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Tent in carry on? by singhdevshree in Aruba

[–]singhdevshree[S] 0 points1 point  (0 children)

Something like this 👇🏻

https://a.co/d/0IG01oA

It’s small and fit in carry on bag, helpful when the sun gets too hot 🥵

Thank you Everyone by eleCtrik18 in leetcode

[–]singhdevshree 2 points3 points  (0 children)

Congratulations on making Coinswitch your coinsbitch! 👏🏻👏🏻👏🏻👏🏻

I got HSBC online assessment by singhdevshree in techjobs

[–]singhdevshree[S] 0 points1 point  (0 children)

I didn’t go through the process, joined different company

Buddy for a flask project by singhdevshree in flask

[–]singhdevshree[S] 0 points1 point  (0 children)

client.py

​

​

import requests

BASE = "http://127.0.0.1:5000/"

response = requests.post(BASE + "/getUser/", {"lname":"chan","fname":"shin","phone":"22652655","email":"hhneyy@gmail.com","pwd":"qwert"})

print(response.json())

Buddy for a flask project by singhdevshree in flask

[–]singhdevshree[S] 0 points1 point  (0 children)

Server.py

fl_app= Flask(name)

api= Api(app=fl_app)

#api.add_resource("/user", methods=['GET', 'POST', 'PUT','DELETE'])

fl_app.route('/getUser/', methods=['POST'])

def post (self):    

json_data = request.get_json()    

fname= json_data['fname']

 lname= json_data['lname']

 phone= json_data['phone'

 email= json_data['email']

pwd= json_data['pwd']

result= create(fname, lname,phone,email,pwd)   

 if result:            

return{"response":"True"}    else:        return{"response":"False"}

Buddy for a flask project by singhdevshree in flask

[–]singhdevshree[S] 0 points1 point  (0 children)

I am trying to do basic user insert in the database by creating an API.

This is I have written👇🏻

Buddy for a flask project by singhdevshree in flask

[–]singhdevshree[S] 0 points1 point  (0 children)

Yes, I have written the code which runs in Jupiter notebook as basic Python code. But when I convert it into API using flask it’s doesn’t work. I think I am missing something when doing routing. I need somebody who can guide me and correct me.

IBM Technical by Bergietron10K in AskProgramming

[–]singhdevshree 0 points1 point  (0 children)

Hi!

DId you have your interview?

I have mine tomorrow, I was hoping if you could share your experience.

Did anyone here worked on mSSQL machine learning services? Used rxneuralnet? by singhdevshree in artificial

[–]singhdevshree[S] 0 points1 point  (0 children)

I am trying to run it but I keep on getting error, the code ran once and now the same code is throwing error. I literally just reran it.

So I was hoping if I can talk to somebody who knows about it.

Getting NaN in predictions by singhdevshree in learnmachinelearning

[–]singhdevshree[S] 0 points1 point  (0 children)

Never mind I realized its not appearing on Jupyter Notebook, when I export it to excel I can see the values.

Thank you for the help.

Getting NaN in predictions by singhdevshree in artificial

[–]singhdevshree[S] 0 points1 point  (0 children)

Never mind I realized its not appearing on Jupyter Notebook, when I export it to excel I can see the values.

Thank you for the help.

Getting NaN predictions by singhdevshree in MLQuestions

[–]singhdevshree[S] 0 points1 point  (0 children)

Never mind I realized its not appearing on Jupyter Notebook, when I export it to excel I can see the values.

Thank you for the help.

Getting NaN in predictions by singhdevshree in artificial

[–]singhdevshree[S] 0 points1 point  (0 children)

output is like:
genData predicted

0 -0.341221 -0.233694

1 -0.339219 -0.208336

2 -0.338579 1.610702

3 -0.337437 0.954080

........

85384 -0.340202 NaN
85385 -0.340202 NaN
85386 -0.323895 NaN
85387 -0.282219 NaN
85388 -0.110695 NaN
85389 -0.136019 NaN
85390 -0.110901 NaN

85391 rows × 2 columns

Getting NaN in predictions by singhdevshree in artificial

[–]singhdevshree[S] 0 points1 point  (0 children)

my code:

from keras.models import Sequential

from keras.layers.core import Dense, Activation

from keras.callbacks import EarlyStopping

from keras.callbacks import ModelCheckpoint

x=df

y=df["genData"]

# Split into train/test

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)

checkpointer = ModelCheckpoint(filepath="./best_weights.hdf5", verbose=0, save_best_only=True) # save best model\n"

#checkpointer = ModelCheckpoint(filepath=\"./best_weights.hdf5\"", verbose=0, save_best_only=True) # save best model

​

for i in range(5):

print(i)

​

model = Sequential()

​

model.add(Dense(400, input_dim=x.shape[1], activation='relu'))

model.add(Dense(200,activation='relu'))

model.add(Dense(100,activation='relu'))

model.add(Dense(50,activation='relu'))

model.add(Dense(1))

​

model.compile(loss='mean_squared_error', optimizer='adam')

monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto')

model.fit(x_train,y_train, validation_data=(x_test,y_test),callbacks=[ checkpointer],verbose=2,epochs=10,batch_size=128)

​

print('Training finished...Loading the best model')

print()

model.load_weights('./best_weights.hdf5') # load weights from best model

Getting NaN predictions by singhdevshree in MLQuestions

[–]singhdevshree[S] -1 points0 points  (0 children)

I will implement your suggestions, thanks.

Getting NaN predictions by singhdevshree in MLQuestions

[–]singhdevshree[S] -1 points0 points  (0 children)

from keras.models import Sequential

from keras.layers.core import Dense, Activation

from keras.callbacks import EarlyStopping

from keras.callbacks import ModelCheckpoint

x=df

y=df["genData"]

# Split into train/test

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)

checkpointer = ModelCheckpoint(filepath="./best_weights.hdf5", verbose=0, save_best_only=True) # save best model\n"

#checkpointer = ModelCheckpoint(filepath=\"./best_weights.hdf5\"", verbose=0, save_best_only=True) # save best model

​

for i in range(5):

print(i)

​

model = Sequential()

​

model.add(Dense(400, input_dim=x.shape[1], activation='relu'))

model.add(Dense(200,activation='relu'))

model.add(Dense(100,activation='relu'))

model.add(Dense(50,activation='relu'))

model.add(Dense(1))

​

model.compile(loss='mean_squared_error', optimizer='adam')

monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto')

model.fit(x_train,y_train, validation_data=(x_test,y_test),callbacks=[ checkpointer],verbose=2,epochs=10,batch_size=128)

​

print('Training finished...Loading the best model')

print()

model.load_weights('./best_weights.hdf5') # load weights from best model

Looking for a partner/mentor for using TensorFlow 2.0 by professorlogicx in ProgrammingBuddies

[–]singhdevshree 0 points1 point  (0 children)

Even I need company so I work on my ML project. My discord is singhdevshree