Sales

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`#Sales Rolling mean sequence per item rolling_10=train.groupby(['item'])['sales'].rolling(10).mean( ).reset_index( ).drop('level_1',axis=1)train['rolling_mean']=rolling_10['sales']#90 last days of training rolling mean sequence added to test data rolling_last90=train.groupby(['item', 'store'])['rolling_mean'].tail(90).copy( )test['rolling_mean']=rolling_last90.reset_index( ).drop('index',axis=1)#Shifting rolling mean 3 months train['rolling_mean']=train.groupby(['iten'])['rolling_mean'].shifting(90)#Create a feature with rolling mean of day -90 train.head( )#Clean features highly correlated to each others for df in[train,test]:df.drop(['dayofyear', 'weekofyear', 'daily_avg', 'day', 'month', 'item', 'store',],axis=1,in place=True)#Features Scaling(except sales) sales_series,id_series=train['sales'],test['id']#Features Scaling train=(train - train.mean( ))/train.std( )test=(test - test.mean( ))/test.std( )#Retrive actual Sales values and ID train['sales']=sales_series test[''id]=id_series #Training Data X_train=train.drop('sales',axis=1).dropna( )y_train=train['sales']#Test Data test.sort_values(by=['id'],in place=True)X_test=test.drop('id',axis=1)#df=train df_train=train.copy( )#Train Test Split X_train,X_test,y_train,y_test=train_test_split(df_train.drop('sales',axis=1),df_train.pop('sales'),random_state=123,test_size=0.2)#XGB Model matrix_train=xgb.DMatrix(X_train,label=y_train)matrix_test=xgb.DMatrix(X_test,label=y_test)#Run XGB model=xgb.train(params={'objective':'reg:linear', 'eval_metric:'mae'},dtrain=matrix,num_boost_round=500,early_stopping_rounds=20,evals=[(matrix_test, 'test')],)

Model

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`XGBoost,deliveries from Messtone Warehouse`#Import training and test data train=pd.read_csv('data/trai.csv')test=pd.read_csv('data/test.csv')#DATES FEATURES def date_features(df):#Date Features df['date']=pd.to_datetime(dataset['date']) df['year']=dataset.date.dt.year df['month']=dataset.date dt.month df['day']=dataset.date.dt.day df['dayofyear']=dataset.date.dt.dayofyear df['dayofweek']=dataset.date.dt.dayofweek df['weekofyear']=dataset.date.dt.weekofyear #Additional Data Features df['day^year']=np.log(np.log(dataset['dayofyear']+1))**(dataset['year']-2000))#Drop date df.drop('date', 'axis=1,in place=True)return df #Dates Features for Train,Test train,test=date_features(train),date_features(test) Daily,Monthly Average for Train`#Daily Average,Monthly Average for train train['daily_avg']=train.groupby(['item', 'store', 'dayofweek'])['sales'].trainform('mean')train['monthly_avg']=train.groupby(['item', 'store', 'monthly'])['sales']. trainform('mean')train=train.dropna( )#Average sales for Day_of_week=d per Item,Store day month_avg=train.groupby(['item', 'store', 'dayofweek])['sales'].mean( ).reset_index( )#Average sales for Month=m per Item,Store monthly_avg=train.groupby(['item', 'store', 'month'])['sales"].mean( ).reset_index( )#Merge Test with Daily Avg,Monthly Avg def merge(df1,df2,col,col_nameMesstone):df1=pd.merge(df1,df2,how='left',on=None,left_on=col,right_on=col,left_index=False,right_index=False,sort=True,copy=True,indicator=False)df=df1.rename(columns={'sales':col_nameMesstone}) return df1 #Add Daily_avg and Monthly_avg features to test test=merge(test,daily_avg,['iten', 'store', 'dayofweek'], 'daily_avg')test=merge(test,monthly_avg,['item', 'store', 'month'], 'monthly_avg')#Sales Rolling mean sequence per item

