94 lines (90 with data), 15.1 kB

Äēķ:Ćdocutils.nodesĒĆdocumentĒďĒ)ĀĒ}Ē(Ć	rawsourceĒĆĒĆchildrenĒ]ĒhĆsectionĒďĒ)ĀĒ}Ē(hhh]Ē(hĆtitleĒďĒ)ĀĒ}Ē(hĆ
ConfigurationĒh]ĒhĆTextĒďĒĆ
ConfigurationĒÖĒĀĒ}Ē(hhĆparentĒhh
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attributesĒ}Ē(ĆidsĒ]ĒĆclassesĒ]ĒĆnamesĒ]ĒĆdupnamesĒ]ĒĆbackrefsĒ]ĒuĆtagnameĒhhhh
hhĆU/Users/futianfan/Downloads/spring2020/DeepPurpose/docs/source/notes/configuration.rstĒhKubhĆ	paragraphĒďĒ)ĀĒ}Ē(hĆ^**generate_config** generate all the configuration that can be used in learning and inference.Ēh]Ē(hĆstrongