Loading deepchem/models/tensorgraph/models/symmetry_function_regression.py +13 −41 Original line number Diff line number Diff line Loading @@ -118,9 +118,9 @@ class ANIRegression(TensorGraph): def __init__(self, n_tasks, max_atoms, n_feat=1120, n_hidden=40, n_embedding=10, atom_number_cases=[1, 6, 7, 8], atom_number_cases=[1, 6, 7, 8, 16], **kwargs): """ Parameters Loading @@ -135,7 +135,7 @@ class ANIRegression(TensorGraph): self.n_tasks = n_tasks self.max_atoms = max_atoms self.n_hidden = n_hidden self.n_embedding = n_embedding self.n_feat = n_feat self.atom_number_cases = atom_number_cases super(ANIRegression, self).__init__(**kwargs) self.build_graph() Loading @@ -143,43 +143,14 @@ class ANIRegression(TensorGraph): def build_graph(self): self.atom_numbers = Feature(shape=(None, self.max_atoms), dtype=tf.int32) self.atom_flags = Feature(shape=(None, self.max_atoms, self.max_atoms)) self.atom_coordinates = Feature(shape=(None, self.max_atoms, 3)) distance_matrix = DistanceMatrix( self.max_atoms, in_layers=[self.atom_coordinates, self.atom_flags]) distance_cutoff_radial = DistanceCutoff( self.max_atoms, cutoff=4.6 / 0.52917721092, in_layers=[distance_matrix, self.atom_flags]) distance_cutoff_angular = DistanceCutoff( self.max_atoms, cutoff=3.1 / 0.52917721092, in_layers=[distance_matrix, self.atom_flags]) radial_symmetry = RadialSymmetry( self.max_atoms, atomic_number_differentiated=True, atom_numbers=self.atom_number_cases, in_layers=[distance_cutoff_radial, distance_matrix, self.atom_numbers]) angular_symmetry = AngularSymmetryMod( self.max_atoms, atomic_number_differentiated=True, atom_numbers=self.atom_number_cases, in_layers=[distance_cutoff_angular, distance_matrix, self.atom_coordinates, self.atom_numbers]) atom_embedding = DTNNEmbedding( n_embedding=0, in_layers=[self.atom_numbers]) feature_merge = BPFeatureMerge( self.max_atoms, in_layers=[ atom_embedding, radial_symmetry, angular_symmetry, self.atom_flags ]) self.atom_feats = Feature(shape=(None, self.max_atoms, self.n_feat)) Hidden = AtomicDifferentiatedDense( self.max_atoms, self.n_hidden, self.atom_number_cases, activation='tanh', in_layers=[feature_merge, self.atom_numbers]) in_layers=[self.atom_feats, self.atom_numbers]) Hidden2 = AtomicDifferentiatedDense( self.max_atoms, Loading Loading @@ -212,6 +183,7 @@ class ANIRegression(TensorGraph): predict=False, pad_batches=True): for epoch in range(epochs): print('Starting epoch %i' % epoch) for (X_b, y_b, w_b, ids_b) in dataset.iterbatches( batch_size=self.batch_size, deterministic=True, Loading @@ -228,5 +200,5 @@ class ANIRegression(TensorGraph): feed_dict[self.atom_flags] = np.stack([flags]*self.max_atoms, axis=2)*\ np.stack([flags]*self.max_atoms, axis=1) feed_dict[self.atom_numbers] = np.array(X_b[:, :, 0], dtype=int) feed_dict[self.atom_coordinates] = np.array(X_b[:, :, 1:], dtype=float) feed_dict[self.atom_feats] = np.array(X_b[:, :, 1:], dtype=float) yield feed_dict No newline at end of file deepchem/trans/__init__.py +2 −1 Original line number Diff line number Diff line Loading @@ -16,3 +16,4 @@ from deepchem.trans.transformers import PowerTransformer from deepchem.trans.transformers import CoulombFitTransformer from deepchem.trans.transformers import