Merge pull request #1550 from peastman/deprecated (26b924c5) · Commits · 钟慕尧 / deepchem

contrib/atomicconv/models/atomicnet_ops.py

+5 −5

Original line number	Diff line number	Diff line
		@@ -80,8 +80,8 @@ def AtomicNNLayer(tensor, size, weights, biases, name=None):
		"""

		if len(tensor.get_shape()) != 2:
		raise ValueError('Dense layer input must be 2D, not %dD' %
		len(tensor.get_shape()))
		raise ValueError(
		'Dense layer input must be 2D, not %dD' % len(tensor.get_shape()))
		with tf.name_scope(name, 'fully_connected', [tensor, weights, biases]):
		return tf.nn.xw_plus_b(tensor, weights, biases)

		@@ -111,8 +111,8 @@ def gather_neighbors(X, nbr_indices, B, N, M, d):
		example_tensors = tf.unstack(X, axis=0)
		example_nbrs = tf.unstack(nbr_indices, axis=0)
		all_nbr_coords = []
		for example, (example_tensor,
		example_nbr) in enumerate(zip(example_tensors, example_nbrs)):
		for example, (example_tensor, example_nbr) in enumerate(
		zip(example_tensors, example_nbrs)):
		nbr_coords = tf.gather(example_tensor, example_nbr)
		all_nbr_coords.append(nbr_coords)
		neighbors = tf.stack(all_nbr_coords)
		@@ -149,7 +149,7 @@ def DistanceTensor(X, Nbrs, boxsize, B, N, M, d):
		nbrs_tensors = tf.unstack(nbrs, axis=1)
		for nbr, nbr_tensor in enumerate(nbrs_tensors):
		_D = tf.subtract(nbr_tensor, atom_tensor)
		_D = tf.subtract(_D, boxsize * tf.round(tf.div(_D, boxsize)))
		_D = tf.subtract(_D, boxsize * tf.round(tf.math.divide(_D, boxsize)))
		D.append(_D)
		else:
		for atom, atom_tensor in enumerate(atom_tensors):

contrib/atomicconv/models/legacy.py

+11 −11

Original line number	Diff line number	Diff line
		@@ -278,7 +278,7 @@ class TensorflowGraphModel(Model):
		# non-zero weight examples in the batch. Also, instead of using
		# tf.reduce_mean (which can put ops on the CPU) we explicitly
		# calculate with div/sum so it stays on the GPU.
		gradient_cost = tf.div(
		gradient_cost = tf.math.divide(
		tf.reduce_sum(weighted_cost), self.batch_size)
		gradient_costs.append(gradient_cost)

		@@ -416,8 +416,8 @@ class TensorflowGraphModel(Model):
		feeding and fetching the same tensor.
		"""
		weights = []
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		with placeholder_scope:
		for task in range(self.n_tasks):
		weights.append(
		@@ -617,8 +617,8 @@ class TensorflowClassifier(TensorflowGraphModel):
		Placeholders are wrapped in identity ops to avoid the error caused by
		feeding and fetching the same tensor.
		"""
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		with graph.as_default():
		batch_size = self.batch_size
		n_classes = self.n_classes
		@@ -770,8 +770,8 @@ class TensorflowRegressor(TensorflowGraphModel):
		Placeholders are wrapped in identity ops to avoid the error caused by
		feeding and fetching the same tensor.
		"""
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		with graph.as_default():
		batch_size = self.batch_size
		labels = []
		@@ -859,8 +859,8 @@ class TensorflowMultiTaskRegressor(TensorflowRegressor):
		batch_size x n_features.
		"""
		n_features = self.n_features
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		with graph.as_default():
		with placeholder_scope:
		self.mol_features = tf.placeholder(
		@@ -906,8 +906,8 @@ class TensorflowMultiTaskRegressor(TensorflowRegressor):
		weight_init=tf.truncated_normal(
		shape=[prev_layer_size, 1],
		stddev=weight_init_stddevs[i]),
		bias_init=tf.constant(value=bias_init_consts[i], shape=[1
		]))))
		bias_init=tf.constant(value=bias_init_consts[i],
		shape=[1]))))
		return output

		def construct_feed_dict(self, X_b, y_b=None, w_b=None, ids_b=None):

contrib/one_shot_models/multitask_classifier.py

+7 −5

Original line number	Diff line number	Diff line
		@@ -85,7 +85,8 @@ class MultitaskGraphClassifier(Model):
		pad_batches=True,
		verbose=True):

