Some abstractions include 1. Estimator API 2. Keras 3. TF Learn 4. TF-Slim 5. Layers Most abstractions lie in the tf.contrib package Best abstractions are Estimators, Keras (Abstraction of tf and theano), Layers (Halfway on tf.contrib and rest on tf.layers) 1. Estimator API tf.estimator.* Contains DNNClassifier(), DNNRegressor() etc Needs a feature column: tf.feature_column.numeric_column() Then create a model: tf.estimator.DNNClassifier() Then an input function: tf.estimator.inputs.numpy/pandas_input_fn(); eval input fn shouldnt be shuffled Then train: tf.estimator.DNNClassifier.train() Then predict: tf.estimator.DNNClassifier.predict() 2. Keras API tf.contrib.keras 1. Make model object: tf.contrib.keras import models; model = models.Sequential() 2. Add the layers you want: tf.contrib.keras.layers; model.add(layers.Dense()) 3. Compie the model: model.compile(optimizer, loss, metrics=[]) # all params in strings 4. model.fit() 5. model.predict() for softmax values / model.predict_classes() for class values