yapfed (ff3db6f8) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorflow_models/IRV.py

+4 −4

Original line number	Diff line number	Diff line
		@@ -91,8 +91,8 @@ class TensorflowMultiTaskIRVClassifier(TensorflowLogisticRegression):

		https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2750043/
		"""
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		K = self.K
		with graph.as_default():
		output = []
		@@ -106,8 +106,8 @@ class TensorflowMultiTaskIRVClassifier(TensorflowLogisticRegression):
		b2 = tf.Variable(tf.constant([0.01]), name="b2", dtype=tf.float32)

		label_placeholders = self.add_label_placeholders(graph, name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(graph,
		name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(
		graph, name_scopes)
		if training:
		graph.queue = tf.FIFOQueue(
		capacity=5,

deepchem/models/tensorflow_models/init.py

+2 −4

Original line number	Diff line number	Diff line
		@@ -52,8 +52,7 @@ class TensorflowGraph(object):
		def __init__(self, graph, session, name_scopes, output, labels, weights,
		loss):
		warnings.warn("TensorflowGraph is deprecated. "
		"Will be removed in DeepChem 1.4.",
		DeprecationWarning)
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		self.graph = graph
		self.session = session
		self.name_scopes = name_scopes
		@@ -180,8 +179,7 @@ class TensorflowGraphModel(Model):
		If not none, is used as random seed for tensorflow.
		"""
		warnings.warn("TensorflowGraphModel is deprecated. "
		"Will be removed in DeepChem 1.4.",
		DeprecationWarning)
		"Will be removed in DeepChem 1.4.", DeprecationWarning)

		# Save hyperparameters
		self.n_tasks = n_tasks

deepchem/models/tensorflow_models/fcnet.py

+72 −37

Original line number	Diff line number	Diff line
		@@ -9,6 +9,7 @@ import time
		import numpy as np
		import tensorflow as tf
		import threading
		import collections

		import deepchem as dc
		from deepchem.nn import model_ops
		@@ -29,11 +30,12 @@ class TensorGraphMultiTaskClassifier(TensorGraph):
		n_tasks,
		n_features,
		layer_sizes=[1000],
		weight_init_stddevs=[0.02],
		bias_init_consts=[1.0],
		weight_init_stddevs=0.02,
		bias_init_consts=1.0,
		weight_decay_penalty=0.0,
		weight_decay_penalty_type="l2",
		dropouts=[0.5],
		dropouts=0.5,
		activation_fns=tf.nn.relu,
		n_classes=2,
		**kwargs):
		"""Create a TensorGraphMultiTaskClassifier.
		@@ -49,17 +51,24 @@ class TensorGraphMultiTaskClassifier(TensorGraph):
		number of features
		layer_sizes: list
		the size of each dense layer in the network. The length of this list determines the number of layers.
		weight_init_stddevs: list
		weight_init_stddevs: list or float
		the standard deviation of the distribution to use for weight initialization of each layer. The length
		of this list should equal len(layer_sizes).
		bias_init_consts: list
		of this list should equal len(layer_sizes). Alternatively this may be a single value instead of a list,
		in which case the same value is used for every layer.
		bias_init_consts: list or loat
		the value to initialize the biases in each layer to. The length of this list should equal len(layer_sizes).
		Alternatively this may be a single value instead of a list, in which case the same value is used for every layer.
		weight_decay_penalty: float
		the magnitude of the weight decay penalty to use
		weight_decay_penalty_type: str
		the type of penalty to use for weight decay, either 'l1' or 'l2'
		dropouts: list
		dropouts: list or float
		the dropout probablity to use for each layer. The length of this list should equal len(layer_sizes).
		Alternatively this may be a single value instead of a list, in which case the same value is used for every layer.
		activation_fns: list or object
		the Tensorflow activation function to apply to each layer. The length of this list should equal
		len(layer_sizes). Alternatively this may be a single value instead of a list, in which case the
		same value is used for every layer.
		n_classes: int
		the number of classes
		"""
		@@ -68,6 +77,15 @@ class TensorGraphMultiTaskClassifier(TensorGraph):
		self.n_tasks = n_tasks
		self.n_features = n_features
		self.n_classes = n_classes
		n_layers = len(layer_sizes)
		if not isinstance(weight_init_stddevs, collections.Sequence):
		weight_init_stddevs = [weight_init_stddevs] * n_layers
		if not isinstance(bias_init_consts, collections.Sequence):
		bias_init_consts = [bias_init_consts] * n_layers
		if not isinstance(dropouts, collections.Sequence):
		dropouts = [dropouts] * n_layers
		if not isinstance(activation_fns, collections.Sequence):
		activation_fns = [activation_fns] * n_layers

