Loading deepchem/models/tensorgraph/symmetry_functions.py +44 −9 Original line number Diff line number Diff line Loading @@ -46,7 +46,11 @@ class DistanceMatrix(Layer): # Calculate pairwise distance d = tf.sqrt(tf.reduce_sum(tf.square(tensor1 - tensor2), axis=3)) # Masking for valid atom index self.out_tensor = d * tf.to_float(atom_flags) out_tensor = d * tf.to_float(atom_flags) if set_tensors: self.out_tensor = out_tensor return out_tensor class DistanceCutoff(Layer): Loading Loading @@ -79,7 +83,11 @@ class DistanceCutoff(Layer): d = 0.5 * (tf.cos(np.pi * d / self.Rc) + 1) out_tensor = d * d_flag out_tensor = out_tensor * tf.expand_dims((1 - tf.eye(self.max_atoms)), 0) out_tensor = out_tensor if set_tensors: self.out_tensor = out_tensor return out_tensor class RadialSymmetry(Layer): Loading Loading @@ -142,9 +150,14 @@ class RadialSymmetry(Layer): tf.expand_dims(atom_number_embedded[:, :, atom_type], axis=1), axis=3) out_tensors.append(tf.reduce_sum(out_tensor * selected_atoms, axis=2)) self.out_tensor = tf.concat(out_tensors, axis=2) out_tensor = tf.concat(out_tensors, axis=2) else: self.out_tensor = tf.reduce_sum(out_tensor, axis=2) out_tensor = tf.reduce_sum(out_tensor, axis=2) if set_tensors: self.out_tensor = out_tensor return out_tensor class AngularSymmetry(Layer): Loading Loading @@ -227,9 +240,14 @@ class AngularSymmetry(Layer): out_tensor = tf.pow(1 + lambd * tf.cos(theta), zeta) * \ tf.exp(-ita * (tf.square(R_ij) + tf.square(R_ik) + tf.square(R_jk))) * \ f_R_ij * f_R_ik * f_R_jk self.out_tensor = tf.reduce_sum(out_tensor, axis=[2, 3]) * \ out_tensor = tf.reduce_sum(out_tensor, axis=[2, 3]) * \ tf.pow(tf.constant(2.), 1 - tf.reshape(self.zeta, (1, 1, -1))) if set_tensors: self.out_tensor = out_tensor return out_tensor class AngularSymmetryMod(Layer): """ Angular Symmetry Function """ Loading Loading @@ -345,9 +363,14 @@ class AngularSymmetryMod(Layer): tf.expand_dims(selected_atoms, axis=1), axis=4) out_tensors.append( tf.reduce_sum(out_tensor * selected_atoms, axis=[2, 3])) self.out_tensor = tf.concat(out_tensors, axis=2) out_tensor = tf.concat(out_tensors, axis=2) else: self.out_tensor = tf.reduce_sum(out_tensor, axis=[2, 3]) out_tensor = tf.reduce_sum(out_tensor, axis=[2, 3]) if set_tensors: self.out_tensor = out_tensor return out_tensor class BPFeatureMerge(Layer): Loading @@ -369,7 +392,12 @@ class BPFeatureMerge(Layer): out_tensor = tf.concat( [atom_embedding, radial_symmetry, angular_symmetry], axis=2) self.out_tensor = out_tensor * atom_flags[:, :, 0:1] out_tensor = out_tensor * atom_flags[:, :, 0:1] if set_tensors: self.out_tensor = out_tensor return out_tensor class BPGather(Layer): Loading @@ -390,6 +418,8 @@ class BPGather(Layer): out_tensor = tf.reduce_sum(out_tensor * flags[:, :, 0:1], axis=1) self.out_tensor = out_tensor return out_tensor class AtomicDifferentiatedDense(Layer): """ Separate Dense module for different atoms """ Loading Loading @@ -453,7 +483,12 @@ class AtomicDifferentiatedDense(Layer): output = tf.reshape(output * tf.expand_dims(mask, 2), (-1, self.max_atoms, self.out_channels)) outputs.append(output) self.out_tensor = tf.add_n(outputs) out_tensor = tf.add_n(outputs) if set_tensors: self.out_tensor = out_tensor return out_tensor def none_tensors(self): w, b, out_tensor = self.W, self.b, self.out_tensor Loading Loading