		warnings.warn("MultitaskGraphClassifier is deprecated. "
		warnings.warn(
		"MultitaskGraphClassifier is deprecated. "
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		super(MultitaskGraphClassifier, self).__init__(
		model_dir=logdir, verbose=verbose)
		@@ -191,8 +192,9 @@ class MultitaskGraphClassifier(Model):
		task_label_vector = task_labels[task]
		task_weight_vector = task_weights[task]
		# Convert the labels into one-hot vector encodings.
		one_hot_labels = tf.to_float(
		tf.one_hot(tf.to_int32(tf.squeeze(task_label_vector)), 2))
		one_hot_labels = tf.cast(
		tf.one_hot(tf.cast(tf.squeeze(task_label_vector), tf.int32), 2),
		tf.float32)
		# Since we use tf.nn.softmax_cross_entropy_with_logits note that we pass in
		# un-softmaxed logits rather than softmax outputs.
		task_loss = loss_fn(logits[task], one_hot_labels, task_weight_vector)
		@@ -200,7 +202,7 @@ class MultitaskGraphClassifier(Model):
		# It's ok to divide by just the batch_size rather than the number of nonzero
		# examples (effect averages out)
		total_loss = tf.add_n(task_losses)
		total_loss = tf.div(total_loss, self.batch_size)
		total_loss = tf.math.divide(total_loss, self.batch_size)
		return total_loss

		def add_softmax(self, outputs):

contrib/one_shot_models/multitask_regressor.py

+8 −7

Original line number	Diff line number	Diff line
		@@ -44,7 +44,8 @@ class MultitaskGraphRegressor(Model):
		pad_batches=True,
		verbose=True):

		warnings.warn("MultitaskGraphRegressor is deprecated. "
		warnings.warn(
		"MultitaskGraphRegressor is deprecated. "
		"Will be removed in DeepChem 1.4.", DeprecationWarning)

		super(MultitaskGraphRegressor, self).__init__(
		@@ -154,14 +155,13 @@ class MultitaskGraphRegressor(Model):
		for task in range(self.n_tasks):
		task_label_vector = task_labels[task]
		task_weight_vector = task_weights[task]
		task_loss = loss_fn(outputs[task],
		tf.squeeze(task_label_vector),
		task_loss = loss_fn(outputs[task], tf.squeeze(task_label_vector),
		tf.squeeze(task_weight_vector))
		task_losses.append(task_loss)
		# It's ok to divide by just the batch_size rather than the number of nonzero
		# examples (effect averages out)
		total_loss = tf.add_n(task_losses)
		total_loss = tf.div(total_loss, self.batch_size)
		total_loss = tf.math.divide(total_loss, self.batch_size)
		return total_loss

		def fit(self,
		@@ -222,7 +222,8 @@ class DTNNMultitaskGraphRegressor(MultitaskGraphRegressor):

		def build(self):
		# Create target inputs
		warnings.warn("DTNNMultitaskGraphRegressor is deprecated. "
		warnings.warn(
		"DTNNMultitaskGraphRegressor is deprecated. "
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		self.label_placeholder = tf.placeholder(
		dtype='float32', shape=(None, self.n_tasks), name="label_placeholder")

contrib/tensorflow_models/init.py

+7 −5

Original line number	Diff line number	Diff line
		@@ -51,7 +51,8 @@ class TensorflowGraph(object):

		def __init__(self, graph, session, name_scopes, output, labels, weights,
		loss):
		warnings.warn("TensorflowGraph is deprecated. "
		warnings.warn(
		"TensorflowGraph is deprecated. "
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		self.graph = graph
		self.session = session
		@@ -178,7 +179,8 @@ class TensorflowGraphModel(Model):
		seed: int
		If not none, is used as random seed for tensorflow.
		"""
		warnings.warn("TensorflowGraphModel is deprecated. "
		warnings.warn(
		"TensorflowGraphModel is deprecated. "
		"Will be removed in DeepChem 1.4.", DeprecationWarning)

		# Save hyperparameters
		@@ -278,7 +280,7 @@ class TensorflowGraphModel(Model):
		# non-zero weight examples in the batch. Also, instead of using
		# tf.reduce_mean (which can put ops on the CPU) we explicitly
		# calculate with div/sum so it stays on the GPU.
		gradient_cost = tf.div(
		gradient_cost = tf.math.divide(
		tf.reduce_sum(weighted_cost), self.batch_size)
		gradient_costs.append(gradient_cost)

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