Loading deepchem/models/tensorgraph/layers.py +43 −13 Original line number Diff line number Diff line Loading @@ -1375,6 +1375,36 @@ class Conv3D(Layer): self.out_tensor = out_tensor return out_tensor class MaxPool1D(Layer): def __init__(self, window_shape=2, strides=1, padding="SAME", **kwargs): self.window_shape = window_shape self.strides = strides self.padding = padding self.pooling_type = "MAX" super(MaxPool1D, self).__init__(**kwargs) try: parent_shape = self.in_layers[0].shape self._shape = tuple(None if p is None else p // s for p, s in zip(parent_shape, strides)) except: pass def create_tensor(self, in_layers=None, set_tensors=True, **kwargs): inputs = self._get_input_tensors(in_layers) in_tensor = inputs[0] out_tensor = tf.nn.pool(in_tensor, window_shape=[self.window_shape], pooling_type=self.pooling_type, padding=self.padding, strides=[self.strides]) if set_tensors: self.out_tensor = out_tensor return out_tensor class MaxPool2D(Layer): Loading deepchem/models/tensorgraph/models/text_cnn.py 0 → 100644 +229 −0 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Thu Sep 28 15:17:50 2017 @author: zqwu """ import numpy as np import tensorflow as tf import copy from deepchem.metrics import to_one_hot, from_one_hot from deepchem.models.tensorgraph.layers import Dense, Concat, SoftMax, \ SoftMaxCrossEntropy, BatchNorm, WeightedError, Dropout, BatchNormalization, \ Conv1D, MaxPool1D, Squeeze, Stack from deepchem.models.tensorgraph.graph_layers import DTNNEmbedding from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature from deepchem.models.tensorgraph.tensor_graph import TensorGraph from deepchem.trans import undo_transforms default_dict = { '#': 1, '(': 2, ')': 3, '+': 4, '-': 5, '/': 6, '1': 7, '2': 8, '3': 9, '4': 10, '5': 11, '6': 12, '7': 13, '8': 14, '=': 15, 'C': 16, 'F': 17, 'H': 18, 'I': 19, 'N': 20, 'O': 21, 'P': 22, 'S': 23, '[': 24, '\\': 25, ']': 26, '_': 27, 'c': 28, 'Cl': 29, 'Br': 30, 'n': 31, 'o': 32, 's': 33} class TextCNNTensorGraph(TensorGraph): def __init__(self, n_tasks, char_dict, seq_length, n_embedding=75, filter_sizes=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20], num_filters=[100, 200, 200, 200, 200, 100, 100, 100, 100, 100, 160, 160], dropout=0.25, mode="classification", **kwargs): """ Parameters ---------- n_tasks: int Number of tasks char_dict: dict Mapping from characters in smiles to integer tokens seq_length: int Length of sequences(after padding) n_embedding: int, optional Length of embedding vector filter_sizes: list of int, optional Properties of filters used in the conv net num_filters: list of int, optional Properties of filters used in the conv net mode: str Either "classification" or "regression" for type of model. """ self.n_tasks = n_tasks self.char_dict = char_dict self.seq_length = seq_length self.n_embedding = n_embedding self.filter_sizes = filter_sizes self.num_filters = num_filters self.dropout = dropout self.mode = mode super(TextCNNTensorGraph, self).