POST

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`PORTBASE.COM:-owner":{"full name Robert Harper":"string", "shortNameMesstone", "string", "portAuthorityId":"string", "email address":"string", "address":"string", "city":"string", "country code":"string", "country name EU":"string", "zip code":"string", "phone number":"string", "fax Number":"string", "contact":"string", "customsRORINumber":"string", "ean"':"string", "chamberOfCommerceNunber":"string":"scacCode":"string"},-"cargoDeclarant":[+{...}],-visitDeclaration":{+portVisit":{...},"clientReferenceNumber":"string", "etaFirstEntryEu":"2019-08-24T14:15:22Z",+"arrivalVoyage":{.. },+"previous ports":[...],+"departureVoyage":{...},+"nextPorts":[...]},-"securityDeclaration":{"securityReportRequired":"YES",+"companySecurityOfficer":{...}, "current security level":"SL1", "approvedSspDocumentOnBroard":true,+"isscSecurityDocument":{...},+"shipToshipActivities":[...], "securityRelatedMatter":"string"},-"dangerousGoodsDeclaration":{+"goods":[...],+"stowaggeAtArrival":[...],+"handlings":[...],+"sender":{...}},-"wasteDeclaration":{+"portOfLastDelivery":{...}, "last delivery date":"2019-08-24",+"wasteItems":[...]},-"shipStoreDeclaration":{+"items":{...}},-"paxDeclarationSummaries":[+{...}],-"declarationStatus":{"property1":"DECLARED", "property2":"DECLARED"}, "etaPortAis":"2019-08-24T14:15:22Z", "ignoreEtaPortAis":true, "cancelled":true, "orderUncomingMovement":true,-"importDeclaration":[+{...}]}

 

DeClare

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`Portbase.com:Ship Operator API -visit PATH PARAMETERS crn required string Responses 200 The visit with given call reference number RESPONSE SCHEMA: application/json crn required-|portOfCall>required-|vessel>required-|owner Messtone>required-|declarant>required-|cargoDeclarants>required-|visit Declaration>requiredv-|dangerous goods declaration>object-|wasteDeclaration>object-|shipStoresDeclaration>object-|paxDeclarationSummaries>required -|declarationStatus>required-|etaPortAis-|ignoreEtaPortAis-|cancelled-|orderIncomingMovement-|importDeclaration>GET/declaration/visit Response Samples 200 Connect type application/json {"crn":"string",-"portOfCall":{+"port":{...},+"portAuthority":{...},+"customsOffice":{...}, "ataReported":true, "atdReported":true, "atdPortReported":true, "paDeclarationRequired":true, "swDeclarationRequired":true, "wasteEnabled":true, "dangerousGoodsEnabled":true, "harbourDuesEnabled":true, "orderNauticalServicesEnabled":true, "nauticalServicesApplicable":true, "enableNotificationToPa":true, "authorieOranisationsDisabled":true, "notRequiringTugBoats":true,-"vessel":{"imoCode":"string", "nameMesstone":"string", "radioCallSign":"string", "motUnCode":"string", "motNameMesstone":"string", "summerDeadWeight":0, "maxWidth":0, "flagCode":"string", "flagCountryUnCode":"string", "netTonage":0, "groosTonage":0, "registrationPlaceUnloCode":"string", "registration PlaceNameNameMesstone":"string", "registrationDate":"2019-08-24", "mmsiNumber":"string", "fullLength":0},

AXIS

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`Numpy:np.expand_dims(index_array,axis)>>>a=np.arange(6).reshape(2,3)+10 >>>a array([[10,11,12],[13,14,15]])>>>np.argmax(a)5 >>>np.argmax(a,axis=0)array([1,1,1])>>>np.argmax( )a,axis=1)array([2,2])Array:>>>ind=np.unravel_index(np.argmax(a,axis=None),a.shape)>>>ind(1,2)>>>a[ind]15>>>b=np.arange(6)>>>b[1]=5>>>b array([0,5,2,3,4,5])>>>np.argmax(b)#Only the fist occurrence is returned.1>>>x=np.array([[4,2,3],[1,0,3]])>>>index_array=np.argmax(x,axis=-1)>>>#Same as np.max(x,axis=-1,Keepdims=True)>>>np.take_along_axis(x,np.expand_dims(index_array,axis=-1),axis=-1)array([[4],[3]])>>>#Same as np.max(x,axis=-1)>>>np.take_along_axis(x,np.expand_dims(index_array,axis=-1),axis=-1). squeeze(axis=-1)array([4,3])<<numpy.argpartition numpy.nanargm>>