		# Add the input features.

		@@ -76,12 +94,13 @@ class TensorGraphMultiTaskClassifier(TensorGraph):

		# Add the dense layers

		for size, weight_stddev, bias_const, dropout in zip(
		layer_sizes, weight_init_stddevs, bias_init_consts, dropouts):
		for size, weight_stddev, bias_const, dropout, activation_fn in zip(
		layer_sizes, weight_init_stddevs, bias_init_consts, dropouts,
		activation_fns):
		layer = Dense(
		in_layers=[prev_layer],
		out_channels=size,
		activation_fn=tf.nn.relu,
		activation_fn=activation_fn,
		weights_initializer=TFWrapper(
		tf.truncated_normal_initializer, stddev=weight_stddev),
		biases_initializer=TFWrapper(
		@@ -121,8 +140,9 @@ class TensorGraphMultiTaskClassifier(TensorGraph):
		pad_batches=pad_batches):
		feed_dict = dict()
		if y_b is not None and not predict:
		feed_dict[self.labels[0]] = to_one_hot(
		y_b.flatten(), self.n_classes).reshape(-1, self.n_tasks,
		feed_dict[self.labels[0]] = to_one_hot(y_b.flatten(),
		self.n_classes).reshape(
		-1, self.n_tasks,
		self.n_classes)
		if X_b is not None:
		feed_dict[self.features[0]] = X_b
		@@ -137,11 +157,12 @@ class TensorGraphMultiTaskRegressor(TensorGraph):
		n_tasks,
		n_features,
		layer_sizes=[1000],
		weight_init_stddevs=[0.02, 0.02],
		bias_init_consts=[1.0, 1.0],
		weight_init_stddevs=0.02,
		bias_init_consts=1.0,
		weight_decay_penalty=0.0,
		weight_decay_penalty_type="l2",
		dropouts=[0.5],
		dropouts=0.5,
		activation_fns=tf.nn.relu,
		**kwargs):
		"""Create a TensorGraphMultiTaskRegressor.

		@@ -156,23 +177,39 @@ class TensorGraphMultiTaskRegressor(TensorGraph):
		number of features
		layer_sizes: list
		the size of each dense layer in the network. The length of this list determines the number of layers.
		weight_init_stddevs: list
		weight_init_stddevs: list or float
		the standard deviation of the distribution to use for weight initialization of each layer. The length
		of this list should equal len(layer_sizes)+1. The final element corresponds to the output layer.
		bias_init_consts: list
		Alternatively this may be a single value instead of a list, in which case the same value is used for every layer.
		bias_init_consts: list or float
		the value to initialize the biases in each layer to. The length of this list should equal len(layer_sizes)+1.
		The final element corresponds to the output layer.
		The final element corresponds to the output layer. Alternatively this may be a single value instead of a list,
		in which case the same value is used for every layer.
		weight_decay_penalty: float
		the magnitude of the weight decay penalty to use
		weight_decay_penalty_type: str
		the type of penalty to use for weight decay, either 'l1' or 'l2'
		dropouts: list
		dropouts: list or float
		the dropout probablity to use for each layer. The length of this list should equal len(layer_sizes).
		Alternatively this may be a single value instead of a list, in which case the same value is used for every layer.
		activation_fns: list or object
		the Tensorflow activation function to apply to each layer. The length of this list should equal
		len(layer_sizes). Alternatively this may be a single value instead of a list, in which case the
		same value is used for every layer.
		"""
		super(TensorGraphMultiTaskRegressor, self).__init__(
		mode='regression', **kwargs)
		self.n_tasks = n_tasks
		self.n_features = n_features
		n_layers = len(layer_sizes)
		if not isinstance(weight_init_stddevs, collections.Sequence):
		weight_init_stddevs = [weight_init_stddevs] * (n_layers + 1)
		if not isinstance(bias_init_consts, collections.Sequence):
		bias_init_consts = [bias_init_consts] * (n_layers + 1)
		if not isinstance(dropouts, collections.Sequence):
		dropouts = [dropouts] * n_layers
		if not isinstance(activation_fns, collections.Sequence):
		activation_fns = [activation_fns] * n_layers

		# Add the input features.

		@@ -181,12 +218,13 @@ class TensorGraphMultiTaskRegressor(TensorGraph):

