04_linreg_eager.py

""" Starter code for a simple regression example using eager execution.
Created by Akshay Agrawal (akshayka@cs.stanford.edu)
CS20: "TensorFlow for Deep Learning Research"
cs20.stanford.edu
Lecture 04
"""
import time

import tensorflow as tf
import tensorflow.contrib.eager as tfe
import matplotlib.pyplot as plt

import utils

DATA_FILE = 'data/birth_life_2010.txt'

# In order to use eager execution, `tfe.enable_eager_execution()` must be
# called at the very beginning of a TensorFlow program.
tfe.enable_eager_execution()

# Read the data into a dataset.
data, n_samples = utils.read_birth_life_data(DATA_FILE)
dataset = tf.data.Dataset.from_tensor_slices((data[:,0], data[:,1]))

# Create variables.
w = tfe.Variable(0.0)
b = tfe.Variable(0.0)

# Define the linear predictor.
def prediction(x):
  return x * w + b

# Define loss functions of the form: L(y, y_predicted)
def squared_loss(y, y_predicted):
  return (y - y_predicted) ** 2

def huber_loss(y, y_predicted, m=1.0):
  """Huber loss."""
  t = y - y_predicted
  # Note that enabling eager execution lets you use Python control flow and
  # specificy dynamic TensorFlow computations. Contrast this implementation
  # to the graph-construction one found in `utils`, which uses `tf.cond`.
  return t ** 2 if tf.abs(t) <= m else m * (2 * tf.abs(t) - m)

def train(loss_fn):
  """Train a regression model evaluated using `loss_fn`."""
  print('Training; loss function: ' + loss_fn.__name__)
  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

  # Define the function through which to differentiate.
  def loss_for_example(x, y):
    return loss_fn(y, prediction(x))

  # `grad_fn(x_i, y_i)` returns (1) the value of `loss_for_example`
  # evaluated at `x_i`, `y_i` and (2) the gradients of any variables used in
  # calculating it.
  grad_fn = tfe.implicit_value_and_gradients(loss_for_example)

  start = time.time()
  for epoch in range(100):
    total_loss = 0.0
    for x_i, y_i in tfe.Iterator(dataset):
      loss, gradients = grad_fn(x_i, y_i)
      # Take an optimization step and update variables.
      optimizer.apply_gradients(gradients)
      total_loss += loss
    if epoch % 10 == 0:
      print('Epoch {0}: {1}'.format(epoch, total_loss / n_samples))
  print('Took: %f seconds' % (time.time() - start))
  print('Eager execution exhibits significant overhead per operation. '
        'As you increase your batch size, the impact of the overhead will '
        'become less noticeable. Eager execution is under active development: '
        'expect performance to increase substantially in the near future!')

train(huber_loss)
plt.plot(data[:,0], data[:,1], 'bo')
# The `.numpy()` method of a tensor retrieves the NumPy array backing it.
# In future versions of eager, you won't need to call `.numpy()` and will
# instead be able to, in most cases, pass Tensors wherever NumPy arrays are
# expected.
plt.plot(data[:,0], data[:,0] * w.numpy() + b.numpy(), 'r',
         label="huber regression")
plt.legend()
plt.show()