Added ChemNet models with tests (7e63235c) · Commits · 钟慕尧 / deepchem

deepchem/feat/smiles_featurizers.py

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		@@ -174,8 +174,8 @@ class SmilesToImage(Featurizer):
		self.max_len = max_len
		self.res = res
		self.img_spec = img_spec
		self.embed = int(dims * res / 2)
		super(SmilesToImage, self).__init__(**kwargs)
		self.embed = int(img_size * res / 2)
		super(SmilesToImage, self).__init__()

		def _featurize(self, mol):
		"""Featurizes a single SMILE sequence."""

deepchem/models/init.py

+1 −0

Original line number	Diff line number	Diff line
		@@ -29,6 +29,7 @@ from deepchem.models.tensorgraph.sequential import Sequential
		from deepchem.models.tensorgraph.models.sequence_dnn import SequenceDNN
		from deepchem.models.tensorgraph.models.ontology import OntologyModel, OntologyNode, create_gene_ontology
		from deepchem.models.tensorgraph.models.atomic_conv import AtomicConvModel
		from deepchem.models.tensorgraph.models.chemnet_models import Smiles2Vec, ChemCeption

		#################### Compatibility imports for renamed TensorGraph models. Remove below with DeepChem 3.0. ####################

deepchem/models/tensorgraph/chemnet_layers.py

0 → 100644

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		"""
		Contains implementations of layers used in ChemCeption and Smiles2Vec models.
		"""

		from __future__ import division
		from __future__ import unicode_literals

		__author__ = "Vignesh Ram Somnath"
		__license__ = "MIT"

		import numpy as np
		import tensorflow as tf

		from deepchem.models.tensorgraph.layers import KerasLayer
		from tensorflow.keras.layers import Conv2D, Concatenate, ReLU, Add, MaxPool2D


		class SmilesEmbedding(KerasLayer):

		def __init__(self, charset_size, embedding_dim, **kwargs):
		"""
		Parameters
		----------
		charset_size: int,
		Number of elements in the character to index dictionary
		embedding_dim: int,
		Dimensionality of the embedding
		"""
		super(SmilesEmbedding, self).__init__(**kwargs)
		self.charset_size = charset_size
		self.embedding_dim = embedding_dim

		def _build_layer(self):
		return tf.keras.layers.Embedding(
		input_dim=self.charset_size, output_dim=self.embedding_dim)


		class Stem(tf.keras.layers.Layer):

		def __init__(self, num_filters, **kwargs):
		"""
		Parameters
		----------
		num_filters: int,
		Number of convolutional filters
		"""
		self.num_filters = num_filters
		self._build_layer_components()
		super(Stem, self).__init__(**kwargs)

		def _build_layer_components(self):
		"""Builds the layers components and set _layers attribute."""
		self.conv_layer = Conv2D(
		filters=self.num_filters,
		kernel_size=4,
		strides=2,
		activation=tf.nn.relu)

		self.activation_layer = ReLU()
		self._layers = [self.conv_layer, self.activation_layer]

		def call(self, inputs):
		conv1 = self.conv_layer(inputs)
		return self.activation_layer(conv1)


		class InceptionResnetA(tf.keras.layers.Layer):

		def __init__(self, num_filters, input_dim, **kwargs):
		"""
		Parameters
		----------
		num_filters: int,
		Number of convolutional filters
		input_dim: int,
		Number of channels in the input.
		"""
		self.num_filters = num_filters
		self.input_dim = input_dim
		self._build_layer_components()
		super(InceptionResnetA, self).__init__(**kwargs)

		def _build_layer_components(self):
		"""Builds the layers components and set _layers attribute."""
		self.conv_block1 = [
		Conv2D(
		self.num_filters,
		kernel_size=(1, 1),
		strides=1,
		padding="same",
		activation=tf.nn.relu)
		]

		self.conv_block2 = [
		Conv2D(
		filters=self.num_filters,
		kernel_size=(1, 1),
		strides=1,
		activation=tf.nn.relu,
		padding="same")
		]

		self.conv_block2.append(
		Conv2D(
		filters=self.num_filters,
		kernel_size=(3, 3),
		strides=1,
		activation=tf.nn.relu,
		padding="same"))

		self.conv_block3 = [
		Conv2D(
		filters=self.num_filters,
		kernel_size=1,
		strides=1,
		activation=tf.nn.relu,
		padding="same")
		]

		self.conv_block3.append(
		Conv2D(
		filters=int(self.num_filters * 1.5),
		kernel_size=(3, 3),
		strides=1,
		activation=tf.nn.relu,
		padding="same"))
		self.conv_block3.append(
		Conv2D(
		filters=self.num_filters * 2,
		kernel_size=(3, 3),
		strides=1,
		activation=tf.nn.relu,
		padding="same"))

		self.conv_block4 = [
		Conv2D(
		filters=self.input_dim,
		kernel_size=(1, 1),
		strides=1,
		padding="same")
		]

