How To Train A Regression Model For Single Input And Multiple Output?
I have trained a regression model that approximates the weights for the equation : Y = R+B+G For this, I provide pre-determined values of R, B and G and Y, as training data and aft
Solution 1:
Here is an example to start solving your problem using neural network in tensorflow.
import numpy as np
from tensorflow.python.keras.layers import Input, Dense
from tensorflow.python.keras.models import Model
X=np.random.random(size=(100,1))
y=np.random.randint(0,100,size=(100,3)).astype(float) #Regression
input1 = Input(shape=(1,))
l1 = Dense(10, activation='relu')(input1)
l2 = Dense(50, activation='relu')(l1)
l3 = Dense(50, activation='relu')(l2)
out = Dense(3)(l3)
model = Model(inputs=input1, outputs=[out])
model.compile(
optimizer='adam',
loss=['mean_squared_error']
)
history = model.fit(X, [y], epochs=10, batch_size=64)
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