def fit(self, X, y):
with tf.Graph().as_default() as graph:
tf.set_random_seed(self.tf_random_seed)
self._global_step = tf.Variable(0, name="global_step", trainable=False)
# Setting up input and output placeholders.
input_shape = [None] + list(X.shape[1:])
self._inp = tf.placeholder(tf.float32, input_shape,
name="input")
self._out = tf.placeholder(tf.float32,
[None] if self.n_classes < 2 else [None, self.n_classes],
name="output")
# Create model's graph.
self._model_predictions, self._model_loss = self.model_fn(self._inp, self._out)
# Create data feeder, to sample inputs from dataset.
self._data_feeder = DataFeeder(X, y, self.n_classes, self.batch_size)
# Create trainer and augment graph with gradients and optimizer.
self._trainer = TensorFlowTrainer(self._model_loss,
self._global_step, self.optimizer, self.learning_rate)
self._session = tf.Session(self.tf_master)
# Initialize and train model.
self._trainer.initialize(self._session)
self._trainer.train(self._session,
self._data_feeder.get_feed_dict_fn(self._inp,
self._out), self.steps)
class TensorFlowEstimator(BaseEstimator):
"""Base class for all TensorFlow estimators.
Parameters:
model_fn: Model function, that takes input X, y tensors and outputs
prediction and loss tensors.
n_classes: Number of classes in the target.
tf_master: TensorFlow master. Empty string is default for local.
batch_size: Mini batch size.
steps: Number of steps to run over data.
optimizer: Optimizer name (or class), for example "SGD", "Adam",
"Adagrad".
learning_rate: Learning rate for optimizer.
tf_random_seed: Random seed for TensorFlow initializers.
Setting this value, allows consistency between reruns.
"""
def __init__(self, model_fn, n_classes, tf_master="", batch_size=32, steps=50, optimizer="SGD",
learning_rate=0.1, tf_random_seed=42):
self.n_classes = n_classes
self.tf_master = tf_master
self.batch_size = batch_size
self.steps = steps
self.optimizer = optimizer
self.learning_rate = learning_rate
self.tf_random_seed = tf_random_seed
self.model_fn = model_fn
def fit(self, X, y):
with tf.Graph().as_default() as graph:
tf.set_random_seed(self.tf_random_seed)
self._global_step = tf.Variable(0, name="global_step", trainable=False)
# Setting up input and output placeholders.
input_shape = [None] + list(X.shape[1:])
self._inp = tf.placeholder(tf.float32, input_shape,
name="input")
self._out = tf.placeholder(tf.float32,
[None] if self.n_classes < 2 else [None, self.n_classes],
name="output")
# Create model's graph.
self._model_predictions, self._model_loss = self.model_fn(self._inp, self._out)
# Create data feeder, to sample inputs from dataset.
self._data_feeder = DataFeeder(X, y, self.n_classes, self.batch_size)
# Create trainer and augment graph with gradients and optimizer.
self._trainer = TensorFlowTrainer(self._model_loss,
self._global_step, self.optimizer, self.learning_rate)
self._session = tf.Session(self.tf_master)
# Initialize and train model.
self._trainer.initialize(self._session)
self._trainer.train(self._session,
self._data_feeder.get_feed_dict_fn(self._inp,
self._out), self.steps)
def predict(self, X):
pred = self._session.run(self._model_predictions,
feed_dict={
self._inp.name: X
})
if self.n_classes < 2:
return pred
return pred.argmax(axis=1)