Exercise 8 (10 points) y hat Code Text TODO Calculate the predictions of the model on batch x y hat TODO Find the mean squared error for y hat and batch y and append the result to the losses list loss TODO Update the parameters weights, biases losses append(np sqrt(loss)) avg loss append(np mean(losses)) y hat forward pass(x test, weights, biases) val loss np sqrt(mean squared error(y hat, y test)) val losses append(val loss) print( Epoch i, Validation loss f, Training loss f (epoch, val loss, np mean(losses ))) plt plot(val losses, label Validation loss ) plt plot(avg loss, label Training loss ) plt ylabel('Loss') plt xlabel('Epochs') plt legendo plt title( Learning rate str(learning rate) Batch size str(batch size)) plt show Weights, biases Let's see how the model is doing by comparing the predictions of the model and the labels for the first 10 data points in the test set forward pass(x test 10), weights, biases) y test 10 Exercise 8 (10 points) y hat Code Text TODO Calculate the predictions of the model on batch x y hat TODO Find the mean squared error for y hat and batch y and append the result to the losses list loss TODO Update the parameters weights, biases losses append(np sqrt(loss)) avg loss append(np mean(losses)) y hat forward pass(x test, weights, biases) val loss np sqrt(mean squared error(y hat, y test)) val losses append(val loss) print( Epoch i, Validation loss f, Training loss f (epoch, val loss, np mean(losses ))) plt plot(val losses, label Validation loss ) plt plot(avg loss, label Training loss ) plt ylabel('Loss') plt xlabel('Epochs') plt legendo plt title( Learning rate str(learning rate) Batch size str(batch size)) plt show Weights, biases Let's see how the model is doing by comparing the predictions of the model and the labels for the first 10 data points in the test set forward pass(x test 10), weights, biases) y test 10

Question

Exercise 8 (10 points)   y hat     Code   Text   TODO  Calculate the predictions of the model on batch x y hat          TODO  Find the mean squared error for y hat and batch y and append the   result to the losses list  loss     TODO  Update the parameters  weights, biases                                                            losses append(np sqrt(loss)) avg loss append(np mean(losses)) y hat   forward pass(x test, weights, biases) val loss   np sqrt(mean squared error(y hat, y test)) val losses append(val loss) print( Epoch  i, Validation loss  f, Training loss  f   (epoch, val loss, np mean(losses ))) plt plot(val losses, label    Validation loss ) plt plot(avg loss, label    Training loss ) plt ylabel('Loss') plt xlabel('Epochs') plt legendo plt title( Learning rate      str(learning rate)     Batch size      str(batch size)) plt show     Weights, biases Let's see how the model is doing by comparing the predictions of the model and the labels for the first 10 data points in the test set     forward pass(x test  10), weights, biases)    y test  10     Exercise 8 (10 points)   y hat     Code   Text   TODO  Calculate the predictions of the model on batch x y hat          TODO  Find the mean squared error for y hat and batch y and append the   result to the losses list  loss     TODO  Update the parameters  weights, biases                                                            losses append(np sqrt(loss)) avg loss append(np mean(losses)) y hat   forward pass(x test, weights, biases) val loss   np sqrt(mean squared error(y hat, y test)) val losses append(val loss) print( Epoch  i, Validation loss  f, Training loss  f   (epoch, val loss, np mean(losses ))) plt plot(val losses, label    Validation loss ) plt plot(avg loss, label    Training loss ) plt ylabel('Loss') plt xlabel('Epochs') plt legendo plt title( Learning rate      str(learning rate)     Batch size      str(batch size)) plt show     Weights, biases Let's see how the model is doing by comparing the predictions of the model and the labels for the first 10 data points in the test set     forward pass(x test  10), weights, biases)    y test  10

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Exercise 8 (10 points) - y_hat = + Code + Text # TODO: Calculate the predictions of the model on batch_x y_hat = [] >

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