__init__(**kwargs) self.build_graph() @staticmethod def build_char_dict(dataset, default_dict=default_dict): X = dataset.ids seq_length = int(max([len(smile) for smile in X]) * 1.2) all_smiles = '_'.join(X) tot_len = len(all_smiles) keys = default_dict.keys() out_dict = copy.deepcopy(default_dict) current_key_val = len(keys) + 1 keys.extend(['_', ' ']) extra_keys = [] i = 0 while i < tot_len: if all_smiles[i:i+2] in keys: i = i + 2 elif all_smiles[i:i+1] in keys: i = i + 1 else: extra_keys.append(all_smiles[i]) keys.append(all_smiles[i]) i = i + 1 for extra_key in extra_keys: out_dict[extra_key] = current_key_val current_key_val += 1 out_dict['_'] = current_key_val return out_dict, seq_length def build_graph(self): self.smiles_seqs = Feature(shape=(None, self.seq_length), dtype=tf.int32) self.Embedding = DTNNEmbedding(n_embedding=self.n_embedding, periodic_table_length=len(self.char_dict.keys())+1, in_layers=[self.smiles_seqs]) self.pooled_outputs = [] self.conv_layers = [] for filter_size, num_filter in zip(self.filter_sizes, self.num_filters): self.conv_layers.append(Conv1D(filter_size, num_filter, padding='VALID', in_layers=[self.Embedding])) self.pooled_outputs.append(MaxPool1D(window_shape=self.seq_length-filter_size+1, strides=1, padding='VALID', in_layers=[self.conv_layers[-1]])) concat_outputs = Concat(axis=2, in_layers=self.pooled_outputs) outputs = Squeeze(squeeze_dims=1, in_layers=concat_outputs) #HIGHWAY LAYER highway = Highway(in_layers=[outputs]) self.gather = Dropout(dropout_prob=self.dropout, in_layers=[highway]) costs = [] self.labels_fd = [] for task in range(self.n_tasks): if self.mode == "classification": classification = Dense( out_channels=2, activation_fn=None, in_layers=[self.gather]) softmax = SoftMax(in_layers=[classification]) self.add_output(softmax) label = Label(shape=(None, 2)) self.labels_fd.append(label) cost = SoftMaxCrossEntropy(in_layers=[label, classification]) costs.append(cost) if self.mode == "regression": regression = Dense( out_channels=1, activation_fn=None, in_layers=[self.gather]) self.add_output(regression) label = Label(shape=(None, 1)) self.labels_fd.append(label) cost = L2Loss(in_layers=[label, regression]) costs.append(cost) if self.mode == "classification": all_cost = Concat(in_layers=costs, axis=1) elif self.mode == "regression": all_cost = Stack(in_layers=costs, axis=1) self.weights = Weights(shape=(None, self.n_tasks)) loss = WeightedError(in_layers=[all_cost, self.weights]) self.set_loss(loss) def default_generator(self, dataset, epochs=1, predict=False, deterministic=True, pad_batches=True): """ TensorGraph style implementation similar to deepchem.models.tf_new_models.graph_topology.AlternateWeaveTopology.batch_to_feed_dict """ for epoch in range(epochs): if not predict: print('Starting epoch %i' % epoch) for (X_b, y_b, w_b, ids_b) in dataset.iterbatches( batch_size=self.batch_size, deterministic=deterministic, pad_batches=pad_batches): feed_dict = dict() if y_b is not None and not predict: for index, label in enumerate(self.labels_fd): if self.mode == "classification": feed_dict[label] = to_one_hot(y_b[:, index]) if self.mode == "regression": feed_dict[label] = y_b[:, index:index + 1] if w_b is not None: feed_dict[self.weights] = w_b smiles_seqs = [self.smiles_to_seq(smiles) for smiles in ids_b] feed_dict[self.smiles_seqs] = np.stack(smiles_seqs, axis=0) yield feed_dict def smiles_to_seq(self, smiles): smiles_len = len(smiles) # Starting token seq = [0] keys = self.char_dict.keys() i = 0 while i < smiles_len: if smiles[i:i+1] == ' ': i = i + 1 if smiles[i:i+2] in keys: seq.append(self.char_dict[smiles[i:i+2]]) i = i + 2 elif smiles[i:i+1] in keys: seq.append(self.char_dict[smiles[i:i+1]]) i = i + 1 else: raise ValueError('character not found in dict') for i in range(self.seq_length - len(seq)): seq.append(self.char_dict['_']) return np.array(seq) No newline at end of file Loading