deepchem/models/tensorgraph/symmetry_functions.py +44 −9 Original line number Diff line number Diff line Loading @@ -46,7 +46,11 @@ class DistanceMatrix(Layer): # Calculate pairwise distance d = tf.sqrt(tf.reduce_sum(tf.square(tensor1 - tensor2), axis=3)) # Masking for valid atom index self.out_tensor = d * tf.to_float(atom_flags) out_tensor = d * tf.to_float(atom_flags) if set_tensors: self.out_tensor = out_tensor return out_tensor class DistanceCutoff(Layer): Loading Loading @@ -79,7 +83,11 @@ class DistanceCutoff(Layer): d = 0.5 * (tf.cos(np.pi * d / self.Rc) + 1) out_tensor = d * d_flag out_tensor = out_tensor * tf.expand_dims((1 - tf.eye(self.max_atoms)), 0) out_tensor = out_tensor if set_tensors: self.out_tensor = out_tensor return out_tensor class RadialSymmetry(Layer): Loading Loading @@ -142,9 +150,14 @@ class RadialSymmetry(Layer): tf.expand_dims(atom_number_embedded[:, :, atom_type], axis=1), axis=3) out_tensors.append(tf.reduce_sum(out_tensor * selected_atoms, axis=2)) self.out_tensor = tf.concat(out_tensors, axis=2) out_tensor = tf.concat(out_tensors, axis=2) else: self.out_tensor = tf.reduce_sum(out_tensor, axis=2) out_tensor = tf.reduce_sum(out_tensor, axis=2) if set_tensors: self.out_tensor = out_tensor return out_tensor class AngularSymmetry(Layer): Loading Loading @@ -227,9 +240,14 @@ class AngularSymmetry(Layer): out_tensor = tf.pow(1 + lambd * tf.cos(theta), zeta) * \ tf.exp(-ita * (tf.square(R_ij) + tf.square(R_ik) + tf.square(R_jk))) * \ f_R_ij * f_R_ik * f_R_jk self.out_tensor = tf.reduce_sum(out_tensor, axis=[2, 3]) * \ out_tensor = tf.reduce_sum(out_tensor, axis=[2, 3]) * \ tf.pow(tf.constant(2.), 1 - tf.reshape(self.zeta, (1, 1, -1))) if set_tensors: self.out_tensor = out_tensor return out_tensor class AngularSymmetryMod(Layer): """ Angular Symmetry Function """ Loading Loading @@ -345,9 +363,14 @@ class AngularSymmetryMod(Layer): tf.expand_dims(selected_atoms, axis=1), axis=4) out_tensors.append( tf.reduce_sum(out_tensor * selected_atoms, axis=[2, 3])) self.out_tensor = tf.concat(out_tensors, axis=2) out_tensor = tf.concat(out_tensors, axis=2) else: self.out_tensor = tf.reduce_sum(out_tensor, axis=[2, 3]) out_tensor = tf.reduce_sum(out_tensor, axis=[2, 3]) if set_tensors: self.out_tensor = out_tensor return out_tensor class BPFeatureMerge(Layer): Loading @@ -369,7 +392,12 @@ class BPFeatureMerge(Layer): out_tensor = tf.concat( [atom_embedding, radial_symmetry, angular_symmetry], axis=2) self.out_tensor = out_tensor * atom_flags[:, :, 0:1] out_tensor = out_tensor * atom_flags[:, :, 0:1] if set_tensors: self.out_tensor = out_tensor return out_tensor class BPGather(Layer): Loading @@ -390,6 +418,8 @@ class BPGather(Layer): out_tensor = tf.reduce_sum(out_tensor * flags[:, :, 0:1], axis=1) self.out_tensor = out_tensor return out_tensor class AtomicDifferentiatedDense(Layer): """ Separate Dense module for different atoms """ Loading Loading @@ -453,7 +483,12 @@ class AtomicDifferentiatedDense(Layer): output = tf.reshape(output * tf.expand_dims(mask, 2), (-1, self.max_atoms, self.out_channels)) outputs.append(output) self.out_tensor = tf.add_n(outputs) out_tensor = tf.add_n(outputs) if set_tensors: self.out_tensor = out_tensor return out_tensor def none_tensors(self): w, b, out_tensor = self.W, self.b, self.out_tensor Loading
