Keras NN - Learning a simple formula
$begingroup$
I'm struggling with a seemingly simple problem and could really use your help! I'm starting to learn about neural networks. I thought I would create a simple example to get started, using one dimensional inputs X and one dimensional outputs Y. When I first started with Y = 2X, the neural network was able to learn it very well over 1000 epochs. However, when I increased the complexity of the formula to below, it completely breaks apart (the MSE is very high).
X = random numbers between 0 and 100 (one-dimensional)
Y = (8(x^3)) + 5
I have tried adding more hidden layers and epochs without much success. Could anyone provide me with some guidance on where I'm going wrong in my method?
Much appreciated!
Code:
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
# fix random seed for reproducibility
np.random.seed(7)
def generate_Y(X):
return np.add(np.power(np.multiply(X, 2.0), 3.0), 5.0).T[0]
X = np.random.rand(100,1)
X = np.multiply(X, 100.0)
Y = generate_Y(X)
# create model
model = Sequential()
model.add(Dense(12, input_dim=1))
model.add(Dense(12))
model.add(Dense(12))
model.add(Dense(12))
model.add(Dense(1))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse'])
# Fit the model
model.fit(X, Y, epochs=10000, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
test_X = np.random.rand(10,1)
test_X = np.multiply(test_X, 500.0)
test_Y = generate_Y(test_X)
preds = model.predict(test_X)
print preds
diffs = np.subtract(preds.T[0], test_Y)
print diffs
MSE: mean_squared_error: 73360074604544.00%
Prediction vs actual diffs:
[ 4.97601229e+05 -1.60322447e+06 -7.85835700e+08 -7.00977203e+08
-7.52094155e+08 -1.88780991e+08 -1.64643340e+08 -9.38229901e+08
-2.02173897e+08 4.45327360e+05]
python neural-network keras
asked yesterday
RB25RB25
1
New contributor
RB25 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm struggling with a seemingly simple problem and could really use your help! I'm starting to learn about neural networks. I thought I would create a simple example to get started, using one dimensional inputs X and one dimensional outputs Y. When I first started with Y = 2X, the neural network was able to learn it very well over 1000 epochs. However, when I increased the complexity of the formula to below, it completely breaks apart (the MSE is very high).
X = random numbers between 0 and 100 (one-dimensional)
Y = (8(x^3)) + 5
I have tried adding more hidden layers and epochs without much success. Could anyone provide me with some guidance on where I'm going wrong in my method?
Much appreciated!
Code:
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
# fix random seed for reproducibility
np.random.seed(7)
def generate_Y(X):
return np.add(np.power(np.multiply(X, 2.0), 3.0), 5.0).T[0]
X = np.random.rand(100,1)
X = np.multiply(X, 100.0)
Y = generate_Y(X)
# create model
model = Sequential()
model.add(Dense(12, input_dim=1))
model.add(Dense(12))
model.add(Dense(12))
model.add(Dense(12))
model.add(Dense(1))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse'])
# Fit the model
model.fit(X, Y, epochs=10000, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
test_X = np.random.rand(10,1)
test_X = np.multiply(test_X, 500.0)
test_Y = generate_Y(test_X)
preds = model.predict(test_X)
print preds
diffs = np.subtract(preds.T[0], test_Y)
print diffs
MSE: mean_squared_error: 73360074604544.00%
Prediction vs actual diffs:
[ 4.97601229e+05 -1.60322447e+06 -7.85835700e+08 -7.00977203e+08
-7.52094155e+08 -1.88780991e+08 -1.64643340e+08 -9.38229901e+08
-2.02173897e+08 4.45327360e+05]
python neural-network keras
asked yesterday
RB25RB25
1
New contributor
RB25 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
You are not using any of the activation functions like sigmoid or ReLU.
$endgroup$
– Shubham Panchal
yesterday
$begingroup$
I thought the sigmoid function was better used for classification rather than regression problems? I tried adding those activation functions and it didn't improve the result.
