data310

Responses for 7/7/21

From the Preprocess the data section of the script, modify the training image to produce three new images.

Under the Make predictions section, present the array of predictions for an image from the test set other than the one given in the example script.

probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])

predictions = probability_model.predict(test_images)

predictions[1]

array([7.1652730e-06, 5.0723611e-15, 9.9805683e-01, 7.4160178e-10, 6.1433221e-04, 2.4728529e-16, 1.3216519e-03, 4.8570981e-22, 2.7355423e-10, 1.1303254e-15], dtype=float32)

What does this array represent?

This array represents the model’s “confidence” that this image is correlated to the t-shirt/top, trouser, pullover, dress, coat, sandal, shirt, sneaker, bag, and ankle boot. For further clarification, the first value in this array represents the model’s confidence that this image is a t-shirt/top. Additionally, the last value in this array represents the model’s confidence that this image is an ankle boot.

How were the Softmax() and argmax() functions applied?

The Softmax() function was used to calculate the probabilities of images corresponding to each of the different articles of clothing. The argmax() function was used to infer what article of clothing an image corresponds to based on the image having the highest probability of belonging in this class of clothing.

Does the output from np.argmax() match the label from your test_labels dataset?

Yes, the output from np.argmax() does match the label from my test_labels dataset. Due to np.argmax(predictions[1]) and test_labels[1] both equaling 2, this image has been correctly classified as a pullover.

Under the Verify predictions section, plot two additional images (other than either of the two given in the example script) and include the graph of their predicted label as well as the image itself.

-1st image

-2nd image

Under the Use the trained model section, again select a new image from the test dataset. Produce the predictions for this newly selected image. Does the predicted value match the test label? Although you applied the argmax() function in this second instance, you did not use Softmax() a second time. Why is that so (please be specific)?

-Predictions

The predicted value does match test label, because both values are 7. Based on this information, this image definitely consists of sneakers.

Also, I did not use Softmax() a second time, because I am already referencing it through the predictions_single variable. To clarify, the predictions_single variable references the probability_model variable. Furthermore, the probability_model variable references the Softmax() function.

probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])

predictions_single = probability_model.predict(img)

Produce a plot of 25 handwritten numbers from the data with their labels indicated below each image. Fit the model and report the accuracy of the training dataset. Likewise report the accuracy of the test dataset. As in the above example, from the Verify predictions section, plot two images and include the graph of their predicted label as well as the image itself.

-Plot

-Accuracy

-Verify

Evaluate how your model for the MNIST dataset compared with your model of the Fashion_MNIST dataset. Which of the two models is more accurate? Why do you think this is so?

My model for the MNIST dataset had a testing accuracy of 0.9790999889373779 and a training accuracy of 0.9973166584968567. For my model of the MNIST dataset, the difference between the values of accuracy is 0.01821666955. Also, my model for the Fashion_MNIST dataset had a testing accuracy of 0.8863000273704529 and a training accuracy of 0.918666660785675. For my model of the Fashion_MNIST dataset, the difference between the values of accuracy is 0.03236663341. My model for the MNIST dataset is more accurate, because it has a higher testing accuracy than the other model. By having a higher testing accuracy, my model for the MNIST dataset is able to more accurately predict the numbers of handwritten numbers in new data. By having a lower testing accuracy, my model for the Fashion_MNIST dataset is less accurate in predicting articles of clothing in new data. Additionally, based on the differences between the values of accuracy, my model for the MNIST data set seems to be less overfit than my model for the Fashion_MNIST dataset.