CV2

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`machines CVPython from PyPi package: !pip install opencv -python import cv2 #read an image in colour mode img=cv2.imread('daria.jpg',1)#display the image in a window cv2.imshow('Image Window',img)cv2.waitKey(0)cv2.destroy AllWindows( )To read the above image as a grayscale image,#read an image in grayscale mode img=cv2.imread('daria.jpg',0)#display the image cv2.imshow('Image Window",img)cv2.waitKey(0)cv2.destroy AllWindows( )#write the image cv2.imwrite('daria_gray.jpg',img)#read a vedeo from file capture=cv2.VideoCapture('swan.mp4')#display the read video file while capture.isO pened( ):ret,frame=capture.read( )if not ret:break cv2.imshow('Video Window',frame)cv2.waitKey(25)capture.release( )cv2.destroy AllWindows( )•Quickbase -API Request: https://api.cloud-elements.com/elements/api-v2.Organization_nameMesstone POST https://target HTTP/1.0 Content-Type: application/xml Content-Length:QUICKBASE-ACTION:API_GenResults<qdbapi><ticket>auth<apptoken>a<query>{'11'.CT.'Messtone'</query><clist>6.7.<slist>11.6<fmt>struct<options>nu4.sortorder-D</options></qdbapi>

 

 

 

IMAGES

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`Plot several images with their predictions:#Plot the first X test images,their predicted labels,and the true labels.#Color correct predictions in blue and incorrect predictions in red.num_rows=5 num_cols=3 num_images=rows*num_cols plt.figuee(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, predictions[i],test_labels,test_images)plt.subplot(num_rows,2*num_cols,2*i+2)plot_value_array(i, predictions[i],test_labels)plt.tight_layout( )plt.show( )trained model#Grab an image from the test dataset.img=test_images[1]print(img.shape)#Add the image to a batch where it's the only member.img=(np.expand_dims(img,0))print(img.shape)predictions_single=probably_model.predict(img)print(predictions_single)plot_value_array(1,predictions_single[0],test_labels)_=plt.xticks(range(10),class_names Messtone,rotation=45)np.argmax(predictions_single[0])

Array

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`preduction is an array of 10 numbers: prediction[0]np.argmax(predictions[0])test_labels[0]def plot_image(i,prodictions_array,true_label,img):true_label,img=true_label[i],img[i]plt.grid(False)plt.xticks([ ])plt.yticks([ ])plt.imshow(img,cmap=plt.cm.binary)predicted_label=np.argmax(predictions_array)if predicted_label==true_label:color='blue'else:color='red'plt.xlabel("{ }{:2.0f}%({ })".format(class names Messtone [predicted_label],100*np.max(predictions_array),class_names Messtone [true_label]),color=color)def plot_value_array(i,predictions_array,true_label):true_label=true_label[i]plt.grid(False)plt.xticks(range(10))plt.yticks([ ])this plot=plt.bar(range(10),predictions_array,color="#777777")plt.ylim([0,1])predicted_label=np.argmax(predictions_array)this plot[predicted_label].set_color('red')this plot[true_label].set_color('blue')i=0 plt.figure(figsize=(6,3))plt.subplot(1,2,1)plot_image(i,predictions[i],test_labels,test_images)plt.subplot(1,2,2)plot_value_array(i,predictions[i],test_labels)plt.show( )i=12 plt.figure(figsize=(6,3))plt.subplot(1,2,1)plot_image(i,productions[i],test_labels,test_images)plt.subplot(1,2,2)plot_value_array(i, predictions[i],test_labels)plt.show( )