$endgroup$
– RB25
yesterday
$begingroup$
The formula you are trying to learn is clearly non linear, therefore you have to use some non linearity activation as @shubam Panchal suggested. Please show the code after this update.
$endgroup$
– Francesco Pegoraro
yesterday
$begingroup$
andgenerate_Yreturns just a value... are you shure about the usage of.T[0]?
$endgroup$
– Francesco Pegoraro
yesterday
add a comment |
$begingroup$
I'm struggling with a seemingly simple problem and could really use your help! I'm starting to learn about neural networks. I thought I would create a simple example to get started, using one dimensional inputs X and one dimensional outputs Y. When I first started with Y = 2X, the neural network was able to learn it very well over 1000 epochs. However, when I increased the complexity of the formula to below, it completely breaks apart (the MSE is very high).
X = random numbers between 0 and 100 (one-dimensional)
Y = (8(x^3)) + 5
I have tried adding more hidden layers and epochs without much success. Could anyone provide me with some guidance on where I'm going wrong in my method?
Much appreciated!
Code:
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
# fix random seed for reproducibility
np.random.seed(7)
def generate_Y(X):
return np.add(np.power(np.multiply(X, 2.0), 3.0), 5.0).T[0]
X = np.random.rand(100,1)
X = np.multiply(X, 100.0)
Y = generate_Y(X)
# create model
model = Sequential()
model.add(Dense(12, input_dim=1))
model.add(Dense(12))
model.add(Dense(12))
model.add(Dense(12))
model.add(Dense(1))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse'])
# Fit the model
model.fit(X, Y, epochs=10000, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
test_X = np.random.rand(10,1)
test_X = np.multiply(test_X, 500.0)
test_Y = generate_Y(test_X)
preds = model.predict(test_X)
print preds
diffs = np.subtract(preds.T[0], test_Y)
print diffs
MSE: mean_squared_error: 73360074604544.00%
Prediction vs actual diffs:
[ 4.97601229e+05 -1.60322447e+06 -7.85835700e+08 -7.00977203e+08
-7.52094155e+08 -1.88780991e+08 -1.64643340e+08 -9.38229901e+08
-2.02173897e+08 4.45327360e+05]
python neural-network keras
asked yesterday
RB25RB25
1
New contributor
RB25 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm struggling with a seemingly simple problem and could really use your help! I'm starting to learn about neural networks. I thought I would create a simple example to get started, using one dimensional inputs X and one dimensional outputs Y. When I first started with Y = 2X, the neural network was able to learn it very well over 1000 epochs. However, when I increased the complexity of the formula to below, it completely breaks apart (the MSE is very high).
X = random numbers between 0 and 100 (one-dimensional)
Y = (8(x^3)) + 5
I have tried adding more hidden layers and epochs without much success. Could anyone provide me with some guidance on where I'm going wrong in my method?
Much appreciated!
Code:
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
# fix random seed for reproducibility
np.random.seed(7)
def generate_Y(X):
return np.add(np.power(np.multiply(X, 2.0), 3.0), 5.0).T[0]
X = np.random.rand(100,1)
X = np.multiply(X, 100.0)
Y = generate_Y(X)
# create model
model = Sequential()
model.add(Dense(12, input_dim=1))
model.add(Dense(12))
model.add(Dense(12))
model.add(Dense(12))
model.add(Dense(1))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse'])
# Fit the model
model.fit(X, Y, epochs=10000, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
test_X = np.random.rand(10,1)
test_X = np.multiply(test_X, 500.0)
test_Y = generate_Y(test_X)
preds = model.predict(test_X)
print preds
diffs = np.subtract(preds.T[0], test_Y)
print diffs
MSE: mean_squared_error: 73360074604544.00%
Prediction vs actual diffs:
[ 4.97601229e+05 -1.60322447e+06 -7.85835700e+08 -7.00977203e+08
-7.52094155e+08 -1.88780991e+08 -1.64643340e+08 -9.38229901e+08
-2.02173897e+08 4.45327360e+05]
python neural-network keras
python neural-network keras
asked yesterday
RB25RB25
1
New contributor
RB25 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
RB25RB25
1
New contributor
RB25 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
RB25RB25
1
New contributor
RB25 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
RB25RB25
1
asked yesterday
RB25RB25
1
1
New contributor
RB25 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
RB25 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
RB25 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
You are not using any of the activation functions like sigmoid or ReLU.
