Merge remote-tracking branch 'remotes/origin/master' into BP (e98ccc8b) · Commits · 钟慕尧 / deepchem

deepchem/feat/graph_features.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -173,7 +173,8 @@ def atom_features(atom, bool_id_feat=False, explicit_H=False):
		]) + [atom.GetIsAromatic()]
		# In case of explicit hydrogen(QM8, QM9), avoid calling `GetTotalNumHs`
		if not explicit_H:
		results = results + one_of_k_encoding_unk(atom.GetTotalNumHs(), [0, 1, 2, 3, 4])
		results = results + one_of_k_encoding_unk(atom.GetTotalNumHs(),
		[0, 1, 2, 3, 4])

		return np.array(results)

+1 −1

Original line number	Diff line number	Diff line
		@@ -309,7 +309,7 @@ class WeaveGather(Layer):
		(0.228, 0.114), (0.468, 0.118), (0.739, 0.134),
		(1.080, 0.170), (1.645, 0.283)]
		dist = [
		tf.contrib.distributions.Normal(loc=p[0], scale=p[1])
		tf.contrib.distributions.Normal(p[0], p[1])
		for p in gaussian_memberships
		]
		dist_max = [dist[i].prob(gaussian_memberships[i][0]) for i in range(11)]

+3 −3

Original line number	Diff line number	Diff line
		@@ -171,8 +171,8 @@ class WeaveLayer(Layer):
		tf.stack([atom_features] * max_atoms, axis=1)
		], 3)
		AP_combine_t = tf.transpose(AP_combine, perm=[0, 2, 1, 3])
		AP = tf.tensordot(AP_combine + AP_combine_t, self.W_AP,
		[[3], [0]]) + self.b_AP
		AP = tf.tensordot(AP_combine + AP_combine_t, self.W_AP, [[3], [0]
		]) + self.b_AP
		AP = self.activation(AP)
		PP = tf.tensordot(pair_features, self.W_PP, [[3], [0]]) + self.b_PP
		PP = self.activation(PP)
		@@ -413,7 +413,7 @@ class WeaveGather(Layer):
		(0.228, 0.114), (0.468, 0.118), (0.739, 0.134),
		(1.080, 0.170), (1.645, 0.283)]
		dist = [
		tf.contrib.distributions.Normal(loc=p[0], scale=p[1])
		tf.contrib.distributions.Normal(p[0], p[1])
		for p in gaussian_memberships
		]
		dist_max = [dist[i].prob(gaussian_memberships[i][0]) for i in range(11)]