		self.concat_layer = Concatenate()
		self.add_layer = Add()
		self.activation_layer = ReLU()

		self._layers = self.conv_block1 + self.conv_block2 + self.conv_block3 + self.conv_block4
		self._layers.extend(
		[self.concat_layer, self.add_layer, self.activation_layer])

		def call(self, inputs):
		conv1 = inputs
		for layer in self.conv_block1:
		conv1 = layer(conv1)

		conv2 = inputs
		for layer in self.conv_block2:
		conv2 = layer(conv2)

		conv3 = inputs
		for layer in self.conv_block3:
		conv3 = layer(conv3)

		concat_conv = self.concat_layer([conv1, conv2, conv3])

		conv4 = concat_conv
		for layer in self.conv_block4:
		conv4 = layer(conv4)
		output = self.add_layer([conv4, inputs])
		return self.activation_layer(output)


		class InceptionResnetB(tf.keras.layers.Layer):

		def __init__(self, num_filters, input_dim, **kwargs):
		"""
		Parameters
		----------
		num_filters: int,
		Number of convolutional filters
		input_dim: int,
		Number of channels in the input.
		"""
		self.num_filters = num_filters
		self.input_dim = input_dim
		self._build_layer_components()
		super(InceptionResnetB, self).__init__(**kwargs)

		def _build_layer_components(self):
		"""Builds the layers components and set _layers attribute."""
		self.conv_block1 = [
		Conv2D(
		self.num_filters,
		kernel_size=1,
		strides=1,
		padding="same",
		activation=tf.nn.relu)
		]

		self.conv_block2 = [
		Conv2D(
		filters=self.num_filters,
		kernel_size=(1, 1),
		strides=1,
		activation=tf.nn.relu,
		padding="same")
		]
		self.conv_block2.append(
		Conv2D(
		filters=int(self.num_filters * 1.25),
		kernel_size=(1, 7),
		strides=1,
		activation=tf.nn.relu,
		padding="same"))
		self.conv_block2.append(
		Conv2D(
		filters=int(self.num_filters * 1.5),
		kernel_size=(7, 1),
		strides=1,
		activation=tf.nn.relu,
		padding="same"))

		self.conv_block3 = [
		Conv2D(
		filters=self.input_dim, kernel_size=1, strides=1, padding="same")
		]

		self.concat_layer = Concatenate()
		self.add_layer = Add()
		self.activation_layer = ReLU()

		self._layers = self.conv_block1 + self.conv_block2 + self.conv_block3
		self._layers.extend(
		[self.concat_layer, self.add_layer, self.activation_layer])

		def call(self, inputs):
		conv1 = inputs
		for layer in self.conv_block1:
		conv1 = layer(conv1)

		conv2 = inputs
		for layer in self.conv_block2:
		conv2 = layer(conv2)

		concat_conv = self.concat_layer([conv1, conv2])

		conv3 = concat_conv
		for layer in self.conv_block3:
		conv3 = layer(conv3)

		output = self.add_layer([conv3, inputs])
		output = self.activation_layer(output)
		return output


		class InceptionResnetC(tf.keras.layers.Layer):

		def __init__(self, num_filters, input_dim, **kwargs):
		"""
		Parameters
		----------
		num_filters: int,
		Number of convolutional filters
		input_dim: int,
		Number of channels in the input.
		"""
		self.num_filters = num_filters
		self.input_dim = input_dim
		self._build_layer_components()
		super(InceptionResnetC, self).__init__(**kwargs)

		def _build_layer_components(self):
		"""Builds the layers components and set _layers attribute."""
		self.conv_block1 = [
		Conv2D(
		self.num_filters,
		kernel_size=(1, 1),
		strides=1,
		padding="same",
		activation=tf.nn.relu)
		]

		self.conv_block2 = [
		Conv2D(
		filters=self.num_filters,
		kernel_size=1,
		strides=1,
		activation=tf.nn.relu,
		padding="same")
		]
		self.conv_block2.append(
		Conv2D(
		filters=int(self.num_filters * 1.16),
		kernel_size=(1, 3),
		strides=1,
		activation=tf.nn.relu,
		padding="same"))
		self.conv_block2.append(
		Conv2D(
		filters=int(self.num_filters * 1.33),
		kernel_size=(3, 1),
		strides=1,
		activation=tf.nn.relu,
		padding="same"))

		self.conv_block3 = [
		Conv2D(
		filters=self.input_dim,
		kernel_size=(1, 1),
		strides=1,
		padding="same")
		]

		self.concat_layer = Concatenate()
		self.add_layer = Add()
		self.activation_layer = ReLU()

		self._layers = self.conv_block1 + self.conv_block2 + self.conv_block3
		self._layers.extend(
		[self.concat_layer, self.add_layer, self.activation_layer])

		def call(self, inputs):
		conv1 = inputs
		for layer in self.conv_block1:
		conv1 = layer(conv1)

