description added for logreg (a89fc292) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorflow_models/lr.py

+12 −3

Original line number	Diff line number	Diff line
		@@ -19,6 +19,8 @@ from deepchem.metrics import to_one_hot
		from deepchem.data import pad_features

		def weight_decay(penalty_type, penalty):
		# due to the different shape of weight(ndims=2) and bias(ndims=1),
		# will using this version for logreg
		variables = []
		# exclude bias variables
		for v in tf.trainable_variables():
		@@ -60,6 +62,7 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		bias_init_consts = self.bias_init_consts
		lg_list = []
		for task in range(self.n_tasks):
		#setting up n_tasks nodes(output nodes)
		lg = model_ops.fully_connected_layer(
		tensor=self.mol_features,
		size = 1,
		@@ -73,6 +76,7 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		return lg_list

		def add_label_placeholders(self, graph, name_scopes):
		#label placeholders with size batch_size * 1
		labels = []
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph, name_scopes)
		with placeholder_scope:
		@@ -114,6 +118,7 @@ class TensorflowLogisticRegression(TensorflowGraphModel):

		# weight decay
		if self.penalty != 0.0:
		# using self-defined regularization
		penalty = weight_decay(self.penalty_type, self.penalty)
		loss += penalty

		@@ -124,6 +129,7 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		weights)

		def add_output_ops(self, graph, output):
		# adding output nodes of sigmoid function
		with graph.as_default():
		sigmoid = []
		with tf.name_scope('inference'):
		@@ -139,6 +145,7 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		for task in range(self.n_tasks):
		if y_b is not None:
		y_2column = to_one_hot(y_b[:, task])
		# fix the size to be [?,1]
		orig_dict["labels_%d" % task] = y_2column[:,1:2]
		else:
		# Dummy placeholders
		@@ -164,12 +171,13 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		data = self._get_shared_session(train=False).run(
		self.eval_graph.output, feed_dict=feed_dict)
		batch_outputs = np.asarray(data[:n_tasks], dtype=float)
		# reshape to batch_size x n_tasks x ...
		# transfer 2D prediction tensor to 2D x n_classes(=2)
		complimentary = np.ones(np.shape(batch_outputs))
		complimentary = complimentary - batch_outputs
		batch_outputs = np.squeeze(np.stack(arrays = [complimentary,
		batch_outputs],
		axis = 2))
		# reshape to batch_size x n_tasks x ...
		if batch_outputs.ndim == 3:
		batch_outputs = batch_outputs.transpose((1, 0, 2))
		elif batch_outputs.ndim == 2:
		@@ -201,12 +209,13 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		data = self._get_shared_session(train=False).run(
		self.eval_graph.output, feed_dict=feed_dict)
		batch_output = np.asarray(data[:n_tasks], dtype=float)
		# reshape to batch_size x n_tasks x ...
		# transfer 2D prediction tensor to 2D x n_classes(=2)
		complimentary = np.ones(np.shape(batch_output))
		complimentary = complimentary - batch_output
		batch_output = np.squeeze(np.stack(arrays = [complimentary,
		batch_output],
		axis = 2))
		# reshape to batch_size x n_tasks x ...
		if batch_output.ndim == 3:
		batch_output = batch_output.transpose((1, 0, 2))
		elif batch_output.ndim == 2:

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