Loading deepchem/models/tensorgraph/layers.py +25 −20 Original line number Diff line number Diff line Loading @@ -1405,21 +1405,37 @@ class LSTMStep(Layer): def __init__(self, output_dim, input_dim, init=initializations.glorot_uniform, inner_init=initializations.orthogonal, activation=activations.tanh, inner_activation=activations.hard_sigmoid, init_fn=initializations.glorot_uniform, inner_init_fn=initializations.orthogonal, activation_fn=activations.tanh, inner_activation_fn=activations.hard_sigmoid, **kwargs): """ Parameters ---------- output_dim: int Dimensionality of output vectors. input_dim: int Dimensionality of input vectors. init_fn: object TensorFlow initialization to use for W. inner_init_fn: object TensorFlow initialization to use for U. activation_fn: object TensorFlow activation to use for output. inner_activation_fn: object TensorFlow activation to use for inner steps. """ super(LSTMStep, self).__init__(**kwargs) self.init = init self.inner_init = inner_init self.init = init_fn self.inner_init = inner_init_fn self.output_dim = output_dim # No other forget biases supported right now. self.activation = activation self.inner_activation = inner_activation self.activation = activation_fn self.inner_activation = inner_activation_fn self.input_dim = input_dim def get_initial_states(self, input_shape): Loading Loading @@ -1637,14 +1653,7 @@ class IterRefLSTMEmbedding(Layer): than that from AttnLSTMEmbeding. """ def __init__(self, n_test, n_support, n_feat, max_depth, init='glorot_uniform', activation='linear', **kwargs): def __init__(self, n_test, n_support, n_feat, max_depth, **kwargs): """ Unlike the AttnLSTM model which only modifies the test vectors additively, this model allows for an additive update to be Loading @@ -1661,10 +1670,6 @@ class IterRefLSTMEmbedding(Layer): Number of input atom features max_depth: int Number of LSTM Embedding layers. init: string Type of weight initialization (from Keras) activation: string Activation type (ReLu/Linear/etc.) """ super(IterRefLSTMEmbedding, self).__init__(**kwargs) Loading Loading
deepchem/models/tensorgraph/layers.py +25 −20 Original line number Diff line number Diff line Loading @@ -1405,21 +1405,37 @@ class LSTMStep(Layer): def __init__(self, output_dim, input_dim, init=initializations.glorot_uniform, inner_init=initializations.orthogonal, activation=activations.tanh, inner_activation=activations.hard_sigmoid, init_fn=initializations.glorot_uniform, inner_init_fn=initializations.orthogonal, activation_fn=activations.tanh, inner_activation_fn=activations.hard_sigmoid, **kwargs): """ Parameters ---------- output_dim: int Dimensionality of output vectors. input_dim: int Dimensionality of input vectors. init_fn: object TensorFlow initialization to use for W. inner_init_fn: object TensorFlow initialization to use for U. activation_fn: object TensorFlow activation to use for output. inner_activation_fn: object TensorFlow activation to use for inner steps. """ super(LSTMStep, self).__init__(**kwargs) self.init = init self.inner_init = inner_init self.init = init_fn self.inner_init = inner_init_fn self.output_dim = output_dim # No other forget biases supported right now. self.activation = activation self.inner_activation = inner_activation self.activation = activation_fn self.inner_activation = inner_activation_fn self.input_dim = input_dim def get_initial_states(self, input_shape): Loading Loading @@ -1637,14 +1653,7 @@ class IterRefLSTMEmbedding(Layer): than that from AttnLSTMEmbeding. """ def __init__(self, n_test, n_support, n_feat, max_depth, init='glorot_uniform', activation='linear', **kwargs): def __init__(self, n_test, n_support, n_feat, max_depth, **kwargs): """ Unlike the AttnLSTM model which only modifies the test vectors additively, this model allows for an additive update to be Loading @@ -1661,10 +1670,6 @@ class IterRefLSTMEmbedding(Layer): Number of input atom features max_depth: int Number of LSTM Embedding layers. init: string Type of weight initialization (from Keras) activation: string Activation type (ReLu/Linear/etc.) """ super(IterRefLSTMEmbedding, self).__init__(**kwargs) Loading
