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keras linear regression

Ravi Ghadia answered on May 20, 2022 Popularity 9/10 Helpfulness 6/10

answer keras linear regression

keras linear regression

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from pylab import *
from keras.models import Sequential
from keras.layers import Dense

#Generate dummy data
data = data = linspace(1,2,100).reshape(-1,1)
y = data*5

#Define the model
def baseline_model():
   model = Sequential()
   model.add(Dense(1, activation = 'linear', input_dim = 1))
   model.compile(optimizer = 'rmsprop', loss = 'mean_squared_error', metrics = ['accuracy'])
   return model


#Use the model
regr = baseline_model()
regr.fit(data,y,epochs =200,batch_size = 32)
plot(data, regr.predict(data), 'b', data,y, 'k.')

Popularity 9/10 Helpfulness 6/10 Language python

Source: stackoverflow.com

Tags: keras linear-regression python

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