		# Add the dense layers

		for size, weight_stddev, bias_const, dropout in zip(
		layer_sizes, weight_init_stddevs, bias_init_consts, dropouts):
		for size, weight_stddev, bias_const, dropout, activation_fn in zip(
		layer_sizes, weight_init_stddevs, bias_init_consts, dropouts,
		activation_fns):
		layer = Dense(
		in_layers=[prev_layer],
		out_channels=size,
		activation_fn=tf.nn.relu,
		activation_fn=activation_fn,
		weights_initializer=TFWrapper(
		tf.truncated_normal_initializer, stddev=weight_stddev),
		biases_initializer=TFWrapper(
		@@ -352,10 +390,9 @@ class TensorflowMultiTaskClassifier(TensorflowClassifier):
		batch_size x n_features.
		"""
		warnings.warn("TensorflowMultiTaskClassifier is deprecated. "
		"Will be removed in DeepChem 1.4.",
		DeprecationWarning)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		n_features = self.n_features
		with graph.as_default():
		with placeholder_scope:
		@@ -377,8 +414,8 @@ class TensorflowMultiTaskClassifier(TensorflowClassifier):
		assert n_layers > 0, 'Must have some layers defined.'

		label_placeholders = self.add_label_placeholders(graph, name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(graph,
		name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(
		graph, name_scopes)
		if training:
		graph.queue = tf.FIFOQueue(
		capacity=5,
		@@ -452,11 +489,10 @@ class TensorflowMultiTaskRegressor(TensorflowRegressor):
		batch_size x n_features.
		"""
		warnings.warn("TensorflowMultiTaskRegressor is deprecated. "
		"Will be removed in DeepChem 1.4.",
		DeprecationWarning)
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		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:
		mol_features = tf.placeholder(
		@@ -477,8 +513,8 @@ class TensorflowMultiTaskRegressor(TensorflowRegressor):
		assert n_layers > 0, 'Must have some layers defined.'

		label_placeholders = self.add_label_placeholders(graph, name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(graph,
		name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(
		graph, name_scopes)
		if training:
		graph.queue = tf.FIFOQueue(
		capacity=5,
		@@ -633,8 +669,7 @@ class TensorflowMultiTaskFitTransformRegressor(TensorflowMultiTaskRegressor):
		"""
		warnings.warn("TensorflowMultiTaskFitTransformRegressor "
		"is deprecated. "
		"Will be removed in DeepChem 1.4.",
		DeprecationWarning)
		"Will be removed in DeepChem 1.4.", DeprecationWarning)

		self.fit_transformers = fit_transformers
		self.n_evals = n_evals

deepchem/models/tensorflow_models/lr.py

+9 −10

Original line number	Diff line number	Diff line
		@@ -51,10 +51,9 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		batch_size x n_features.
		"""
		warnings.warn("TensorLogisticRegression is deprecated. "
		"Will be removed in DeepChem 1.4.",
		DeprecationWarning)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		n_features = self.n_features
		with graph.as_default():
		with placeholder_scope:
		@@ -66,8 +65,8 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		lg_list = []

		label_placeholders = self.add_label_placeholders(graph, name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(graph,
		name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(
		graph, name_scopes)
		if training:
		graph.queue = tf.FIFOQueue(
		capacity=5,
		@@ -98,8 +97,8 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		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)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		with placeholder_scope:
		for task in range(self.n_tasks):
		labels.append(

deepchem/models/tensorflow_models/progressive_joint.py

+5 −6

Original line number	Diff line number	Diff line
		@@ -44,8 +44,7 @@ class ProgressiveJointRegressor(TensorflowMultiTaskRegressor):
		List of standard-deviations for alpha in adapter layers.
		"""
		warnings.warn("ProgressiveJointRegressor is deprecated. "
		"Will be removed in DeepChem 1.4.",
		DeprecationWarning)
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		self.alpha_init_stddevs = alpha_init_stddevs
		super(ProgressiveJointRegressor, self).__init__(n_tasks, n_features,
		**kwargs)
		@@ -68,8 +67,8 @@ class ProgressiveJointRegressor(TensorflowMultiTaskRegressor):
		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(
		@@ -94,8 +93,8 @@ class ProgressiveJointRegressor(TensorflowMultiTaskRegressor):
		all_layers = {}
		for i in range(n_layers):
		for task in range(self.n_tasks):
		task_scope = TensorflowGraph.shared_name_scope("task%d" % task, graph,
		name_scopes)
		task_scope = TensorflowGraph.shared_name_scope(
		"task%d" % task, graph, name_scopes)
		print("Adding weights for task %d, layer %d" % (task, i))
		with task_scope as scope:
		if i == 0:

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