getting started with neural networks figure training and validation accuracy the network begins to overfit after nine epochs let' train new network from scratch for nine epochs and then evaluate it on the test set listing ret raining model from scrat ch model models sequential(model add(layers dense( activation='relu'input_shape=( ,))model add(layers dense( activation='relu')model add(layers dense( activation='softmax')model compile(optimizer='rmsprop'loss='categorical_crossentropy'metrics=['accuracy']model fit(partial_x_trainpartial_y_trainepochs= batch_size= validation_data=(x_valy_val)results model evaluate(x_testone_hot_test_labelshere are the final resultsresults [ this approach reaches an accuracy of ~ with balanced binary classification problemthe accuracy reached by purely random classifier would be but in this case it' closer to %so the results seem pretty goodat least when compared to random baselineimport copy test_labels_copy copy copy(test_labelsnp random shuffle(test_labels_copyhits_array np array(test_labels=np array(test_labels_copyfloat(np sum(hits_array)len(test_labels licensed to