
steps=int(x_train.shape[0]/64)
history = model.fit(train_generator, epochs=100, use_multiprocessing=False, batch_size= 64, validation_data=validation_generator, steps_per_epoch=steps, callbacks=[callback_val_loss, callback_val_accuracy])
elapsed_time = datetime.timedelta(seconds=(time.perf_coun... | Tiempo de entrenamiento: 0:52:12.653343
| MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
8. Evaluamos los resultados | _, acc = model.evaluate(x_test, y_test_encoded, verbose=0)
print('> %.3f' % (acc * 100.0))
plt.title('Cross Entropy Loss')
plt.plot(history.history['loss'], color='blue', label='train')
plt.plot(history.history['val_loss'], color='orange', label='test')
plt.show()
plt.title('Classification Accuracy')
plt.plot(history.... | _____no_output_____ | MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
Dibujamos las primeras imágenes: | i = 0
for l in cifar10_labels:
print(i, l)
i += 1
num_rows = 5
num_cols = 4
start = 650
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i+start, predictions[i+start], y_test, x_test)
plt.subplot(... | 0 airplane
1 automobile
2 bird
3 cat
4 deer
5 dog
6 frog
7 horse
8 ship
9 truck
| MIT | Project Portfolio/cnn-cifar10-tf2-v12_Notebook_CarlosCabano.ipynb | CarlosCabano/carloscabano.github.io |
Summary About the DatasetThe data files train.csv and test.csv contain gray-scale images of hand-drawn digits, from zero through nine.Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkne... | import numpy as np
import pandas as pd
import tensorflow as tf
import keras.preprocessing.image
import sklearn.preprocessing
import sklearn.model_selection
import sklearn.metrics
import sklearn.linear_model
import sklearn.naive_bayes
import sklearn.tree
import sklearn.ensemble
import os;
import datetime
import cv2
i... | Platform deatils Windows-10-10.0.15063-SP0
Python version 3.6.2
| BSD-3-Clause | MNIST-image-classification-using-TF.ipynb | jpnevrones/Digit-Recognizer |
Additional info: I am going to use the Kaggle csv based data set but MNIST Data set can also be downloaded and extracted using the below functions. Function to downlaod and Extract MNIST Dataset | url = 'http://commondatastorage.googleapis.com/books1000/'
last_percent_reported = None
def download_progress_hook(count, blockSize, totalSize):
"""A hook to report the progress of a download. This is mostly intended for users with
slow internet connections. Reports every 1% change in download progress.
"""
gl... | train.csv loaded: data_df(42000,785)
test.csv loaded: test_df(28000, 784)
x_test.shape = (28000, 28, 28, 1)
| BSD-3-Clause | MNIST-image-classification-using-TF.ipynb | jpnevrones/Digit-Recognizer |
Preprocessing Normalize data and split into training and validation sets- In order to scale feature that robust to outlier you can use sklearn.preprocessing.RobustScaler() - rtoo = sklearn.preprocessing.RobustScaler() - rtoo.fit(data) - data = rtoo.transform(data) - or you can do standraization by... | # function to normalize data
def normalize_data(data):
data = data / data.max() # convert from [0:255] to [0.:1.]
