encoder_in_out: Input and output tensors of encoders

In dongminjung/DeepPINCS: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning

Description

The graph convolutional network (GCN), recurrent neural network (RNN), convolutional neural network (CNN), and multilayer perceptron (MLP) are used as encoders. The last layer of the encoders is the fully connected layer. The units and activation can be vectors and the length of the vectors represents the number of layers.

Usage

gcn_in_out(max_atoms, feature_dim, gcn_units, gcn_activation,
    fc_units, fc_activation)

rnn_in_out(length_seq, fingerprint_size, embedding_layer = TRUE,
    num_tokens, embedding_dim, rnn_type, rnn_bidirectional,
    rnn_units, rnn_activation, fc_units, fc_activation)

cnn_in_out(length_seq, fingerprint_size, embedding_layer = TRUE,
    num_tokens, embedding_dim, cnn_filters, cnn_kernel_size, cnn_activation,
    fc_units, fc_activation)

mlp_in_out(length_seq, fingerprint_size, embedding_layer = TRUE,
    num_tokens, embedding_dim, fc_units, fc_activation)

Arguments

max_atoms	maximum number of atoms for gcn
feature_dim	dimension of atom features for gcn
gcn_units	dimensionality of the output space in the gcn layer
gcn_activation	activation of the gcn layer
fingerprint_size	the length of a fingerprint
embedding_layer	use the embedding layer if TRUE (default: TRUE)
embedding_dim	a non-negative integer for dimension of the dense embedding
length_seq	length of input sequences
num_tokens	total number of distinct strings
cnn_filters	dimensionality of the output space in the cnn layer
cnn_kernel_size	length of the 1D convolution window in the cnn layer
cnn_activation	activation of the cnn layer
rnn_type	"lstm" or "gru"
rnn_bidirectional	use the bidirectional wrapper for rnn if TRUE
rnn_units	dimensionality of the output space in the rnn layer
rnn_activation	activation of the rnn layer
fc_units	dimensionality of the output space in the fully connected layer
fc_activation	activation of the fully connected layer

Value

input and output tensors of encoders

Author(s)

Dongmin Jung

See Also

keras::layer_activation, keras::bidirectional, keras::layer_conv_1d, keras::layer_dense, keras::layer_dot, keras::layer_embedding, keras::layer_global_average_pooling_1d, keras::layer_input, keras::layer_lstm, keras::layer_gru, keras::layer_flatten

Examples

gcn_in_out(max_atoms = 50,
    feature_dim = 50,
    gcn_units = c(128, 64),
    gcn_activation = c("relu", "relu"),
    fc_units = c(10),
    fc_activation = c("relu"))

dongminjung/DeepPINCS documentation built on Dec. 20, 2021, 12:13 a.m.