Merge branch 'master' into k_fold (c83d7cb3) · Commits · 钟慕尧 / deepchem

README.md

+1 −1

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		@@ -3,7 +3,7 @@
		[![Coverage Status](https://coveralls.io/repos/github/deepchem/deepchem/badge.svg?branch=master)](https://coveralls.io/github/deepchem/deepchem?branch=master)

		DeepChem aims to provide a high quality open-source toolchain that
		democratizes the use of deep-learning in drug discovery, materials science, and quantum chemistry.
		democratizes the use of deep-learning in drug discovery, materials science, quantum chemistry, and biology.

		### Table of contents:

deepchem/models/autoencoder_models/autoencoder.py

+5 −1

Original line number	Diff line number	Diff line
		@@ -2,7 +2,7 @@
		TODO(LESWING) Remove h5py dependency
		TODO(LESWING) Remove keras dependency and replace with functional keras API
		"""

		import warnings
		from deepchem.models import Model
		from deepchem.models.autoencoder_models.model import MoleculeVAE
		from deepchem.feat.one_hot import zinc_charset
		@@ -38,6 +38,8 @@ class TensorflowMoleculeEncoder(Model):
		latent_rep_size: int
		How large a 1D Vector for latent representation
		"""
		warnings.warn("TensorflowMoleculeEncoder Deprecated. "
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		super(TensorflowMoleculeEncoder, self).__init__(
		model_dir=model_dir, verbose=verbose)
		weights_file = os.path.join(model_dir, weights_file)
		@@ -105,6 +107,8 @@ class TensorflowMoleculeDecoder(Model):
		latent_rep_size: int
		How large a 1D Vector for latent representation
		"""
		warnings.warn("TensorflowMoleculeDecoder Deprecated. "
		"Will be removed in DeepChem 1.4.", DeprecationWarning)
		super(TensorflowMoleculeDecoder, self).__init__(
		model_dir=model_dir, verbose=verbose)
		weights_file = os.path.join(model_dir, weights_file)

deepchem/models/autoencoder_models/model.py

+5 −0

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		import warnings
		from keras import backend as K
		from keras import objectives
		from keras.layers import Input, Lambda
		@@ -12,6 +13,10 @@ class MoleculeVAE():

		autoencoder = None

		def __init__(self):
		warnings.warn("Deprecated. Will be removed in DeepChem 1.4.",
		DeprecationWarning)

		def create(self,
		charset_length,
		max_length=120,

deepchem/models/tensorflow_models/IRV.py

+8 −5

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		@@ -2,6 +2,7 @@ from __future__ import print_function
		from __future__ import division
		from __future__ import unicode_literals

		import warnings
		import time
		import numpy as np
		import tensorflow as tf
		@@ -58,7 +59,9 @@ class TensorflowMultiTaskIRVClassifier(TensorflowLogisticRegression):
		If not none, is used as random seed for tensorflow.

		"""

		warnings.warn("The TensorflowMultiTaskIRVClassifier is "
		"deprecated. Will be removed in DeepChem 1.4.",
		DeprecationWarning)
		self.n_tasks = n_tasks
		self.K = K
		self.n_features = 2 * self.K * self.n_tasks
		@@ -89,8 +92,8 @@ 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 = []
		@@ -104,8 +107,8 @@ 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

+16 −6

Original line number	Diff line number	Diff line
		@@ -4,6 +4,7 @@ from __future__ import print_function
		from __future__ import division
		from __future__ import unicode_literals

		import warnings
		import collections
		import pickle
		import os
		@@ -50,6 +51,8 @@ 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)
		self.graph = graph
		self.session = session
		self.name_scopes = name_scopes
		@@ -175,6 +178,9 @@ class TensorflowGraphModel(Model):
		seed: int
		If not none, is used as random seed for tensorflow.
		"""
		warnings.warn("TensorflowGraphModel is deprecated. "
		"Will be removed in DeepChem 1.4.", DeprecationWarning)

		# Save hyperparameters
		self.n_tasks = n_tasks
		self.n_features = n_features
		@@ -438,8 +444,8 @@ 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(
		@@ -482,11 +488,15 @@ class TensorflowGraphModel(Model):
		if train:
		if not self.train_graph.session:
		config = tf.ConfigProto(allow_soft_placement=True)
		#gpu memory growth option
		config.gpu_options.allow_growth = True
		self.train_graph.session = tf.Session(config=config)
		return self.train_graph.session
		else:
		if not self.eval_graph.session:
		config = tf.ConfigProto(allow_soft_placement=True)
		#gpu memory growth option
		config.gpu_options.allow_growth = True
		self.eval_graph.session = tf.Session(config=config)
		return self.eval_graph.session

		@@ -639,8 +649,8 @@ 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
		@@ -792,8 +802,8 @@ 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 = []

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