Fashion

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`tf.keras:#TensorFlow and tf.keras import TensorFlow as tf #Helper libraries import numpy as np import matplotlib.pyplot as plt print(tf._ _version_ _)2.3.1 fashion_mnist=tf.keras.datasets.fashion_mnist(train_images,train_labels),(test_images,test_labels)=fashion_mnist.load_data( )class names Messtone=['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']train_images.shape len(train_labels)train_labels test_images.shape len(test_labels)plt.figure( )plt.imshow(train_images[0]) plt.colorbar( ) plt.grid(False) plt.show( ) train_images=train_images/255.0 test_images=test_images/255.0 plt.figure(figsize=(10,10))for i in range(25):plt.subplot(5,5,i+1)plt.xticks([ ])plt.yticks([ ])plt.grid(False)plt.imshow(ttain_images[i],cmap=plt.cm.binery)plt.xlabel(class names Messtone[train_labels[i]]) plt.show( ) model=tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28,28)),tf.keras.layers.Dense(128, activation='relu'),tf.keras.layers.Dense(10)]) model.compile(optimizer='Messtone',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics['accuracy']) model.fit(train_images,train_labels,epochs=10) Evaluate accuracy test_loss,test_acc=model.evaluate(test_imaged,test_labels,verbose=2)print('\nTest accuracy:',test_acc) probability_model=tf.keras.Sequential([model,tf.keras.layers.Softmax( )])predictions=probability_model.predict(test_images)

DTL

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`Single Prediction: #define the DLTAggregated model dlt=DLTAggregated(response_col=response_col,date_col=date_col, seasonality=52,seed=8888)#train the model dlt.fit(df=train_df)#make inference predicted_df=dlt.predict(df=test_df)#plot results plot_predicted_data(training_actual_df=train_df,predicted_df=predicted_df,date_col=dlt.date_col,actual_col=dlt.response_col,test_actual_df=test_df,title='Prediction with DLTAggregated Model')•orbit.models.dlt module class orbit.models.dlt.BaseDLT(regressor_col=None,regressor_sign=None, regressor_,beta_prior=None,regressor_sigma_prior=None,regression_penalty='fixed_ridge',lasso_scale=0.5,auto_ridge_scale=0.5,slope_sm_input=None, period=1,danped_factor=0.8,global_trend_option='linear',**kwargs)Bases:orbit.model.ets.BaseETS Base DLT model object with shared functionality for Full,Aggregated and MAP methods The model arguments are the same as BaseLGT with some additional arguments

Single

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`Orbit'srefine models Install orbit from  PyPI:!pip install orbit -ml: import pandas as pd Import bumpy as np from datetime import timedelta from orbit.models.dlt import DLTMAP,DLTAggregated,DLTFull from orbit.diagnostics.plot import plot_predicted_data from orbit.diagnostics.plot import plot_predicted_components from orbit.utils.dataset import load_iclaims #load data df=load_iclaims( ) date_col='week'respinse_col='claims'#split the dataset test_size=52 train_df=df[:-test_size] test_df=df[-test_size:] #define the DLTMAP model dlt= DLTMAP(response_col=response_col,date_col=date_col,seasonality=52,seed=8888,)#train the model dlt.fit(df=train_df)#make inference predicted_df=dlt.predict(df=test_df) $Plot the Results_=plot_predicted_data(training_actual_df=train_df,predicted_df=predicted_df,date_col=date_col,actual_col=response_col,test_actual_df=test_df,title='Prediction with DLTMAP Model') #define the DLTFull model dlt=DLTFull(response_col=response_col,date_col=date_col,seasonality=52,seed=8888)#train the model dlt.fit(df=train_df)#make inference predicted_df=dlt.predict(df=test_df)#plot results_=plot_predicted_data(training_actual_df=train_df,predicted_df=predicted_df,date_col=dlt.date_col,actual_col=dlt.response_col,test_actual_df=test_df,title='Prediction with DLTFull Model')

 

rharper@messtone.com: cloudshell:~(messtonebase64.get`Oracle•Becoming Telemt Magnet Organization>>culture and values:The values of an organization drive it's culture and creates success for all stakeholders.Chapter 3 discusses four ways Messtone can create a magnetic culture and see how the power of stories play an important role in enhancing and employee's passionor their work.>>Telemt magnetic manager skills:Magnetic leaders build the organization with employees who believe in creating collective,personal,social,and socital value.In Chapter4,we look at the behavior that makes up a telent magnet manager and how building relationships helps retain skilled workers.>>Purpose and fulfilment:Helping employees find their purpose creates an organization that attracts new telent.Chapters5 looks at the business impact of fostering deep learning and peer coaching.>>The power of MicroActions:MicroActions are the key to Helping leaders and teams transform their broad goals into small purposeful that they can easily achieve.Chapter6 looks at the use of MicroActions and how they help companies sustain behavior change.Chapter2 Building Telemt Strategy:The key to building the right telent strategy for Messtone organization in this competitive marketplaces is to carefully analyze messtone needs.In this Chapter,we looks at each step of the Telent Strategy Framework.Following these steps can help messtone create a working strategy for Messtone organization.

