<reponame>waynewolf/abucket
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 sets
x_train = np.array([1., 2., 3., 4.])
y_train = np.array([0., -1., -2., -3.])
x_eval = np.array([2., 5., 8., 1.])
y_eval = np.array([-1.01, -4.1, -7, 0.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x_train}, y_train, 4, num_epochs=1000)
eval_input_fn = tf.contrib.learn.io.numpy_input_fn(
    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000)
    
# train
estimator.fit(input_fn=input_fn, steps=1000)
# Here we evaluate how well our model did. 
train_loss = estimator.evaluate(input_fn=input_fn)
eval_loss = estimator.evaluate(input_fn=eval_input_fn)
print("train loss: %r"% train_loss)
print("eval loss: %r"% eval_loss)

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 sets
x_train = np.array([1., 2., 3., 4.])
y_train = np.array([0., -1., -2., -3.])
x_eval = np.array([2., 5., 8., 1.])
y_eval = np.array([-1.01, -4.1, -7, 0.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x_train}, y_train, 4, num_epochs=1000)
eval_input_fn = tf.contrib.learn.io.numpy_input_fn(
    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000)
    
# train
estimator.fit(input_fn=input_fn, steps=1000)
# Here we evaluate how well our model did. 
train_loss = estimator.evaluate(input_fn=input_fn)
eval_loss = estimator.evaluate(input_fn=eval_input_fn)
print("train loss: %r"% train_loss)
print("eval loss: %r"% eval_loss)
# Declare list of features, we only have one real-valued feature # Build a linear model and predict values # Loss sub-graph # Training sub-graph # ModelFnOps connects subgraphs we built to the # appropriate functionality. # define our data sets # train # Here we evaluate how well our model did.