implement input data normalization #3 (18477e53) · 提交 · AIOps-NanKai / model / DAGMM

dagmm/dagmm.py

+26 −1

原始行号	差异行号	差异行
		import tensorflow as tf
		from sklearn.preprocessing import StandardScaler
		from sklearn.externals import joblib

		from dagmm.compression_net import CompressionNet
		from dagmm.estimation_net import EstimationNet
		@@ -17,11 +19,13 @@ class DAGMM:
		"""

		MODEL_FILENAME = "DAGMM_model"
		SCALER_FILENAME = "DAGMM_scaler"

		def __init__(self, comp_hiddens, comp_activation,
		est_hiddens, est_activation, est_dropout_ratio=0.5,
		minibatch_size=1024, epoch_size=100,
		learning_rate=0.0001, lambda1=0.1, lambda2=0.005):
		learning_rate=0.0001, lambda1=0.1, lambda2=0.005,
		normalize=True):
		"""
		Parameters
		----------
		@@ -54,6 +58,9 @@ class DAGMM:
		lambda2 : float (optional)
		a parameter of loss function
		(for sum of diagonal elements of covariance)
		normalize : bool (optional)
		specify whether input data need to be normalized.
		by default, input data is normalized.
		"""
		self.comp_net = CompressionNet(comp_hiddens, comp_activation)
		self.est_net = EstimationNet(est_hiddens, est_activation)
		@@ -68,6 +75,9 @@ class DAGMM:
		self.lambda1 = lambda1
		self.lambda2 = lambda2

		self.normalize = normalize
		self.scaler = None

		self.graph = None
		self.sess = None

		@@ -85,6 +95,10 @@ class DAGMM:
		"""
		n_samples, n_features = x.shape

		if self.normalize:
		self.scaler = scaler = StandardScaler()
		x = scaler.fit_transform(x)

		with tf.Graph().as_default() as graph:
		self.graph = graph

		@@ -159,6 +173,9 @@ class DAGMM:
		if self.sess is None:
		raise Exception("Trained model does not exist.")

		if self.normalize:
		x = self.scaler.transform(x)

		energies = self.sess.run(self.energy, feed_dict={self.input:x})
		return energies

		@@ -182,6 +199,10 @@ class DAGMM:
		model_path = join(fdir, self.MODEL_FILENAME)
		self.saver.save(self.sess, model_path)

		if self.normalize:
		scaler_path = join(fdir, self.SCALER_FILENAME)
		joblib.dump(self.scaler, scaler_path)

		def restore(self, fdir):
		""" Restore trained model from designated directory.

		@@ -203,3 +224,7 @@ class DAGMM:
		self.saver.restore(self.sess, model_path)

		self.input, self.energy = tf.get_collection("save")

		if self.normalize:
		scaler_path = join(fdir, self.SCALER_FILENAME)
		self.scaler = joblib.load(scaler_path)