tensorflow的contrib的基本用法和自定义模型

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 npimport tensorflow as tf# Declare list of features, we only have one real-valued featuredef 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 setx = 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)# trainestimator.fit(input_fn=input_fn, steps=1000)# evaluate our modelprint(estimator.evaluate(input_fn=input_fn, steps=10))