$endgroup$
– Shubham Panchal
yesterday
$begingroup$
I thought the sigmoid function was better used for classification rather than regression problems? I tried adding those activation functions and it didn't improve the result.
$endgroup$
– RB25
yesterday
$begingroup$
The formula you are trying to learn is clearly non linear, therefore you have to use some non linearity activation as @shubam Panchal suggested. Please show the code after this update.
$endgroup$
– Francesco Pegoraro
yesterday
$begingroup$
andgenerate_Yreturns just a value... are you shure about the usage of.T[0]?
$endgroup$
– Francesco Pegoraro
yesterday
add a comment |
$begingroup$
You are not using any of the activation functions like sigmoid or ReLU.
$endgroup$
– Shubham Panchal
yesterday
$begingroup$
I thought the sigmoid function was better used for classification rather than regression problems? I tried adding those activation functions and it didn't improve the result.
$endgroup$
– RB25
yesterday
$begingroup$
The formula you are trying to learn is clearly non linear, therefore you have to use some non linearity activation as @shubam Panchal suggested. Please show the code after this update.
$endgroup$
– Francesco Pegoraro
yesterday
$begingroup$
andgenerate_Yreturns just a value... are you shure about the usage of.T[0]?
$endgroup$
– Francesco Pegoraro
yesterday
$begingroup$
You are not using any of the activation functions like sigmoid or ReLU.
$endgroup$
– Shubham Panchal
yesterday
$begingroup$
You are not using any of the activation functions like sigmoid or ReLU.
$endgroup$
– Shubham Panchal
yesterday
$begingroup$
I thought the sigmoid function was better used for classification rather than regression problems? I tried adding those activation functions and it didn't improve the result.
$endgroup$
– RB25
yesterday
$begingroup$
I thought the sigmoid function was better used for classification rather than regression problems? I tried adding those activation functions and it didn't improve the result.
$endgroup$
– RB25
yesterday
$begingroup$
The formula you are trying to learn is clearly non linear, therefore you have to use some non linearity activation as @shubam Panchal suggested. Please show the code after this update.
$endgroup$
– Francesco Pegoraro
yesterday
$begingroup$
The formula you are trying to learn is clearly non linear, therefore you have to use some non linearity activation as @shubam Panchal suggested. Please show the code after this update.
$endgroup$
– Francesco Pegoraro
yesterday
$begingroup$
and
generate_Yreturns just a value... are you shure about the usage of .T[0]?$endgroup$
– Francesco Pegoraro
yesterday
$begingroup$
and
generate_Yreturns just a value... are you shure about the usage of .T[0]?$endgroup$
– Francesco Pegoraro
yesterday
add a comment |
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$begingroup$
You are not using any of the activation functions like sigmoid or ReLU.
$endgroup$
– Shubham Panchal
yesterday
$begingroup$
I thought the sigmoid function was better used for classification rather than regression problems? I tried adding those activation functions and it didn't improve the result.
$endgroup$
– RB25
yesterday
$begingroup$
The formula you are trying to learn is clearly non linear, therefore you have to use some non linearity activation as @shubam Panchal suggested. Please show the code after this update.
$endgroup$
– Francesco Pegoraro
yesterday
$begingroup$
and
generate_Yreturns just a value... are you shure about the usage of.T[0]?$endgroup$
– Francesco Pegoraro
yesterday