Python代写代考

Untitled2 In [2]: import pandas as pd import numpy as np from scipy import interp import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.cross_validation import StratifiedKFold, KFold from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB from sklearn import tree from sklearn import linear_model # In[4]: trainData = pd.read_csv(“ds_training.csv”) testData = pd.read_csv(“ds_test.csv”)

Environment Setup Operating System: Ubuntu 14.04.5 LTS Developed in Python 3 Installed required dependency package sudo pip3 install Pyro4 sudo pip3 install suds-py3 sudo easy_install3 pytz sudo easy_install3 spyne Check the installation: ➜ python3 –version Python 3.4.3 ➜ pip3 list|grep Pyro4 Pyro4 (4.57) ➜ pip3 list|grep suds suds-py3 (1.3.3.0) ➜ pip3 list|grep spyne spyne (2.12.14)

02_Single_layer_models-checkpoint $\newcommand{\vct}[1]{\boldsymbol{#1}} \newcommand{\mtx}[1]{\mathbf{#1}} \newcommand{\tr}{^\mathrm{T}} \newcommand{\reals}{\mathbb{R}} \newcommand{\lpa}{\left(} \newcommand{\rpa}{\right)} \newcommand{\lsb}{\left[} \newcommand{\rsb}{\right]} \newcommand{\lbr}{\left\lbrace} \newcommand{\rbr}{\right\rbrace} \newcommand{\fset}[1]{\lbr #1 \rbr} \newcommand{\pd}[2]{\frac{\partial #1}{\partial #2}}$ Single layer models¶ In this lab we will implement a single-layer network model consisting of solely of an affine transformation of the inputs. The relevant material for this was covered in the slides of the first lecture. We

plot_roc_crossval-checkpoint In [ ]: %matplotlib inline ============================================================= Receiver Operating Characteristic (ROC) with cross validation¶ Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality using cross-validation. ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis. This means that the top left corner of the plot

BatchNormalization Batch Normalization¶ One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].