I have modeled a neural network with three layers using SGD optimization for classifying two categories. And now for class 0, the model has high precision but very low recall, while class 1 is the reverse. The total accuracy is about 80%. My training set size is about 200K and test set is about 100K. Size of class 0 to class 1, is about 1:2. I want to explore the mis-classified labels for class 0 in one batch to improve the accuracy. How should I do this task promisingly in TensorFlow? The best hyper-parameters I have now are:batch_sizes = [128]initial_learning_rates = [0.9]final_learning_rates = [0.03]regular_lambdas = [0.003]keep_probs = [1.0] # dropout did not work well.The arch of NN model is,h1_length = 256h2_length = h1_length//5h3_length = h2_length//5num_labels = 2Below is the metrics report of my model:        precision    recall  f1-score   support0       0.86      0.46      0.60     378631       0.79      0.97      0.87     79848avg / total       0.81      0.80      0.78    117711
how to trace, analyze and even solve the misclassified labels in tensorflow
neural network;classification;tensorflow