IRVTransformer from deepchem.trans.transformers import DAGTransformer from deepchem.trans.transformers import ANITransformer deepchem/trans/transformers.py +196 −19 Original line number Diff line number Diff line Loading @@ -925,3 +925,180 @@ class ImageTransformer(Transformer): images = [scipy.ndimage.imread(x, mode='RGB') for x in X] images = [scipy.misc.imresize(x, size=self.size) for x in images] return np.array(images), y, w class ANITransformer(Transformer): """Performs transform from 3D coordinates to ANI symmetry functions """ def __init__(self, max_atoms=23, radial_cutoff=4.6, angular_cutoff=3.1, radial_length=32, angular_length=8, atom_cases=[1, 6, 7, 8, 16], coordinates_in_bohr=True, transform_X=True, transform_y=False, transform_w=False): """ Only X can be transformed """ self.max_atoms = max_atoms self.radial_cutoff = radial_cutoff self.angular_cutoff = angular_cutoff self.radial_length = radial_length self.angular_length = angular_length self.atom_cases = atom_cases self.coordinates_in_bohr = coordinates_in_bohr self.transform_X = transform_X self.transform_y = transform_y self.transform_w = transform_w self.compute_graph = self.build() assert self.transform_X assert not self.transform_y assert not self.transform_w def transform_array(self, X, y, w): if self.transform_X: n_samples = X.shape[0] batches = np.linspace(0, n_samples, int(n_samples/100)).astype(int) X_out = [] sess = tf.Session(graph=self.compute_graph) num_transformed = 0 for i, start in enumerate(batches[:-1]): if start == batches[-1]: X_batch = X[start:] else: X_batch = X[start:batches[i+1]] output = sess.run([self.outputs], feed_dict={self.inputs: X_batch})[0] X_out.append(output) num_transformed = num_transformed + X_batch.shape[0] print('%i samples transformed' % num_transformed) X_new = np.concatenate(X_out, axis=0) assert X_new.shape[0] == X.shape[0] return (X_new, y, w) def untransform(self, z): raise NotImplementedError( "Cannot untransform datasets with ANITransformer.") def build(self): """ tensorflow computation graph for transform """ graph = tf.Graph() with graph.as_default(): self.inputs = tf.placeholder(tf.float32, shape=(None, self.max_atoms, 4)) atom_numbers = tf.cast(self.inputs[:, :, 0], tf.int32) flags = tf.sign(atom_numbers) flags = tf.to_float(tf.expand_dims(flags, 1)*tf.expand_dims(flags, 2)) coordinates = self.inputs[:, :, 1:] if self.coordinates_in_bohr: coordinates = coordinates * 0.52917721092 d = self.distance_matrix(coordinates, flags) d_radial_cutoff = self.distance_cutoff(d, self.radial_cutoff, flags) d_angular_cutoff = self.distance_cutoff(d, self.angular_cutoff, flags) radial_sym = self.radial_symmetry(d_radial_cutoff, d, atom_numbers) angular_sym = self.angular_symmetry(d_angular_cutoff, d, atom_numbers, coordinates) self.outputs = tf.concat([tf.to_float(tf.expand_dims(atom_numbers, 2)), radial_sym, angular_sym], axis=2) return graph def distance_matrix(self, coordinates, flags): """ Generate distance matrix """ max_atoms = self.max_atoms tensor1 = tf.stack([coordinates]*max_atoms, axis=1) tensor2 = tf.stack([coordinates]*max_atoms, axis=2) # Calculate pairwise distance d = tf.sqrt(tf.reduce_sum(tf.square(tensor1 - tensor2), axis=3)) # Masking for valid atom index d = d * flags