# Learning rate tested with 0.001, 0.005 and 0.01 over 300 epochs for Table VII sgd = optimizers.SGD(learning_rate=0.005, decay=1e-6, momentum=0.9, nesterov=True) model.compile( optimizer=sgd, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) # Batch size of 128, 256 & 512 tested for Modification 2 # Batch size set to 128 for Modification 3 and 4 history = model.fit(train_images_cifar, train_labels_cifar, batch_size=128, epochs=200, validation_data=(test_images_cifar, test_labels_cifar)) plt.plot(history.history["accuracy"]) plt.plot(history.history['val_accuracy']) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title("model accuracy") plt.ylabel("Accuracy") plt.xlabel("Epoch") plt.legend(["Accuracy", "Validation Accuracy", "loss", "Validation Loss"]) plt.show()
# print(train_images_mnist.shape) # print(test_images_mnist) print('test_images_mnist:', test_images_mnist.shape) print('test_labels_mnist:', test_labels_mnist.shape) sgd = optimizers.SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile( optimizer=sgd, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) history = model.fit(train_images_mnist, train_labels_mnist, batch_size=256, epochs=200, validation_data=(test_images_mnist, test_labels_mnist)) plt.plot(history.history["accuracy"]) plt.plot(history.history['val_accuracy']) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title("model accuracy") plt.ylabel("Accuracy") plt.xlabel("Epoch") plt.legend(["Accuracy", "Validation Accuracy", "loss", "Validation Loss"]) plt.show()