debug and update (3ab4460a) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/symmetry_function_regression.py

+3 −2

Original line number	Diff line number	Diff line
		@@ -18,7 +18,7 @@ from deepchem.models.tensorgraph.symmetry_functions import DistanceMatrix, \

		class BPSymmetryFunctionRegression(TensorGraph):

		def __init__(self, n_tasks, max_atoms, n_hidden=10, **kwargs):
		def __init__(self, n_tasks, max_atoms, n_hidden=10, n_embedding=10, **kwargs):
		"""
		Parameters
		----------
		@@ -32,6 +32,7 @@ class BPSymmetryFunctionRegression(TensorGraph):
		self.n_tasks = n_tasks
		self.max_atoms = max_atoms
		self.n_hidden = n_hidden
		self.n_embedding = n_embedding
		super(BPSymmetryFunctionRegression, self).__init__(**kwargs)
		self.build_graph()

		@@ -51,7 +52,7 @@ class BPSymmetryFunctionRegression(TensorGraph):
		angular_symmetry = AngularSymmetry(
		self.max_atoms,
		in_layers=[distance_cutoff, distance_matrix, self.atom_coordinates])
		atom_embedding = DTNNEmbedding(n_embedding=5, in_layers=[self.atom_numbers])
		atom_embedding = DTNNEmbedding(n_embedding=self.n_embedding, in_layers=[self.atom_numbers])

		feature_merge = BPFeatureMerge(
		self.max_atoms,

+5 −5

Original line number	Diff line number	Diff line
		@@ -99,7 +99,7 @@ class RadialSymmetry(Layer):
		def build(self):
		""" Parameters for the Gaussian """
		self.Rs = tf.Variable(tf.constant(0.))
		#self.ita = tf.Variable(tf.constant(1.))
		#self.ita = tf.exp(tf.Variable(tf.constant(0.)))
		self.ita = 1.

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		@@ -124,10 +124,10 @@ class AngularSymmetry(Layer):

		def build(self):
		#self.lambd = tf.Variable(tf.constant(1.))
		#self.ita = tf.Variable(tf.constant(1.))
		#self.zeta = tf.Variable(tf.constant(1.))
		self.lambd = 1.
		#self.ita = tf.exp(tf.Variable(tf.constant(0.)))
		self.ita = 1.
		#self.zeta = tf.Variable(tf.constant(0.8))
		self.zeta = 0.8

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		@@ -154,7 +154,7 @@ class AngularSymmetry(Layer):
		theta = tf.reduce_sum(tf.tile(tf.expand_dims(vector_distances, axis=3), (1,1,1,max_atoms,1)) * \
		tf.tile(tf.expand_dims(vector_distances, axis=2), (1,1,max_atoms,1,1)), axis=4)

		theta = tf.div(theta, R_ij * R_ik)
		theta = tf.div(theta, R_ij * R_ik + 1e-5)

		out_tensor = tf.pow(1+self.lambdtf.cos(theta), self.zeta) \
		tf.exp(-self.ita(tf.square(R_ij)+tf.square(R_ik)+tf.square(R_jk))) \

+4 −2

Original line number	Diff line number	Diff line
		@@ -25,19 +25,21 @@ metric = [
		# Batch size of models
		max_atoms = 23
		n_hidden = 10
		n_embedding = 0
		batch_size = 16

		model = dc.models.BPSymmetryFunctionRegression(
		len(tasks),
		max_atoms,
		n_hidden=n_hidden,
		n_embedding=n_embedding,
		batch_size=batch_size,
		learning_rate=0.001,
		learning_rate=0.0001,
		use_queue=False,
		mode="regression")

		# Fit trained model
		model.fit(train_dataset, nb_epoch=20)
		model.fit(train_dataset, nb_epoch=200, checkpoint_interval=100)

		print("Evaluating model")
		train_scores = model.evaluate(train_dataset, metric, transformers)