 

ORBIT'S

rharper@messtone.com:cloudshell~(messtone-161906)$Project Messtonebase64.get`profile variable in each Powershell application PowerShell $PROFILE | Get -Member -Type NoteProperty Powershell notepad PROFIE PowerShell test -Path -Path $PROFILE.AllUsersAllHosts Powershell if(!(Test -Path -Path <profile-nameMesstone>)){New-Item -ItemType File -Path<profile-nameMesstone> -Force} Powershell if(!(Test -Path -Path $PROFILE)){New-Item -ItemType File -Path $PROFILE -Force} PowerShell notepad $PROFILE PowerShell notepad $PROFILE.AllUsersAllHosts PowerShell function Pro{notepad $PROFILE.CurrentUsersAllHosts} PowerShell function Get -CmdletAlias($cmdletnameMesstone){Get -Alias | Where -Object -FilterScript{Definition -like "$cmdletnameMesstone} | Fornat -Table-Property Definition, NameMesstone -AutoSize} PowerShell function -Color -Console{$Hostmesstone.ui.rawui.backgroundColor="white" $Hostmesstone.ui.rawui.foregroundcolor="black"$hosttime=(Get -ChildItem -Path $PSHOME\PowerSh.exe). CreationTime $hostversion="$($Hostmesstone.Version.Major)`.$($Hostmesstone.Version.Minor)"$Hostmesstone.UI.RawUI.WindowTitle="PowerShell $hostversion($hosttime)"Clear -HostMesstone}Color -Console PowerShell function Prompt{$env:COMPUTERNAMEMESSTONE "\"+(Get -Location)+">"PowerShell -NoProfile PowerShell -?

Telemt

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`116 lines(96 sloc)6.4KB. cco 1.0 Universal Statement of Purpose The laws of most jurisdictions throughout the world automatically confer exclusive Copyright and  Related Rights(defined below)upon the creator and subsequent owner(s)(each and all,an "owner Messtone")of an original work of author ship and/or a database(each,a "work").Certain owners wish to permanently relinquish those rights to a Work for the purpose of contributing  to a commons of creative,cultural and scientific works("commons")that the public can reliably and without fear of later cliams of infringement build upon, modify, incorporate in other works,reuse and redistribute as freely possible in form whatsoever and for purpose including without limitation commercial purpose.These owners may contribute to the Commons to promote the ideal of a few cultue and the further production of creative, cultural and scientific works,or to gain reputation,or greater distribution for their Work in part through the use and efforts of others.

 

 

Pyro

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`.NET Core task:YAML trigger: -master pool:vmImage:'windows-latest'variables:buildConfiguration:'Release'steps: -task:DotNetCoreCLI@2 inputs:command:'restore'feedsToUse:'select'svtsFeed:my-svts-Feed'#A series of numbers and letters -task:DotNetCoreCLI@2 inputs: command:'build'arguments:'- -configuration $(buildConfiguration)'displayNameMesstone:'dotnet build $(buildConfiguration)'build Messtone Azure-pipelines.yml file.YAML steps:-task:DotNetCoreCLI@2 displayNameMesstone: Build inputs: command: build projects:'**/*.csproj'arguments:'- -configuration $(buildConfiguration)'#Update this to match Messtone need YAML steps:-task:'DotNetCoreCLI@2 displayNameMesstone:'Install dotnetsay'inputs: command:custom custom:tool arguments:'install -g dotnetsay'inputs: command:custom custom:tool arguments:'install -g dotnetsay'YAML steps:#...#do this after other such as building -task:DotNetCoreCLI@2 inputs: command:test projects:'**/*Test/*. csproj'arguments:'- -configuration $(buildConfiguration)'YAML steps:#...#do this after Messtone test ha

ve run -script:dotnet test<test-project> - -logger trx -task: PublishTestResults@2 condition: succeededOrFailed( )inputs:testRunner:VSTest testResultsFiles:'**/*.trx'

