def _build_graph(self): self.one_hot_seq = Feature(shape=(None, self.pad_length, self.num_amino_acids), dtype=tf.float32) conv1 = Conv1D(kernel_size=2, filters=512, in_layers=[self.one_hot_seq]) maxpool1 = MaxPool1D(strides=2, padding="VALID", in_layers=[conv1]) conv2 = Conv1D(kernel_size=3, filters=512, in_layers=[maxpool1]) flattened = Flatten(in_layers=[conv2]) dense1 = Dense(out_channels=400, in_layers=[flattened], activation_fn=tf.nn.tanh) dropout = Dropout(dropout_prob=self.dropout_p, in_layers=[dense1]) output = Dense(out_channels=1, in_layers=[dropout], activation_fn=None) self.add_output(output) if self.mode == "regression": label = Label(shape=(None, 1)) loss = L2Loss(in_layers=[label, output]) else: raise NotImplementedError( "Classification support not added yet. Missing details in paper." ) weights = Weights(shape=(None, )) weighted_loss = WeightedError(in_layers=[loss, weights]) self.set_loss(weighted_loss)
def test_Conv1D_pickle(): tg = TensorGraph() feature = Feature(shape=(tg.batch_size, 1, 1)) conv = Conv1D(2, 1, in_layers=feature) tg.add_output(conv) tg.set_loss(conv) tg.build() tg.save()
def build_graph(self): self.smiles_seqs = Feature(shape=(None, self.seq_length), dtype=tf.int32) # Character embedding 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.kernel_sizes, self.num_filters): # Multiple convolutional layers with different filter widths self.conv_layers.append( Conv1D( kernel_size=filter_size, filters=num_filter, padding='valid', in_layers=[self.Embedding])) # Max-over-time pooling self.pooled_outputs.append( ReduceMax(axis=1, in_layers=[self.conv_layers[-1]])) # Concat features from all filters(one feature per filter) concat_outputs = Concat(axis=1, in_layers=self.pooled_outputs) dropout = Dropout(dropout_prob=self.dropout, in_layers=[concat_outputs]) dense = Dense( out_channels=200, activation_fn=tf.nn.relu, in_layers=[dropout]) # Highway layer from https://arxiv.org/pdf/1505.00387.pdf self.gather = Highway(in_layers=[dense]) 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 = Stack(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 _build_graph(self): self.smiles_seqs = Feature(shape=(None, self.seq_length), dtype=tf.int32) # Character embedding Embedding = DTNNEmbedding( n_embedding=self.n_embedding, periodic_table_length=len(self.char_dict.keys()) + 1, in_layers=[self.smiles_seqs]) pooled_outputs = [] conv_layers = [] for filter_size, num_filter in zip(self.kernel_sizes, self.num_filters): # Multiple convolutional layers with different filter widths conv_layers.append( Conv1D(kernel_size=filter_size, filters=num_filter, padding='valid', in_layers=[Embedding])) # Max-over-time pooling pooled_outputs.append( ReduceMax(axis=1, in_layers=[conv_layers[-1]])) # Concat features from all filters(one feature per filter) concat_outputs = Concat(axis=1, in_layers=pooled_outputs) dropout = Dropout(dropout_prob=self.dropout, in_layers=[concat_outputs]) dense = Dense(out_channels=200, activation_fn=tf.nn.relu, in_layers=[dropout]) # Highway layer from https://arxiv.org/pdf/1505.00387.pdf gather = Highway(in_layers=[dense]) if self.mode == "classification": logits = Dense(out_channels=self.n_tasks * 2, activation_fn=None, in_layers=[gather]) logits = Reshape(shape=(-1, self.n_tasks, 2), in_layers=[logits]) output = SoftMax(in_layers=[logits]) self.add_output(output) labels = Label(shape=(None, self.n_tasks, 2)) loss = SoftMaxCrossEntropy(in_layers=[labels, logits]) else: vals = Dense(out_channels=self.n_tasks * 1, activation_fn=None, in_layers=[gather]) vals = Reshape(shape=(-1, self.n_tasks, 1), in_layers=[vals]) self.add_output(vals) labels = Label(shape=(None, self.n_tasks, 1)) loss = ReduceSum(L2Loss(in_layers=[labels, vals])) weights = Weights(shape=(None, self.n_tasks)) weighted_loss = WeightedError(in_layers=[loss, weights]) self.set_loss(weighted_loss)
def test_conv_1D(self): """Test that Conv1D can be invoked.""" width = 5 in_channels = 2 out_channels = 3 batch_size = 10 in_tensor = np.random.rand(batch_size, width, in_channels) with self.test_session() as sess: in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32) out_tensor = Conv1D(width, out_channels)(in_tensor) sess.run(tf.global_variables_initializer()) out_tensor = out_tensor.eval() assert out_tensor.shape == (batch_size, width, out_channels)
def test_conv_1D(self): """Test that Conv1D can be invoked.""" width = 5 in_channels = 2 filters = 3 kernel_size = 2 batch_size = 10 in_tensor = np.random.rand(batch_size, width, in_channels) with self.session() as sess: in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32) out_tensor = Conv1D(filters, kernel_size)(in_tensor) sess.run(tf.global_variables_initializer()) out_tensor = out_tensor.eval() self.assertEqual(out_tensor.shape[0], batch_size) self.assertEqual(out_tensor.shape[2], filters)