Loading deepchem/models/layers.py +41 −28 Original line number Diff line number Diff line Loading @@ -5,7 +5,7 @@ try: from collections.abc import Sequence as SequenceCollection except: from collections import Sequence as SequenceCollection from typing import Callable, Dict, List from typing import Callable, Dict, List, Tuple from tensorflow.keras import activations, initializers, backend from tensorflow.keras.layers import Dropout, BatchNormalization, Dense, Activation Loading Loading @@ -374,7 +374,14 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): """ Graph convolution layer used in MolGAN model. MolGAN is a WGAN type model for generation of small molecules. Not used directly, higher level layers like MultiGraphConvolutionLayer. Not used directly, higher level layers like MultiGraphConvolutionLayer use it. This layer performs basic convolution on one-hot encoded matrices containing atom and bond information. This layer also accepts three inputs for the case when convolution is performed more than once and results of previous convolution need to used. It was done in such a way to avoid creating another layer that accepts three inputs rather than two. The last input layer is so-called hidden_layer and it hold results of the convolution while first two are unchanged input tensors. References ---------- Loading @@ -383,11 +390,11 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): """ def __init__(self, units, activation="tanh", dropout_rate=0.0, edges=5, name="", units: int, activation: Callable = "tanh", dropout_rate: float = 0.0, edges: int = 5, name: str = "", **kwargs): """ Initialize this layer. Loading Loading @@ -472,6 +479,9 @@ class GraphAggregationLayer(tf.keras.layers.Layer): """ Graph Aggregation layer used in MolGAN model. MolGAN is a WGAN type model for generation of small molecules. Performs aggregation on tensor resulting from convolution layers. Given its simple nature it might be removed in future and to GraphEncoderLayer. References ---------- Loading @@ -480,17 +490,17 @@ class GraphAggregationLayer(tf.keras.layers.Layer): """ def __init__(self, units, activation="tanh", dropout_rate=0.0, name="", units: int = 128, activation: Callable = "tanh", dropout_rate: float = 0.0, name: str = "", **kwargs): """ Initialize the layer Parameters --------- units: int units: int, optional (default=128) Dimesion of dense layers used for aggregation activation: function, optional (default=Tanh) activation function used across model, default is Tanh Loading @@ -516,7 +526,7 @@ class GraphAggregationLayer(tf.keras.layers.Layer): Parameters ---------- inputs: list inputs: List Single tensor resulting from graph convolution layer training: bool Should this layer be run in training mode. Loading Loading @@ -548,7 +558,9 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): Multiple pass convolution layer used in MolGAN model. MolGAN is a WGAN type model for generation of small molecules. It takes outputs of previous convolution layer and uses them as inputs for the next one. It simplifies the overall framework. them as inputs for the next one. It simplifies the overall framework, but might be moved to GraphEncoderLayer in the future in order to reduce number of layers. References ---------- Loading @@ -557,18 +569,18 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): """ def __init__(self, units, activation="tanh", dropout_rate=0.0, edges=5, name="", units: Tuple = (128, 64), activation: Callable = "tanh", dropout_rate: float = 0.0, edges: int = 5, name: str = "", **kwargs): """ Initialize the layer Parameters --------- units: list, min_length=2 units: Tuple, optional (default=(128,64)), min_length=2 List of dimensions used by consecutive convolution layers. The more values the more convolution layers invoked. activation: function, optional (default=tanh) Loading Loading @@ -640,8 +652,9 @@ class GraphEncoderLayer(tf.keras.layers.Layer): """ Main learning layer used by MolGAN model. MolGAN is a WGAN type model for generation of small molecules. It role is to further simplify model. This layer can be manually built by stacking graph convolution layers followed by graph aggregation. It role is to further simplify model. This layer can be manually built by stacking graph convolution layers followed by graph aggregation. References ---------- Loading @@ -650,18 +663,18 @@ class GraphEncoderLayer(tf.keras.layers.Layer): """ def __init__(self, units, activation="tanh", dropout_rate=0.0, edges=5, name="", units: List = [(128, 64), 128], activation: Callable = "tanh", dropout_rate: float = 0.0, edges: int = 5, name: str = "", **kwargs): """ Initialize the layer. Parameters --------- units: list, length=2 units: List, optional (default=[(128, 64), 128]) List of units for MultiGraphConvolutionLayer and GraphAggregationLayer i.e. [(128,64),128] means two convolution layers dims = [128,64] followed by aggregation layer dims=128 Loading Loading
