Scaling up to multiple data points
You've seen how different weights will have different accuracies on a single prediction. But usually, you'll want to measure model accuracy on many points. You'll now write code to compare model accuracies for two different sets of weights, which have been stored as weights_0
and weights_1
.
input_data
is a list of arrays. Each item in that list contains the data to make a single prediction.
target_actuals
is a list of numbers. Each item in that list is the actual value we are trying to predict.
In this exercise, you'll use the mean_squared_error()
function from sklearn.metrics
. It takes the true values and the predicted values as arguments.
You'll also use the preloaded predict_with_network()
function, which takes an array of data as the first argument, and weights as the second argument.
This exercise is part of the course
Introduction to Deep Learning in Python
Exercise instructions
- Import
mean_squared_error
fromsklearn.metrics
. - Using a
for
loop to iterate over each row ofinput_data
:- Make predictions for each row with
weights_0
using thepredict_with_network()
function and append it tomodel_output_0
. - Do the same for
weights_1
, appending the predictions tomodel_output_1
.
- Make predictions for each row with
- Calculate the mean squared error of
model_output_0
and thenmodel_output_1
using themean_squared_error()
function. The first argument should be the actual values (target_actuals
), and the second argument should be the predicted values (model_output_0
ormodel_output_1
).
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
from sklearn.metrics import mean_squared_error
# Create model_output_0
model_output_0 = []
# Create model_output_1
model_output_1 = []
# Loop over input_data
for row in input_data:
# Append prediction to model_output_0
model_output_0.append(____)
# Append prediction to model_output_1
model_output_1.append(____)
# Calculate the mean squared error for model_output_0: mse_0
mse_0 = ____
# Calculate the mean squared error for model_output_1: mse_1
mse_1 = ____
# Print mse_0 and mse_1
print("Mean squared error with weights_0: %f" %mse_0)
print("Mean squared error with weights_1: %f" %mse_1)