tensorflow的contrib.learn的基本用法
import tensorflow as tf # NumPy is often used to load, manipulate and preprocess data. import numpy as np # Declare list of features. We only have one real-valued feature. There are many # other types of columns that are more complicated and useful. features = [tf.contrib.layers.real_valued_column("x", dimension=1)] # An estimator is the front end to invoke training (fitting) and evaluation # (inference). There are many predefined types like linear regression, # logistic regression, linear classification, logistic classification, and # many neural network classifiers and regressors. The following code # provides an estimator that does linear regression. estimator = tf.contrib.learn.LinearRegressor(feature_columns=features) # TensorFlow provides many helper methods to read and set up data sets. # Here we use `numpy_input_fn`. We have to tell the function how many batches # of data (num_epochs) we want and how big each batch should be. x = np.array([1., 2., 3., 4.]) y = np.array([0., -1., -2., -3.]) input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x}, y, batch_size=4, num_epochs=1000) # We can invoke 1000 training steps by invoking the `fit` method and passing the # training data set. estimator.fit(input_fn=input_fn, steps=1000) # Here we evaluate how well our model did. In a real example, we would want # to use a separate validation and testing data set to avoid overfitting. print(estimator.evaluate(input_fn=input_fn))contrib的自定义模型
import numpy as np import tensorflow as tf # Declare list of features, we only have one real-valued feature def model(features, labels, mode): # Build a linear model and predict values W = tf.get_variable("W", [1], dtype=tf.float64) b = tf.get_variable("b", [1], dtype=tf.float64) y = W*features['x'] + b # Loss sub-graph loss = tf.reduce_sum(tf.square(y - labels)) # Training sub-graph global_step = tf.train.get_global_step() optimizer = tf.train.GradientDescentOptimizer(0.01) train = tf.group(optimizer.minimize(loss), tf.assign_add(global_step, 1)) # ModelFnOps connects subgraphs we built to the # appropriate functionality. return tf.contrib.learn.ModelFnOps( mode=mode, predictions=y, loss=loss, train_op=train) estimator = tf.contrib.learn.Estimator(model_fn=model) # define our data set x = np.array([1., 2., 3., 4.]) y = np.array([0., -1., -2., -3.]) input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, 4, num_epochs=1000) # train estimator.fit(input_fn=input_fn, steps=1000) # evaluate our model print(estimator.evaluate(input_fn=input_fn, steps=10))