		conv2 = inputs
		for layer in self.conv_block2:
		conv2 = layer(conv2)

		concat_conv = self.concat_layer([conv1, conv2])

		conv3 = concat_conv
		for layer in self.conv_block3:
		conv3 = layer(conv3)

		output = self.add_layer([conv3, inputs])
		output = self.activation_layer(output)
		return output


		class ReductionA(tf.keras.layers.Layer):

		def __init__(self, num_filters, **kwargs):
		"""
		Parameters
		----------
		num_filters: int,
		Number of convolutional filters
		"""
		self.num_filters = num_filters
		self._build_layer_components()
		super(ReductionA, self).__init__(**kwargs)

		def _build_layer_components(self):
		"""Builds the layers components and set _layers attribute."""
		self.max_pool1 = MaxPool2D(pool_size=(3, 3), strides=2, padding="valid")

		self.conv_block1 = [
		Conv2D(
		int(self.num_filters * 1.5),
		kernel_size=(3, 3),
		strides=2,
		padding="valid",
		activation=tf.nn.relu)
		]

		self.conv_block2 = [
		Conv2D(
		filters=self.num_filters,
		kernel_size=1,
		strides=1,
		activation=tf.nn.relu,
		padding="same")
		]
		self.conv_block2.append(
		Conv2D(
		filters=self.num_filters,
		kernel_size=3,
		strides=1,
		activation=tf.nn.relu,
		padding="same"))
		self.conv_block2.append(
		Conv2D(
		filters=int(self.num_filters * 1.5),
		kernel_size=3,
		strides=2,
		activation=tf.nn.relu,
		padding="valid"))

		self.concat_layer = Concatenate()
		self.activation_layer = ReLU()

		self._layers = self.conv_block1 + self.conv_block2
		self._layers.extend(
		[self.max_pool1, self.concat_layer, self.activation_layer])

		def call(self, inputs):
		maxpool1 = self.max_pool1(inputs)
		conv1 = inputs
		for layer in self.conv_block1:
		conv1 = layer(conv1)

		conv2 = inputs
		for layer in self.conv_block2:
		conv2 = layer(conv2)

		output = self.concat_layer([maxpool1, conv1, conv2])
		output = self.activation_layer(output)
		return output


		class ReductionB(tf.keras.layers.Layer):

		def __init__(self, num_filters, **kwargs):
		"""
		Parameters
		----------
		num_filters: int,
		Number of convolutional filters
		"""
		self.num_filters = num_filters
		self._build_layer_components()
		super(ReductionB, self).__init__(**kwargs)

		def _build_layer_components(self):
		"""Builds the layers components and set _layers attribute."""
		self.max_pool1 = MaxPool2D(pool_size=(3, 3), strides=2, padding="valid")

		self.conv_block1 = [
		Conv2D(
		self.num_filters,
		kernel_size=1,
		strides=1,
		padding="same",
		activation=tf.nn.relu)
		]
		self.conv_block1.append(
		Conv2D(
		int(self.num_filters * 1.5),
		kernel_size=3,
		strides=2,
		padding="valid",
		activation=tf.nn.relu))

		self.conv_block2 = [
		Conv2D(
		filters=self.num_filters,
		kernel_size=1,
		strides=1,
		activation=tf.nn.relu,
		padding="same")
		]
		self.conv_block2.append(
		Conv2D(
		filters=int(self.num_filters * 1.125),
		kernel_size=3,
		strides=2,
		activation=tf.nn.relu,
		padding="valid"))

		self.conv_block3 = [
		Conv2D(
		filters=self.num_filters,
		kernel_size=1,
		strides=1,
		activation=tf.nn.relu,
		padding="same")
		]
		self.conv_block3.append(
		Conv2D(
		filters=int(self.num_filters * 1.125),
		kernel_size=(3, 1),
		strides=1,
		activation=tf.nn.relu,
		padding="same"))

		self.conv_block3.append(
		Conv2D(
		filters=int(self.num_filters * 1.25),
		kernel_size=(3, 3),
		strides=2,
		activation=tf.nn.relu,
		padding="valid"))

		self.concat_layer = Concatenate()
		self.activation_layer = ReLU()

		self._layers = self.conv_block1 + self.conv_block2 + self.conv_block3
		self._layers.extend(
		[self.max_pool1, self.concat_layer, self.activation_layer])

		def call(self, inputs):
		maxpool1 = self.max_pool1(inputs)
		conv1 = inputs
		for layer in self.conv_block1:
		conv1 = layer(conv1)

		conv2 = inputs
		for layer in self.conv_block2:
		conv2 = layer(conv2)

		conv3 = inputs
		for layer in self.conv_block3:
		conv3 = layer(conv3)

		concat = self.concat_layer([maxpool1, conv1, conv2, conv3])
		output = self.activation_layer(concat)
		return output

deepchem/models/tensorgraph/models/chemnet_models.py

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deepchem/models/tensorgraph/tests/chembl_25_small.csv

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