Rename TextCNNTensorGraph to TextCNNModel (4cac0b6d) · Commits · 钟慕尧 / deepchem

deepchem/models/init.py

+1 −1

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		@@ -22,7 +22,7 @@ from deepchem.models.tensorgraph.models.symmetry_function_regression import BPSy

		from deepchem.models.tensorgraph.models.seqtoseq import SeqToSeq
		from deepchem.models.tensorgraph.models.gan import GAN, WGAN
		from deepchem.models.tensorgraph.models.text_cnn import TextCNNTensorGraph
		from deepchem.models.tensorgraph.models.text_cnn import TextCNNModel
		from deepchem.models.tensorgraph.sequential import Sequential
		from deepchem.models.tensorgraph.models.sequence_dnn import SequenceDNN

+3 −3

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		@@ -55,7 +55,7 @@ default_dict = {
		}


		class TextCNNTensorGraph(TensorGraph):
		class TextCNNModel(TensorGraph):
		""" A Convolutional neural network on smiles strings
		Reimplementation of the discriminator module in ORGAN: https://arxiv.org/abs/1705.10843
		Originated from: http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf
		@@ -117,7 +117,7 @@ class TextCNNTensorGraph(TensorGraph):
		self.num_filters = num_filters
		self.dropout = dropout
		self.mode = mode
		super(TextCNNTensorGraph, self).__init__(**kwargs)
		super(TextCNNModel, self).__init__(**kwargs)
		self.build_graph()

		@staticmethod
		@@ -271,7 +271,7 @@ class TextCNNTensorGraph(TensorGraph):
		return np.array(seq)

		def predict_on_generator(self, generator, transformers=[], outputs=None):
		out = super(TextCNNTensorGraph, self).predict_on_generator(
		out = super(TextCNNModel, self).predict_on_generator(
		generator, transformers=[], outputs=outputs)
		if outputs is None:
		outputs = self.outputs

+4 −4

Original line number	Diff line number	Diff line
		@@ -811,10 +811,10 @@ class TestOverfit(test_util.TensorFlowTestCase):

		classification_metric = dc.metrics.Metric(dc.metrics.accuracy_score)

		char_dict, length = dc.models.TextCNNTensorGraph.build_char_dict(dataset)
		char_dict, length = dc.models.TextCNNModel.build_char_dict(dataset)
		batch_size = 10

		model = dc.models.TextCNNTensorGraph(
		model = dc.models.TextCNNModel(
		n_tasks,
		char_dict,
		seq_length=length,
		@@ -849,10 +849,10 @@ class TestOverfit(test_util.TensorFlowTestCase):
		regression_metric = dc.metrics.Metric(
		dc.metrics.pearson_r2_score, task_averager=np.mean)

		char_dict, length = dc.models.TextCNNTensorGraph.build_char_dict(dataset)
		char_dict, length = dc.models.TextCNNModel.build_char_dict(dataset)
		batch_size = 10

		model = dc.models.TextCNNTensorGraph(
		model = dc.models.TextCNNModel(
		n_tasks,
		char_dict,
		seq_length=length,

+4 −4

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		@@ -264,10 +264,10 @@ def benchmark_classification(train_dataset,

		all_data = deepchem.data.DiskDataset.merge(
		[train_dataset, valid_dataset, test_dataset])
		char_dict, length = deepchem.models.TextCNNTensorGraph.build_char_dict(
		char_dict, length = deepchem.models.TextCNNModel.build_char_dict(
		all_data)

		model = deepchem.models.TextCNNTensorGraph(
		model = deepchem.models.TextCNNModel(
		len(tasks),
		char_dict,
		seq_length=length,
		@@ -597,10 +597,10 @@ def benchmark_regression(train_dataset,
		filter_sizes = hyper_parameters['filter_sizes']
		num_filters = hyper_parameters['num_filters']

		char_dict, length = deepchem.models.TextCNNTensorGraph.build_char_dict(
		char_dict, length = deepchem.models.TextCNNModel.build_char_dict(
		train_dataset)

		model = deepchem.models.TextCNNTensorGraph(
		model = deepchem.models.TextCNNModel(
		len(tasks),
		char_dict,
		seq_length=length,

+2 −2

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		@@ -19,12 +19,12 @@ train_dataset, valid_dataset, test_dataset = delaney_datasets
		# Fit models
		metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)

		char_dict, length = dc.models.TextCNNTensorGraph.build_char_dict(train_dataset)
		char_dict, length = dc.models.TextCNNModel.build_char_dict(train_dataset)

		# Batch size of models
		batch_size = 64

		model = dc.models.TextCNNTensorGraph(
		model = dc.models.TextCNNModel(
		len(delaney_tasks),
		char_dict,
		seq_length=length,