Changed feature shapes to deal with tf.feature_column (3f8be5df) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/symmetry_function_regression.py

+12 −4

Original line number	Diff line number	Diff line
		@@ -114,6 +114,8 @@ class BPSymmetryFunctionRegression(TensorGraph):
		feed_dict[self.task_weights[0]] = w_b

		atom_feats, atom_flags = self.compute_features_on_batch(X_b)
		atom_feats = atom_feats.reshape(-1, self.max_atoms * self.n_feat)
		atom_flags = atom_flags.reshape(-1, self.max_atoms * self.max_atoms)
		feed_dict[self.atom_feats] = atom_feats
		feed_dict[self.atom_flags] = atom_flags

		@@ -214,10 +216,13 @@ class ANIRegression(TensorGraph):
		feed_dict = dict()
		X = dataset.X
		flags = np.sign(np.array(X[:upper_lim, :, 0]))
		feed_dict[self.atom_flags] = np.stack([flags]self.max_atoms, axis=2)\
		atom_flags = np.stack([flags]self.max_atoms, axis=2)\
		np.stack([flags]*self.max_atoms, axis=1)
		feed_dict[self.atom_numbers] = np.array(X[:upper_lim, :, 0], dtype=int)
		feed_dict[self.atom_feats] = np.array(X[:upper_lim, :, :], dtype=float)
		feed_dict[self.atom_flags] = atom_flags.reshape(-1, self.max_atoms * self.max_atoms)
		atom_numbers = np.array(X[:upper_lim, :, 0], dtype=int)
		feed_dict[self.atom_numbers] = atom_numbers
		atom_feats = np.array(X[:upper_lim, :, :], dtype=float)
		feed_dict[self.atom_feats] = atom_feats.reshape(-1, self.max_atoms * 4)
		return self.session.run([self.grad], feed_dict=feed_dict)

		def pred_one(self, X, atomic_nums, constraints=None):
		@@ -284,7 +289,8 @@ class ANIRegression(TensorGraph):
		X = Z
		inp = np.array(X).reshape((1, self.max_atoms, 4))
		dd = dc.data.NumpyDataset(inp, np.array([1]), np.array([1]))
		res = self.compute_grad(dd)[0][0][0]
		res = self.compute_grad(dd)[0][0]
		res = res.reshape(self.max_atoms, 4)
		res = res[:num_atoms, 1:]

		if constraints is not None:
		@@ -402,6 +408,8 @@ class ANIRegression(TensorGraph):

		atom_feats, atom_numbers, atom_flags = self.compute_features_on_batch(
		X_b)
		atom_feats = atom_feats.reshape(-1, self.max_atoms * 4)
		atom_flags = atom_flags.reshape(-1, self.max_atoms * self.max_atoms)
		feed_dict[self.atom_feats] = atom_feats
		feed_dict[self.atom_numbers] = atom_numbers
		feed_dict[self.atom_flags] = atom_flags

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