deepchem/models/layers.py +41 −28 Original line number Diff line number Diff line Loading @@ -5,7 +5,7 @@ try: from collections.abc import Sequence as SequenceCollection except: from collections import Sequence as SequenceCollection from typing import Callable, Dict, List from typing import Callable, Dict, List, Tuple from tensorflow.keras import activations, initializers, backend from tensorflow.keras.layers import Dropout, BatchNormalization, Dense, Activation Loading Loading @@ -374,7 +374,14 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): """ Graph convolution layer used in MolGAN model. MolGAN is a WGAN type model for generation of small molecules. Not used directly, higher level layers like MultiGraphConvolutionLayer. Not used directly, higher level layers like MultiGraphConvolutionLayer use it. This layer performs basic convolution on one-hot encoded matrices containing atom and bond information. This layer also accepts three inputs for the case when convolution is performed more than once and results of previous convolution need to used. It was done in such a way to avoid creating another layer that accepts three inputs rather than two. The last input layer is so-called hidden_layer and it hold results of the convolution while first two are unchanged input tensors. References ---------- Loading @@ -383,11 +390,11 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): """ def __init__(self, units, activation="tanh", dropout_rate=0.0, edges=5, name="", units: int, activation: Callable = "tanh", dropout_rate: float = 0.0, edges: int = 5, name: str = "", **kwargs): """ Initialize this layer. Loading Loading @@ -472,6 +479,9 @@ class GraphAggregationLayer(tf.keras.layers.Layer): """ Graph Aggregation layer used in MolGAN model. MolGAN is a WGAN type model for generation of small molecules. Performs aggregation on tensor resulting from convolution layers. Given its simple nature it might be removed in future and to GraphEncoderLayer. References ---------- Loading @@ -480,17 +490,17 @@ class GraphAggregationLayer(tf.keras.layers.Layer): """ def __init__(self, units, activation="tanh", dropout_rate=0.0, name="", units: int = 128, activation: Callable = "tanh", dropout_rate: float = 0.0, name: str = "", **kwargs): """ Initialize the layer Parameters --------- units: int units: int, optional (default=128) Dimesion of dense layers used for aggregation activation: function, optional (default=Tanh) activation function used across model, default is Tanh Loading @@ -516,7 +526,7 @@ class GraphAggregationLayer(tf.keras.layers.Layer): Parameters ---------- inputs: list inputs: List Single tensor resulting from graph convolution layer training: bool Should this layer be run in training mode. Loading Loading @@ -548,7 +558,9 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): Multiple pass convolution layer used in MolGAN model. MolGAN is a WGAN type model for generation of small molecules. It takes outputs of previous convolution layer and uses them as inputs for the next one. It simplifies the overall framework. them as inputs for the next one. It simplifies the overall framework, but might be moved to GraphEncoderLayer in the future in order to reduce number of layers. References ---------- Loading @@ -557,18 +569,18 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): """ def __init__(self, units, activation="tanh", dropout_rate=0.0, edges=5, name="", units: Tuple = (128, 64), activation: Callable = "tanh", dropout_rate: float = 0.0, edges: int = 5, name: str = "", **kwargs): """ Initialize the layer Parameters --------- units: list, min_length=2 units: Tuple, optional (default=(128,64)), min_length=2 List of dimensions used by consecutive convolution layers. The more values the more convolution layers invoked. activation: function, optional (default=tanh) Loading Loading @@ -640,8 +652,9 @@ class GraphEncoderLayer(tf.keras.layers.Layer): """ Main learning layer used by MolGAN model. MolGAN is a WGAN type model for generation of small molecules. It role is to further simplify model. This layer can be manually built by stacking graph convolution layers followed by graph aggregation. It role is to further simplify model. This layer can be manually built by stacking graph convolution layers followed by graph aggregation. References ---------- Loading @@ -650,18 +663,18 @@ class GraphEncoderLayer(tf.keras.layers.Layer): """ def __init__(self, units, activation="tanh", dropout_rate=0.0, edges=5, name="", units: List = [(128, 64), 128], activation: Callable = "tanh", dropout_rate: float = 0.0, edges: int = 5, name: str = "", **kwargs): """ Initialize the layer. Parameters --------- units: list, length=2 units: List, optional (default=[(128, 64), 128]) List of units for MultiGraphConvolutionLayer and GraphAggregationLayer i.e. [(128,64),128] means two convolution layers dims = [128,64] followed by aggregation layer dims=128 Loading