deepchem/models/tensorgraph/layers.py +43 −13 Original line number Diff line number Diff line Loading @@ -1375,6 +1375,36 @@ class Conv3D(Layer): self.out_tensor = out_tensor return out_tensor class MaxPool1D(Layer): def __init__(self, window_shape=2, strides=1, padding="SAME", **kwargs): self.window_shape = window_shape self.strides = strides self.padding = padding self.pooling_type = "MAX" super(MaxPool1D, self).__init__(**kwargs) try: parent_shape = self.in_layers[0].shape self._shape = tuple(None if p is None else p // s for p, s in zip(parent_shape, strides)) except: pass def create_tensor(self, in_layers=None, set_tensors=True, **kwargs): inputs = self._get_input_tensors(in_layers) in_tensor = inputs[0] out_tensor = tf.nn.pool(in_tensor, window_shape=[self.window_shape], pooling_type=self.pooling_type, padding=self.padding, strides=[self.strides]) if set_tensors: self.out_tensor = out_tensor return out_tensor class MaxPool2D(Layer): Loading
deepchem/models/tensorgraph/models/text_cnn.py 0 → 100644 +229 −0 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Thu Sep 28 15:17:50 2017 @author: zqwu """ import numpy as np import tensorflow as tf import copy from deepchem.metrics import to_one_hot, from_one_hot from deepchem.models.tensorgraph.layers import Dense, Concat, SoftMax, \ SoftMaxCrossEntropy, BatchNorm, WeightedError, Dropout, BatchNormalization, \ Conv1D, MaxPool1D, Squeeze, Stack from deepchem.models.tensorgraph.graph_layers import DTNNEmbedding from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature from deepchem.models.tensorgraph.tensor_graph import TensorGraph from deepchem.trans import undo_transforms default_dict = { '#': 1, '(': 2, ')': 3, '+': 4, '-': 5, '/': 6, '1': 7, '2': 8, '3': 9, '4': 10, '5': 11, '6': 12, '7': 13, '8': 14, '=': 15, 'C': 16, 'F': 17, 'H': 18, 'I': 19, 'N': 20, 'O': 21, 'P': 22, 'S': 23, '[': 24, '\\': 25, ']': 26, '_': 27, 'c': 28, 'Cl': 29, 'Br': 30, 'n': 31, 'o': 32, 's': 33} class TextCNNTensorGraph(TensorGraph): def __init__(self, n_tasks, char_dict, seq_length, n_embedding=75, filter_sizes=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20], num_filters=[100, 200, 200, 200, 200, 100, 100, 100, 100, 100, 160, 160], dropout=0.25, mode="classification", **kwargs): """ Parameters ---------- n_tasks: int Number of tasks char_dict: dict Mapping from characters in smiles to integer tokens seq_length: int Length of sequences(after padding) n_embedding: int, optional Length of embedding vector filter_sizes: list of int, optional Properties of filters used in the conv net num_filters: list of int, optional Properties of filters used in the conv net mode: str Either "classification" or "regression" for type of model. """ self.n_tasks = n_tasks self.char_dict = char_dict self.seq_length = seq_length self.n_embedding = n_embedding self.filter_sizes = filter_sizes self.num_filters = num_filters self.dropout = dropout self.mode = mode super(TextCNNTensorGraph, self).