finishing tensorgraph progressive (f373058b) · Commits · 钟慕尧 / deepchem

deepchem/models/init.py

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		@@ -17,6 +17,7 @@ from deepchem.models.tensorgraph.fcnet import MultiTaskFitTransformRegressor
		from deepchem.models.tensorgraph.IRV import TensorflowMultiTaskIRVClassifier
		from deepchem.models.tensorgraph.robust_multitask import RobustMultitaskClassifier
		from deepchem.models.tensorgraph.robust_multitask import RobustMultitaskRegressor
		from deepchem.models.tensorgraph.progressive_multitask import ProgressiveMultitaskRegressor
		from deepchem.models.tensorgraph.models.graph_models import WeaveTensorGraph, DTNNTensorGraph, DAGTensorGraph, GraphConvTensorGraph, MPNNTensorGraph
		from deepchem.models.tensorgraph.models.symmetry_function_regression import BPSymmetryFunctionRegression, ANIRegression

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		@@ -1207,6 +1207,29 @@ class Sigmoid(Layer):
		return out_tensor


		class ReLU(Layer):
		""" Compute the relu activation of input: f(x) = relu(x)
		Only one input is allowed, output will have the same shape as input
		"""

		def __init__(self, in_layers=None, **kwargs):
		super(ReLU, self).__init__(in_layers, **kwargs)
		try:
		self._shape = tuple(self.in_layers[0].shape)
		except:
		pass

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		inputs = self._get_input_tensors(in_layers)
		if len(inputs) != 1:
		raise ValueError("ReLU must have a single input layer.")
		parent = inputs[0]
		out_tensor = tf.nn.relu(parent)
		if set_tensors:
		self.out_tensor = out_tensor
		return out_tensor


		class Concat(Layer):

		def __init__(self, in_layers=None, axis=1, **kwargs):

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		@@ -928,6 +928,7 @@ class Submodel(object):
		optimizer = self.graph.optimizer
		else:
		optimizer = self.optimizer
		# Should we keep a separate global step count for each submodel?
		global_step = self.graph._get_tf('GlobalStep')
		tf_opt = optimizer._create_optimizer(global_step)
		self._train_op = tf_opt.minimize(loss.out_tensor, global_step, variables)

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		@@ -37,6 +37,7 @@ from deepchem.models.tensorgraph.layers import Multiply
		from deepchem.models.tensorgraph.layers import ReduceMean
		from deepchem.models.tensorgraph.layers import ReduceSquareDifference
		from deepchem.models.tensorgraph.layers import ReduceSum
		from deepchem.models.tensorgraph.layers import ReLU
		from deepchem.models.tensorgraph.layers import Repeat
		from deepchem.models.tensorgraph.layers import Reshape
		from deepchem.models.tensorgraph.layers import SluiceLoss
		@@ -228,6 +229,17 @@ class TestLayers(test_util.TensorFlowTestCase):
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size,)

		def test_relu(self):
		"""Test that Sigmoid can be invoked."""
		batch_size = 10
		n_features = 5
		in_tensor = np.random.rand(batch_size, n_features)
		with self.test_session() as sess:
		in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
		out_tensor = ReLU()(in_tensor)
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size, n_features)

		def test_sigmoid(self):
		"""Test that Sigmoid can be invoked."""
		batch_size = 10