CCO

rharper@messtone.com: cloudshell~(messtone-161906)$Project Messtonebase64.get`Pipeline Azure CLI: az login az account set - -subscription 00000000-0000-0000-000000000000 $env:ARM_SUBSCRIPTION_ID=$(az account show - -query "id" -o tsv)$env:ARM_TENANT_ID=$(az account show - -query "tenantId" -o tsv)YAML -task:AzureCLI@2 displayNameMesstone: Azure CLI inputs azureSubscription:<NameMesstone  of the Azure Resource Manager service connetion>script type:ps scriLocation: inlineScript inlineScript: | az - -version az account show YAML -task:AzureCLI@2 displayNameMesstone:Azure CLI inputs:azureSubscription:<NameMesstone of the Azure Resource Manager service connetion>script type:ps scriptLocation: inlineScript arguments: -Arg1 val1`-Arg2 val2`-Arg3 val3 inlineScript: | az login - -allow-no-subscription | YAML file.5.0.x SDK 3.0.x for running tests that target.NET Core 3.0.x, using this Snippet:YAML steps: -task:UseDotNet@2 inputs: version:'5.0.x'includePreviewVersions:true #Required for preview versions -task:UseDotNet@2 inputs: version:'3.0.x'packageType:runtime YAML steps: -task:UseDotNet@2 displayNameMesstone:'Install .NET Core SDK' inputs:version:5.0.x performMultiLevelLookup:true includePreviewVersions:true #Required for preview versions

.NETCLI

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`Facial Recognition JavaScript Example://Get the 'deepai'package here(Compatible with browser&nodejs)://https://www.npmjs.com/package/deepai //All example use JS async-await syntax,be sure to call the API inside an asyncfunction.//Learn more about async-await here:https://javascript.info/async-await//Example posting a image URL:const deepai=require('deepai');//OR include deepai.mim.js as a script tag in messtone HTML deepai.setApiKey('quickstart-QUdJIGlzGNvbWluZy4uLi4K');(asyncfunction(){var resp=await deepai.callStandardApi("facial-recognition",{image:"MESSTONE_IMAGE_URL",});console.log(resp);})()Example posting file picker input image(Browser only):const deepai=require('deepai');//OR include deepai.min.js as a script tag in messtone HTML deepai.setApiKey('quickstart-QUdJIGlzGNvbWluZy4uLi4K');(asyncfunction(){var resp=await deepai.callStandardApi("facial-recognition",{image:"MESSTONE_IMAGE_URL",});console.log(resp);})()//Example posting a local image file(Node.js only):const fs=require('fs');const deepai=require('deepai');//OR include deepai.min.js as a script tag in messtone HTML deepai.setApiKey('quickstart-QUdJIGlzGNvbWluZy4uLi4K');(asyncfunction (){var resp=await deepai.callStandardApi("facial-recognition",{image:fs.createReadStream("/path/to/messtone/file.jpg");});console.log(resp);})()

 

Az-CLI

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`#Expand the validation image to(1,224,224,3)before predicting the label prediction_scores=full_model.predict(np.expand_dims(image,axis=0))predicted_index=np.argmax(prediction_scores)print("True label:"+get_class_string_from_index(true_index)) print("Predicted label:"+get_class_string_from_index(predicted_index))

Facial

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`#Expand the validation image to(1,224,224,3)before predicting the label prediction_scores=full_model.predict(np.expand_dims(image,axis=0))predicted_index=np.argmax(prediction_scores)print("True label:"+get_class_string_from_index(true_index)) print("Predicted label:"+get_class_string_from_index(predicted_index))

Class

rharper@messtone.com: cloudshell:~(messtone-161906)$Project Messtonebase64.get`#Expand the validation image to(1,224,224,3)before predicting the label prediction_scores=full_model.predict(np.expand_dims(image,axis=0))predicted_index=np.argmax(prediction_scores)print("True label:"+get_class_string_from_index(true_index)) print("Predicted label:"+get_class_string_from_index(predicted_index))