__init__(**kwargs) self.build_graph() @staticmethod def build_char_dict(dataset, default_dict=default_dict): X = dataset.ids seq_length = int(max([len(smile) for smile in X]) * 1.2) all_smiles = '_'.join(X) tot_len = len(all_smiles) keys = default_dict.keys() out_dict = copy.deepcopy(default_dict) current_key_val = len(keys) + 1 keys.extend(['_', ' ']) extra_keys = [] i = 0 while i < tot_len: if all_smiles[i:i+2] in keys: i = i + 2 elif all_smiles[i:i+1] in keys: i = i + 1 else: extra_keys.append(all_smiles[i]) keys.append(all_smiles[i]) i = i + 1 for extra_key in extra_keys: out_dict[extra_key] = current_key_val current_key_val += 1 out_dict['_'] = current_key_val return out_dict, seq_length def build_graph(self): self.smiles_seqs = Feature(shape=(None, self.seq_length), dtype=tf.int32) self.Embedding = DTNNEmbedding(n_embedding=self.n_embedding, periodic_table_length=len(self.char_dict.keys())+1, in_layers=[self.smiles_seqs]) self.pooled_outputs = [] self.conv_layers = [] for filter_size, num_filter in zip(self.filter_sizes, self.num_filters): self.conv_layers.append(Conv1D(filter_size, num_filter, padding='VALID', in_layers=[self.Embedding])) self.pooled_outputs.append(MaxPool1D(window_shape=self.seq_length-filter_size+1, strides=1, padding='VALID', in_layers=[self.conv_layers[-1]])) concat_outputs = Concat(axis=2, in_layers=self.pooled_outputs) outputs = Squeeze(squeeze_dims=1, in_layers=concat_outputs) #HIGHWAY LAYER highway = Highway(in_layers=[outputs]) self.gather = Dropout(dropout_prob=self.dropout, in_layers=[highway]) costs = [] self.labels_fd = [] for task in range(self.n_tasks): if self.mode == "classification": classification = Dense( out_channels=2, activation_fn=None, in_layers=[self.gather]) softmax = SoftMax(in_layers=[classification]) self.add_output(softmax) label = Label(shape=(None, 2)) self.labels_fd.append(label) cost = SoftMaxCrossEntropy(in_layers=[label, classification]) costs.append(cost) if self.mode == "regression": regression = Dense( out_channels=1, activation_fn=None, in_layers=[self.gather]) self.add_output(regression) label = Label(shape=(None, 1)) self.labels_fd.append(label) cost = L2Loss(in_layers=[label, regression]) costs.append(cost) if self.mode == "classification": all_cost = Concat(in_layers=costs, axis=1) elif self.mode == "regression": all_cost = Stack(in_layers=costs, axis=1) self.weights = Weights(shape=(None, self.n_tasks)) loss = WeightedError(in_layers=[all_cost, self.weights]) self.set_loss(loss) def default_generator(self, dataset, epochs=1, predict=False, deterministic=True, pad_batches=True): """ TensorGraph style implementation similar to deepchem.models.tf_new_models.graph_topology.AlternateWeaveTopology.batch_to_feed_dict """ for epoch in range(epochs): if not predict: print('Starting epoch %i' % epoch) for (X_b, y_b, w_b, ids_b) in dataset.iterbatches( batch_size=self.batch_size, deterministic=deterministic, pad_batches=pad_batches): feed_dict = dict() if y_b is not None and not predict: for index, label in enumerate(self.labels_fd): if self.mode == "classification": feed_dict[label] = to_one_hot(y_b[:, index]) if self.mode == "regression": feed_dict[label] = y_b[:, index:index + 1] if w_b is not None: feed_dict[self.weights] = w_b smiles_seqs = [self.smiles_to_seq(smiles) for smiles in ids_b] feed_dict[self.smiles_seqs] = np.stack(smiles_seqs, axis=0) yield feed_dict def smiles_to_seq(self, smiles): smiles_len = len(smiles) # Starting token seq = [0] keys = self.char_dict.keys() i = 0 while i < smiles_len: if smiles[i:i+1] == ' ': i = i + 1 if smiles[i:i+2] in keys: seq.append(self.char_dict[smiles[i:i+2]]) i = i + 2 elif smiles[i:i+1] in keys: seq.append(self.char_dict[smiles[i:i+1]]) i = i + 1 else: raise ValueError('character not found in dict') for i in range(self.seq_length - len(seq)): seq.append(self.char_dict['_']) return np.array(seq) No newline at end of file