return data
# class labels to one-hot vectors e.g. 1 => [0 1 0 0 0 0 0 0 0 0]
def dense_to_one_hot(labels_dense, num_classes):
num_labels = labels_dense.shape[0]
index_offset = np.aran... | x_train_valid.shape = (42000, 28, 28, 1)
y_train_valid_labels.shape = (42000,)
image_size = 784
image_width = 28
image_height = 28
labels_count = 10
| BSD-3-Clause | MNIST-image-classification-using-TF.ipynb | jpnevrones/Digit-Recognizer |
Data augmenttaionlets stick to basics like rotations, translations, zoom using keras | def generate_images(imgs):
# rotations, translations, zoom
image_generator = keras.preprocessing.image.ImageDataGenerator(
rotation_range = 10, width_shift_range = 0.1 , height_shift_range = 0.1,
zoom_range = 0.1)
# get transformed images
imgs = image_generator.flow(imgs.copy(), np.... | _____no_output_____ | BSD-3-Clause | MNIST-image-classification-using-TF.ipynb | jpnevrones/Digit-Recognizer |
Benchmarking on some basic ML modelsAs we have our training data ready lets run couple of basic machine elarning model, I would consider these models to kind of create a baseline which would help me later own to generlize the performance of my model. In simple word these would give me datapoints to compare the perfor... | logistic_regression = sklearn.linear_model.LogisticRegression(verbose=0, solver='lbfgs',multi_class='multinomial')
extra_trees = sklearn.ensemble.ExtraTreesClassifier(verbose=0)
random_forest = sklearn.ensemble.RandomForestClassifier(verbose=0)
bench_markingDict = {'logistic_regression': logistic_regression,
... | 1 : logistic_regression train/valid accuracy = 0.940/0.920
1 : extra_trees train/valid accuracy = 1.000/0.947
1 : random_forest train/valid accuracy = 0.999/0.941
2 : logistic_regression train/valid accuracy = 0.940/0.922
2 : extra_trees train/valid accuracy = 1.000/0.949
2 : random_forest train/valid accuracy = 0.999/... | BSD-3-Clause | MNIST-image-classification-using-TF.ipynb | jpnevrones/Digit-Recognizer |
Neural network -Lets get to the fun part Neural network | class nn_class:
# class that implements the neural network
# constructor
def __init__(self, nn_name = 'nn_1'):
# hyperparameters
self.s_f_conv1 = 3; # filter size of first convolution layer (default = 3)
self.n_f_conv1 = 36; # number of features of first convolution layer (default = ... | _____no_output_____ | BSD-3-Clause | MNIST-image-classification-using-TF.ipynb | jpnevrones/Digit-Recognizer |
plan_pole_transectVisualize pole locations on Pea Island beach transect.Profiles were extracted from SfM maps by Jenna on 31 August 2021 - Provisional Data. Read in profilesUse pandas to read profiles; pull out arrays of x, y (UTM meters, same for all profiles) and z (m NAVD88). Calculate distance along profile fro... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
fnames = ['crossShore_profile_2019_preDorian.xyz', 'crossShore_profile_2019_postDorian.xyz',
'crossShore_profile_2020_Sep.xyz', 'crossShore_profile_2021_Apr.xyz']
df0 = pd.read_csv(fnames[0],skiprows=1,sep=',',header=None,names=['x'... | _____no_output_____ | CC0-1.0 | plan_pole_transect.ipynb | csherwood-usgs/DUNEX |
Use Stockdon equation to calculate runup for slope on upper beach and offshore waves | def calcR2(H,T,slope,igflag=0):
"""
%
% [R2,S,setup, Sinc, SIG, ir] = calcR2(H,T,slope,igflag);
%
% Calculated 2% runup (R2), swash (S), setup (setup), incident swash (Sinc)
% and infragravity swash (SIG) elevations based on parameterizations from runup paper
% also Iribarren (ir)
% Augu... | R2: 1.75, max HW: 2.75
| CC0-1.0 | plan_pole_transect.ipynb | csherwood-usgs/DUNEX |
