graph models need to have deterministic in interface (8a4a3626) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/graph_models.py

+10 −5

Original line number	Diff line number	Diff line
		@@ -123,6 +123,7 @@ class WeaveTensorGraph(TensorGraph):
		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
		@@ -132,7 +133,7 @@ class WeaveTensorGraph(TensorGraph):
		print('Starting epoch %i' % epoch)
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		deterministic=True,
		deterministic=deterministic,
		pad_batches=pad_batches):

		feed_dict = dict()
		@@ -279,6 +280,7 @@ class DTNNTensorGraph(TensorGraph):
		dataset,
		epochs=1,
		predict=False,
		deterministic=True,
		pad_batches=True):
		""" TensorGraph style implementation
		similar to deepchem.models.tf_new_models.graph_topology.DTNNGraphTopology.batch_to_feed_dict
		@@ -288,7 +290,7 @@ class DTNNTensorGraph(TensorGraph):
		print('Starting epoch %i' % epoch)
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		deterministic=True,
		deterministic=deterministic,
		pad_batches=pad_batches):

		feed_dict = dict()
		@@ -430,6 +432,7 @@ class DAGTensorGraph(TensorGraph):
		dataset,
		epochs=1,
		predict=False,
		deterministic=True,
		pad_batches=True):
		""" TensorGraph style implementation
		similar to deepchem.models.tf_new_models.graph_topology.DAGGraphTopology.batch_to_feed_dict
		@@ -439,7 +442,7 @@ class DAGTensorGraph(TensorGraph):
		print('Starting epoch %i' % epoch)
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		deterministic=True,
		deterministic=deterministic,
		pad_batches=pad_batches):

		feed_dict = dict()
		@@ -575,13 +578,14 @@ class GraphConvTensorGraph(TensorGraph):
		dataset,
		epochs=1,
		predict=False,
		deterministic=True,
		pad_batches=True):
		for epoch in range(epochs):
		if not predict:
		print('Starting epoch %i' % epoch)
		for ind, (X_b, y_b, w_b, ids_b) in enumerate(
		dataset.iterbatches(
		self.batch_size, pad_batches=True, deterministic=True)):
		self.batch_size, pad_batches=True, deterministic=deterministic)):
		d = {}
		for index, label in enumerate(self.my_labels):
		if self.mode == 'classification':
		@@ -789,6 +793,7 @@ class MPNNTensorGraph(TensorGraph):
		dataset,
		epochs=1,
		predict=False,
		deterministic=True,
		pad_batches=True):
		""" Same generator as Weave models """
		for epoch in range(epochs):
		@@ -796,7 +801,7 @@ class MPNNTensorGraph(TensorGraph):
		print('Starting epoch %i' % epoch)
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		deterministic=True,
		deterministic=deterministic,
		pad_batches=pad_batches):

		feed_dict = dict()

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