Merge pull request #628 from lilleswing/acnn-fixes (9ec1cc90) · Commits · 钟慕尧 / deepchem

contrib/atomicconv/acnn/core/tensor_graph_hyper_param_search.py

+3 −0

Original line number	Diff line number	Diff line
		@@ -83,6 +83,9 @@ def params():
		for values in itertools.product(radial1, radial2, radial3, layer_sizes,
		learning_rates, epochs):
		d = {
		"frag1_num_atoms": 140,
		"frag2_num_atoms": 821,
		"complex_num_atoms": 908,
		"radial": [values[0], values[1], values[2]],
		"layer_sizes": values[3],
		"learning_rate": values[4],

+3 −3

Original line number	Diff line number	Diff line
		@@ -9,7 +9,7 @@ __license__ = "MIT"
		import sys

		sys.path.append("../../models")
		from deepchem.models.tensorgraph.layers import Layer, Feature, Label, AtomicConvolution, L2Loss
		from deepchem.models.tensorgraph.layers import Layer, Feature, Label, AtomicConvolution, L2Loss, ReduceMean
		from deepchem.models import TensorGraph

		import numpy as np
		@@ -64,7 +64,7 @@ class AtomicConvScore(Layer):
		self.layer_sizes = layer_sizes
		super(AtomicConvScore, self).__init__(**kwargs)

		def _create_tensor(self):
		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		frag1_layer = self.in_layers[0].out_tensor
		frag2_layer = self.in_layers[1].out_tensor
		complex_layer = self.in_layers[2].out_tensor
		@@ -212,7 +212,7 @@ def atomic_conv_model(
		])

		label = Label(shape=(None, 1))
		loss = L2Loss(in_layers=[score, label])
		loss = ReduceMean(in_layers=L2Loss(in_layers=[score, label]))

		def feed_dict_generator(dataset, batch_size, epochs=1, pad_batches=True):