return d def distance_cutoff(self, d, cutoff, flags): """ Generate distance matrix with trainable cutoff """ # Cutoff with threshold Rc d_flag = flags * tf.sign(cutoff - d) d_flag = tf.nn.relu(d_flag) d_flag = d_flag * tf.expand_dims((1 - tf.eye(self.max_atoms)), 0) d = 0.5 * (tf.cos(np.pi * d / cutoff) + 1) return d * d_flag def radial_symmetry(self, d_cutoff, d, atom_numbers): """ Radial Symmetry Function """ embedding = tf.eye(np.max(self.atom_cases)+1) atom_numbers_embedded = tf.nn.embedding_lookup(embedding, atom_numbers) d_cutoff = tf.stack([d_cutoff] * self.radial_length, axis=3) d = tf.stack([d] * self.radial_length, axis=3) Rs = np.linspace(0., self.radial_cutoff, self.radial_length) ita = np.ones_like(Rs) * 1.25 / (Rs[1] - Rs[0])**2 Rs = tf.to_float(np.reshape(Rs, (1, 1, 1, -1))) ita = tf.to_float(np.reshape(ita, (1, 1, 1, -1))) out = tf.exp(-ita * tf.square(d - Rs)) * d_cutoff out_tensors = [] for atom_type in self.atom_cases: selected_atoms = tf.expand_dims( tf.expand_dims(atom_numbers_embedded[:, :, atom_type], axis=1), axis=3) out_tensors.append(tf.reduce_sum(out * selected_atoms, axis=2)) return tf.concat(out_tensors, axis=2) def angular_symmetry(self, d_cutoff, d, atom_numbers, coordinates): """ Angular Symmetry Function """ max_atoms = self.max_atoms embedding = tf.eye(np.max(self.atom_cases)+1) atom_numbers_embedded = tf.nn.embedding_lookup(embedding, atom_numbers) Rs = np.linspace(0., self.angular_cutoff, self.angular_length) ita = 1.25 / (Rs[1] - Rs[0])**2 thetas = np.linspace(0., np.pi, self.angular_length) zeta = float(self.angular_length**2) ita, zeta, Rs, thetas = np.meshgrid(ita, zeta, Rs, thetas) zeta = tf.to_float(np.reshape(zeta, (1, 1, 1, 1, -1))) ita = tf.to_float(np.reshape(ita, (1, 1, 1, 1, -1))) Rs = tf.to_float(np.reshape(Rs, (1, 1, 1, 1, -1))) thetas = tf.to_float(np.reshape(thetas, (1, 1, 1, 1, -1))) length = zeta.get_shape().as_list()[-1] vector_distances = tf.stack([coordinates]*max_atoms, 1) - tf.stack([coordinates]*max_atoms, 2) R_ij = tf.stack([d]*max_atoms, axis=3) R_ik = tf.stack([d]*max_atoms, axis=2) f_R_ij = tf.stack([d_cutoff]*max_atoms, axis=3) f_R_ik = tf.stack([d_cutoff]*max_atoms, axis=2) # Define angle theta = arccos(R_ij(Vector) dot R_ik(Vector)/R_ij(distance)/R_ik(distance)) vector_mul = tf.reduce_sum(tf.stack([vector_distances]*max_atoms, axis=3) * \ tf.stack([vector_distances]*max_atoms, axis=2), axis=4) vector_mul = vector_mul * tf.sign(f_R_ij) * tf.sign(f_R_ik) theta = tf.acos(tf.div(vector_mul, R_ij * R_ik + 1e-5)) R_ij = tf.stack([R_ij] * length, axis=4) R_ik = tf.stack([R_ik] * length, axis=4) f_R_ij = tf.stack([f_R_ij] * length, axis=4) f_R_ik = tf.stack([f_R_ik] * length, axis=4) theta = tf.stack([theta] * length, axis=4) out_tensor = tf.pow((1. + tf.cos(theta - thetas))/2., zeta) * \ tf.exp(-ita * tf.square((R_ij + R_ik)/2. - Rs)) * f_R_ij * f_R_ik out_tensors = [] for id_j, atom_type_j in enumerate(self.atom_cases): for atom_type_k in self.atom_cases[id_j:]: selected_atoms = tf.stack([atom_numbers_embedded[:, :, atom_type_j]] * max_atoms, axis=2) * \ tf.stack([atom_numbers_embedded[:, :, atom_type_k]] * max_atoms, axis=1) selected_atoms = tf.expand_dims( tf.expand_dims(selected_atoms, axis=1), axis=4) out_tensors.append( tf.reduce_sum(out_tensor * selected_atoms, axis=(2, 3))) return tf.concat(out_tensors, axis=2) def get_num_feats(self): n_feat = self.outputs.get_shape().as_list()[-1] return n_feat No newline at end of file examples/qm7/qm7_ANI.py +18 −8 Original line number Diff line number Diff line Loading @@ -16,12 +16,6 @@ tasks, datasets, transformers = dc.molnet.load_qm7_from_mat( featurizer='BPSymmetryFunction') train_dataset, valid_dataset, test_dataset = datasets # Fit models metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] # Batch size of models max_atoms = 23 n_hidden = 40 Loading @@ -29,11 +23,27 @@ n_embedding = 0 batch_size = 16 atom_number_cases = [1, 6, 7, 8, 16] ANItransformer = dc.trans.ANITransformer(max_atoms=max_atoms, atom_cases=atom_number_cases) train_dataset = ANItransformer.transform(train_dataset) valid_dataset = ANItransformer.transform(valid_dataset) test_dataset = ANItransformer.transform(test_dataset) # The first column is atom numbers n_feat = ANItransformer.get_num_feats() - 1 # Fit models metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] model = dc.models.ANIRegression( len(tasks), max_atoms, n_feat=n_feat, n_hidden=n_hidden, n_embedding=n_embedding, atom_number_cases=atom_number_cases, batch_size=batch_size, learning_rate=0.001, Loading @@ -41,7 +51,7 @@ model = dc.models.ANIRegression( mode="regression") # Fit trained model model.fit(train_dataset, nb_epoch=20, checkpoint_interval=10) model.fit(train_dataset, nb_epoch=100, checkpoint_interval=100) print("Evaluating model") train_scores = model.evaluate(train_dataset, metric, transformers) Loading Loading
deepchem/models/tensorgraph/models/symmetry_function_regression.py +13 −41 Original line number Diff line number Diff line Loading @@ -118,9 +118,9 @@ class ANIRegression(TensorGraph): def __init__(self, n_tasks, max_atoms, n_feat=1120, n_hidden=40, n_embedding=10, atom_number_cases=[1, 6, 7, 8], atom_number_cases=[1, 6, 7, 8, 16], **kwargs): """ Parameters Loading @@ -135,7 +135,7 @@ class ANIRegression(TensorGraph): self.n_tasks = n_tasks self.max_atoms = max_atoms self.n_hidden = n_hidden self.n_embedding = n_embedding self.n_feat = n_feat self.atom_number_cases = atom_number_cases super(ANIRegression, self).__init__(**kwargs) self.build_graph() Loading @@ -143,43 +143,14 @@ class ANIRegression(TensorGraph): def build_graph(self): self.atom_numbers = Feature(shape=(None, self.max_atoms), dtype=tf.int32) self.atom_flags = Feature(shape=(None, self.max_atoms, self.max_atoms)) self.atom_coordinates = Feature(shape=(None, self.max_atoms, 3)) distance_matrix = DistanceMatrix( self.max_atoms, in_layers=[self.atom_coordinates, self.atom_flags]) distance_cutoff_radial = DistanceCutoff( self.max_atoms, cutoff=4.6 / 0.52917721092, in_layers=[distance_matrix, self.atom_flags]) distance_cutoff_angular = DistanceCutoff( self.max_atoms, cutoff=3.1 / 0.52917721092, in_layers=[distance_matrix, self.atom_flags]) radial_symmetry = RadialSymmetry( self.max_atoms, atomic_number_differentiated=True, atom_numbers=self.atom_number_cases, in_layers=[distance_cutoff_radial, distance_matrix, self.atom_numbers]) angular_symmetry = AngularSymmetryMod( self.max_atoms, atomic_number_differentiated=True, atom_numbers=self.atom_number_cases, in_layers=[distance_cutoff_angular, distance_matrix, self.atom_coordinates, self.atom_numbers]) atom_embedding = DTNNEmbedding( n_embedding=0, in_layers=[self.atom_numbers]) feature_merge = BPFeatureMerge( self.max_atoms, in_layers=[ atom_embedding, radial_symmetry, angular_symmetry, self.atom_flags ]) self.atom_feats = Feature(shape=(None, self.max_atoms, self.n_feat)) Hidden = AtomicDifferentiatedDense( self.max_atoms, self.n_hidden, self.atom_number_cases, activation='tanh', in_layers=[feature_merge, self.atom_numbers]) in_layers=[self.atom_feats, self.atom_numbers]) Hidden2 = AtomicDifferentiatedDense( self.max_atoms, Loading Loading @@ -212,6 +183,7 @@ class ANIRegression(TensorGraph): predict=False, pad_batches=True): for epoch in range(epochs): print('Starting epoch %i' % epoch) for (X_b, y_b, w_b, ids_b) in dataset.iterbatches( batch_size=self.batch_size, deterministic=True, Loading @@ -228,5 +200,5 @@ class ANIRegression(TensorGraph): feed_dict[self.atom_flags] = np.stack([flags]*self.max_atoms, axis=2)*\ np.stack([flags]*self.max_atoms, axis=1) feed_dict[self.atom_numbers] = np.array(X_b[:, :, 0], dtype=int) feed_dict[self.atom_coordinates] = np.array(X_b[:, :, 1:], dtype=float) feed_dict[self.atom_feats] = np.array(X_b[:, :, 1:], dtype=float) yield feed_dict No newline at end of file
deepchem/trans/__init__.py +2 −1 Original line number Diff line number Diff line Loading @@ -16,3 +16,4 @@ from deepchem.trans.transformers import PowerTransformer from deepchem.trans.transformers import CoulombFitTransformer from deepchem.trans.transformers import IRVTransformer from deepchem.trans.transformers import DAGTransformer from deepchem.trans.transformers import ANITransformer
deepchem/trans/transformers.py +196 −19 Original line number Diff line number Diff line Loading @@ -925,3 +925,180 @@ class ImageTransformer(Transformer): images = [scipy.ndimage.imread(x, mode='RGB') for x in X] images = [scipy.misc.imresize(x, size=self.size) for x in images] return np.array(images), y, w class ANITransformer(Transformer): """Performs transform from 3D coordinates to ANI symmetry functions """ def __init__(self, max_atoms=23, radial_cutoff=4.6, angular_cutoff=3.1, radial_length=32, angular_length=8, atom_cases=[1, 6, 7, 8, 16], coordinates_in_bohr=True, transform_X=True, transform_y=False, transform_w=False): """ Only X can be transformed """ self.max_atoms = max_atoms self.radial_cutoff = radial_cutoff self.angular_cutoff = angular_cutoff self.radial_length = radial_length self.angular_length = angular_length self.atom_cases = atom_cases self.coordinates_in_bohr = coordinates_in_bohr self.transform_X = transform_X self.transform_y = transform_y self.transform_w = transform_w self.compute_graph = self.build() assert self.transform_X assert not self.transform_y assert not self.transform_w def transform_array(self, X, y, w): if self.transform_X: n_samples = X.shape[0] batches = np.linspace(0, n_samples, int(n_samples/100)).astype(int) X_out = [] sess = tf.Session(graph=self.compute_graph) num_transformed = 0 for i, start in enumerate(batches[:-1]): if start == batches[-1]: X_batch = X[start:] else: X_batch = X[start:batches[i+1]] output = sess.run([self.outputs], feed_dict={self.inputs: X_batch})[0] X_out.append(output) num_transformed = num_transformed + X_batch.shape[0] print('%i samples transformed' % num_transformed) X_new = np.concatenate(X_out, axis=0) assert X_new.shape[0] == X.shape[0] return (X_new, y, w) def untransform(self, z): raise NotImplementedError( "Cannot untransform datasets with ANITransformer.") def build(self): """ tensorflow computation graph for transform """ graph = tf.Graph() with graph.as_default(): self.inputs = tf.placeholder(tf.float32, shape=(None, self.max_atoms, 4)) atom_numbers = tf.cast(self.inputs[:, :, 0], tf.int32) flags = tf.sign(atom_numbers) flags = tf.to_float(tf.expand_dims(flags, 1)*tf.expand_dims(flags, 2)) coordinates = self.inputs[:, :, 1:] if self.coordinates_in_bohr: coordinates = coordinates * 0.52917721092 d = self.distance_matrix(coordinates, flags) d_radial_cutoff = self.distance_cutoff(d, self.radial_cutoff, flags) d_angular_cutoff = self.distance_cutoff(d, self.angular_cutoff, flags) radial_sym = self.radial_symmetry(d_radial_cutoff, d, atom_numbers) angular_sym = self.angular_symmetry(d_angular_cutoff, d, atom_numbers, coordinates) self.outputs = tf.concat([tf.to_float(tf.expand_dims(atom_numbers, 2)), radial_sym, angular_sym], axis=2) return graph def distance_matrix(self, coordinates, flags): """ Generate distance matrix """ max_atoms = self.max_atoms tensor1 = tf.stack([coordinates]*max_atoms, axis=1) tensor2 = tf.stack([coordinates]*max_atoms, axis=2) # Calculate pairwise distance d = tf.sqrt(tf.reduce_sum(tf.square(tensor1 - tensor2), axis=3)) # Masking for valid atom index d = d * flags return d def distance_cutoff(self, d, cutoff, flags): """ Generate distance matrix with trainable cutoff """ # Cutoff with threshold Rc d_flag = flags * tf.sign(cutoff - d) d_flag = tf.nn.relu(d_flag) d_flag = d_flag * tf.expand_dims((1 - tf.eye(self.max_atoms)), 0) d = 0.5 * (tf.cos(np.pi * d / cutoff) + 1) return d * d_flag def radial_symmetry(self, d_cutoff, d, atom_numbers): """ Radial Symmetry Function """ embedding = tf.eye(np.max(self.atom_cases)+1) atom_numbers_embedded = tf.nn.embedding_lookup(embedding, atom_numbers) d_cutoff = tf.stack([d_cutoff] * self.radial_length, axis=3) d = tf.stack([d] * self.radial_length, axis=3) Rs = np.linspace(0., self.radial_cutoff, self.radial_length) ita = np.ones_like(Rs) * 1.25 / (Rs[1] - Rs[0])**2 Rs = tf.to_float(np.reshape(Rs, (1, 1, 1, -1))) ita = tf.to_float(np.reshape(ita, (1, 1, 1, -1))) out = tf.exp(-ita * tf.square(d - Rs)) * d_cutoff out_tensors = [] for atom_type in self.atom_cases: selected_atoms = tf.expand_dims( tf.expand_dims(atom_numbers_embedded[:, :, atom_type], axis=1), axis=3) out_tensors.append(tf.reduce_sum(out * selected_atoms, axis=2)) return tf.concat(out_tensors, axis=2) def angular_symmetry(self, d_cutoff, d, atom_numbers, coordinates): """ Angular Symmetry Function """ max_atoms = self.max_atoms embedding = tf.eye(np.max(self.atom_cases)+1) atom_numbers_embedded = tf.nn.embedding_lookup(embedding, atom_numbers) Rs = np.linspace(0., self.angular_cutoff, self.angular_length) ita = 1.25 / (Rs[1] - Rs[0])**2 thetas = np.linspace(0., np.pi, self.angular_length) zeta = float(self.angular_length**2) ita, zeta, Rs, thetas = np.meshgrid(ita, zeta, Rs, thetas) zeta = tf.to_float(np.reshape(zeta, (1, 1, 1, 1, -1))) ita = tf.to_float(np.reshape(ita, (1, 1, 1, 1, -1))) Rs = tf.to_float(np.reshape(Rs, (1, 1, 1, 1, -1))) thetas = tf.to_float(np.reshape(thetas, (1, 1, 1, 1, -1))) length = zeta.get_shape().as_list()[-1] vector_distances = tf.stack([coordinates]*max_atoms, 1) - tf.stack([coordinates]*max_atoms, 2) R_ij = tf.stack([d]*max_atoms, axis=3) R_ik = tf.stack([d]*max_atoms, axis=2) f_R_ij = tf.stack([d_cutoff]*max_atoms, axis=3) f_R_ik = tf.stack([d_cutoff]*max_atoms, axis=2) # Define angle theta = arccos(R_ij(Vector) dot R_ik(Vector)/R_ij(distance)/R_ik(distance)) vector_mul = tf.reduce_sum(tf.stack([vector_distances]*max_atoms, axis=3) * \ tf.stack([vector_distances]*max_atoms, axis=2), axis=4) vector_mul = vector_mul * tf.sign(f_R_ij) * tf.sign(f_R_ik) theta = tf.acos(tf.div(vector_mul, R_ij * R_ik + 1e-5)) R_ij = tf.stack([R_ij] * length, axis=4) R_ik = tf.stack([R_ik] * length, axis=4) f_R_ij = tf.stack([f_R_ij] * length, axis=4) f_R_ik = tf.stack([f_R_ik] * length, axis=4) theta = tf.stack([theta] * length, axis=4) out_tensor = tf.pow((1. + tf.cos(theta - thetas))/2., zeta) * \ tf.exp(-ita * tf.square((R_ij + R_ik)/2. - Rs)) * f_R_ij * f_R_ik out_tensors = [] for id_j, atom_type_j in enumerate(self.atom_cases): for atom_type_k in self.atom_cases[id_j:]: selected_atoms = tf.stack([atom_numbers_embedded[:, :, atom_type_j]] * max_atoms, axis=2) * \ tf.stack([atom_numbers_embedded[:, :, atom_type_k]] * max_atoms, axis=1) selected_atoms = tf.expand_dims( tf.expand_dims(selected_atoms, axis=1), axis=4) out_tensors.append( tf.reduce_sum(out_tensor * selected_atoms, axis=(2, 3))) return tf.concat(out_tensors, axis=2) def get_num_feats(self): n_feat = self.outputs.get_shape().as_list()[-1] return n_feat No newline at end of file
examples/qm7/qm7_ANI.py +18 −8 Original line number Diff line number Diff line Loading @@ -16,12 +16,6 @@ tasks, datasets, transformers = dc.molnet.load_qm7_from_mat( featurizer='BPSymmetryFunction') train_dataset, valid_dataset, test_dataset = datasets # Fit models metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] # Batch size of models max_atoms = 23 n_hidden = 40 Loading @@ -29,11 +23,27 @@ n_embedding = 0 batch_size = 16 atom_number_cases = [1, 6, 7, 8, 16] ANItransformer = dc.trans.ANITransformer(max_atoms=max_atoms, atom_cases=atom_number_cases) train_dataset = ANItransformer.transform(train_dataset) valid_dataset = ANItransformer.transform(valid_dataset) test_dataset = ANItransformer.transform(test_dataset) # The first column is atom numbers n_feat = ANItransformer.get_num_feats() - 1 # Fit models metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] model = dc.models.ANIRegression( len(tasks), max_atoms, n_feat=n_feat, n_hidden=n_hidden, n_embedding=n_embedding, atom_number_cases=atom_number_cases, batch_size=batch_size, learning_rate=0.001, Loading @@ -41,7 +51,7 @@ model = dc.models.ANIRegression( mode="regression") # Fit trained model model.fit(train_dataset, nb_epoch=20, checkpoint_interval=10) model.fit(train_dataset, nb_epoch=100, checkpoint_interval=100) print("Evaluating model") train_scores = model.evaluate(train_dataset, metric, transformers) Loading