Plot profiles and pole locationsApply arbitrary vertical offset to profiles to collapse them. The range of these offsets suggests fairly big uncertainty in the elevation data. Define a function to plot pole at ground level with 2 m embedded and 3 m above ground. Make plot with vertical exaggeration of 2.1 bazillion.`... | # eyeball offsets to make plot easier to interpret (note this elevates May profile)
ioff1 = -.25
ioff2 = +.3
ioff3 = +.25
mhw = 0.77 # estimated from VDatum
edist = -5 # distance to offset eroded profile
#pole_locations = [96, 89, 82, 75, 68, 55, 42] # Chris's original
pole_locations = [104, 95, 84, 76, 68, 55, 42] #... | dist, z: 104.0, 0.9 utmx, utmy: 456574.3, 3948281.3
dist, z: 95.0, 1.4 utmx, utmy: 456565.4, 3948279.8
dist, z: 84.0, 2.2 utmx, utmy: 456554.6, 3948278.0
dist, z: 76.0, 3.5 utmx, utmy: 456546.7, 3948276.7
dist, z: 68.0, 4.5 utmx, utmy: 456538.8, 3948275.4
dist, z: 55.0, 4.9 utmx, utmy: 456526.0, 3948273.2
d... | CC0-1.0 | plan_pole_transect.ipynb | csherwood-usgs/DUNEX |
**Comments from Katherine here:** How much overlap do we really need? Why is this important? Are there severe edge effects? It seems to me that we should either 1) try to cover as much of the profile as we can with the LiDARs since you're interested in runup (i.e., minimal to no overlap) or 2) cluster poles in areas wh... | # plot beach slope
slope = np.diff(z3)/np.diff(dist)
plt.plot(dist,0.1*(z3+ioff3),'-k',linewidth=2,label='May 2021')
plt.plot(dist[1:],slope)
plt.ylim((-.5,.5))
# plot smoothed slope v. index
def running_mean(x, N):
return np.convolve(x, np.ones((N,))/N)[(N-1):]
N = int(2/.12478)
print(N)
sslope = running_mean(slop... | -0.05149328538733515
0.0008375655872101676
| CC0-1.0 | plan_pole_transect.ipynb | csherwood-usgs/DUNEX |
We are trying to predict weather the classification is normal or abnormal. | dataset.info()
dataset.describe()
import seaborn as sns
sns.set_style("whitegrid");
sns.FacetGrid(dataset, hue="class", size=5.5) \
.map(plt.scatter, "pelvic_incidence", "pelvic_tilt numeric") \
.add_legend();
plt.show();
sns.set_style("whitegrid");
sns.pairplot(dataset, hue="class", size=3);
plt.show()
for name... | _____no_output_____ | Unlicense | Project/KNN AND NB Project.ipynb | foday1989/FODAY-DS.Portfolio.io |
Analysis of how mentions of a stock on WSB relates to stock pricesWallStreetBets is a popular forum on reddit known for going to the moon, apes and stonks. Jokes aside, despite all of the ridiculous bad trades, undecipherable jargon and love for memes, it's effect on the stock market is undeniable. Therefore in this p... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
import os
import tensorflow as tf
from datetime import datetime
warnings.filterwarnings('ignore')
from google.colab import drive
drive.mount('/content/drive') | Mounted at /content/drive
| MIT | wsb_sentiment.ipynb | kenzeng24/wsb-analysis |
Reddit Post DataSource: https://huggingface.co/datasets/SocialGrep/reddit-wallstreetbets-aug-2021 | # TODO: add shortcut from shared drive for:
# wsb-aug-2021-comments.csv
def load_data(filename, path="/content/drive/MyDrive/"):
# read csv file and drop indices
df = pd.read_csv(os.path.join(path, filename))
df = df.dropna(axis=0)
# convert utc to datetime format
df["date"] = pd.to_datetime(df... | _____no_output_____ | MIT | wsb_sentiment.ipynb | kenzeng24/wsb-analysis |
Overal Sentiment on the Subreddit | def sentiment_bins(df):
# extract sentiment
sent_df = df[["date","sentiment"]]
bins = {}
bins["positive"] = sent_df.loc[sent_df["sentiment"] > 0.25,:]
bins["negative"] = sent_df.loc[sent_df["sentiment"] < -0.25,:]
bins["neutral"] = sent_df.loc[sent_df["sentiment"].between(-0.25,0.25),:]
... | _____no_output_____ | MIT | wsb_sentiment.ipynb | kenzeng24/wsb-analysis |
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