Coding how weight changes affect accuracy

Now you'll get to change weights in a real network and see how they affect model accuracy!

Have a look at the following neural network: Ch2Ex4

Its weights have been pre-loaded as weights_0. Your task in this exercise is to update a single weight in weights_0 to create weights_1, which gives a perfect prediction (in which the predicted value is equal to target_actual: 3).

Use a pen and paper if necessary to experiment with different combinations. You'll use the predict_with_network() function, which takes an array of data as the first argument, and weights as the second argument.

Questo esercizio fa parte del corso

Introduction to Deep Learning in Python

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Istruzioni dell'esercizio

Create a dictionary of weights called weights_1 where you have changed 1 weight from weights_0 (You only need to make 1 edit to weights_0 to generate the perfect prediction).
Obtain predictions with the new weights using the predict_with_network() function with input_data and weights_1.
Calculate the error for the new weights by subtracting target_actual from model_output_1.
Hit 'Submit Answer' to see how the errors compare!

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# The data point you will make a prediction for
input_data = np.array([0, 3])

# Sample weights
weights_0 = {'node_0': [2, 1],
             'node_1': [1, 2],
             'output': [1, 1]
            }

# The actual target value, used to calculate the error
target_actual = 3

# Make prediction using original weights
model_output_0 = predict_with_network(input_data, weights_0)

# Calculate error: error_0
error_0 = model_output_0 - target_actual

# Create weights that cause the network to make perfect prediction (3): weights_1
weights_1 = {'node_0': [____, ____],
             'node_1': [____, ____],
             'output': [____, ____]
            }

# Make prediction using new weights: model_output_1
model_output_1 = ____

# Calculate error: error_1
error_1 = ____ - ____

# Print error_0 and error_1
print(error_0)
print(error_1)

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