def train(output_dir,
mnist_location=constants.MNIST_LOCATION,
training_len=constants.TRAINING_LEN,
masks=None,
presets=None,
train_order_seed=None):
"""Train the MNIST model, possibly with presets and masks.
Args:
output_dir: The directory to which to write model logs and output.
mnist_location: The location of the MNIST numpy npz file.
training_len: How long to run the model. A tuple of two values. The first
value is the unit of measure (either "epochs" or "iterations") and the
second is the number of units for which to train.
masks: The masks, if any, used to prune weights. Masks can come in
one of four forms:
* A dictionary of numpy arrays. Each dictionary key is the name of the
corresponding tensor that is to be masked out. Each value is a numpy
array containing the masks (1 for including a weight, 0 for excluding).
* The string name of a directory containing one file for each
mask (in the form of bedrock.save_restore).
* A list of strings paths and dictionaries representing several masks.
The mask used for training is the union of the pruned networks
represented by these masks.
* None, meaning the network should not be pruned.
presets: The initial weights for the network, if any. Presets can come in
any of the non-list forms mentioned for masks; each numpy array
stores the desired initializations.
train_order_seed: The random seed, if any, to be used to determine the
order in which training examples are shuffled before being presented
to the network.
"""
# Retrieve previous information, if any.
masks = save_restore.standardize(masks, union.union)
presets = save_restore.standardize(presets)
# Create the dataset and model.
dataset = dataset_mnist.Dataset(mnist_location,
train_order_seed=train_order_seed)
inputs, labels = dataset.placeholders
model = model_fc.ModelFc(constants.HYPERPARAMETERS,
inputs,
labels,
presets=presets,
masks=masks)
# Train.
params = {
'test_interval': 100,
'save_summaries': True,
'save_network': True
}
trainer.train(tf.Session(), dataset, model, constants.OPTIMIZER_FN,
training_len, output_dir, **params)
def train_once(self, iteration, presets=None, masks=None):
tf.reset_default_graph()
sess = tf.Session()
dataset = dataset_mnist.ConstructedDatasetMnist(
train_len=self.train_len)
input_tensor, label_tensor = dataset.placeholders
hyperparameters = {
'layers': [(300, tf.nn.relu), (100, tf.nn.relu), (10, None)]
}
model = model_fc.ModelFc(hyperparameters,
input_tensor,
label_tensor,
presets=presets,
masks=masks)
params = {
'test_interval': 100,
'save_summaries': True,
'save_network': True,
}
return trainer.train(sess,
dataset,
model,
functools.partial(
tf.train.GradientDescentOptimizer, .1),
('iterations', 50000),
output_dir=paths.run(self.output_dir, iteration),
**params)
def train_model(sess, level, dataset, model):
params = {
'test_interval': 100,
'save_summaries': True,
'save_network': True,
}
return trainer.train(
sess,
dataset,
model,
constants.OPTIMIZER_FN,
training_len,
output_dir=paths.run(output_dir, level, experiment_name),
**params)
