def fit(model, dataset, optimizer, verbose=False, logdir=None):
"""Fit the linear-regression model.
Args:
model: The LinearModel to fit.
dataset: The tf.data.Dataset to use for training data.
optimizer: The TensorFlow Optimizer object to be used.
verbose: If true, will print out loss values at every iteration.
logdir: The directory in which summaries will be written for TensorBoard
(optional).
"""
# The loss function to optimize.
mse = lambda xs, ys: mean_square_loss(model, xs, ys)
loss_and_grads = tfe.implicit_value_and_gradients(mse)
if logdir:
# Support for TensorBoard summaries. Once training has started, use:
# tensorboard --logdir=<logdir>
summary_writer = tf.contrib.summary.create_file_writer(logdir)
# Training loop.
for i, (xs, ys) in enumerate(tfe.Iterator(dataset)):
loss, grads = loss_and_grads(xs, ys)
if verbose:
print("Iteration %d: loss = %s" % (i, loss.numpy()))
optimizer.apply_gradients(grads)
if logdir:
with summary_writer.as_default():
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar("loss", loss, step=i)
tf.contrib.summary.scalar("step", i, step=i)
def testSyntheticDataset(self):
true_w = tf.random_uniform([3, 1])
true_b = [1.0]
batch_size = 10
num_batches = 2
noise_level = 0.
dataset = linear_regression.synthetic_dataset(true_w, true_b, noise_level,
batch_size, num_batches)
it = tfe.Iterator(dataset)
for _ in range(2):
(xs, ys) = it.next()
self.assertEqual((batch_size, 3), xs.shape)
self.assertEqual((batch_size, 1), ys.shape)
self.assertEqual(tf.float32, xs.dtype)
self.assertEqual(tf.float32, ys.dtype)
with self.assertRaises(StopIteration):
it.next()
def make_iterator(tensors):
with tf.device('/device:CPU:0'):
ds = tf.data.Dataset.from_tensors(tensors).repeat()
return tfe.Iterator(ds)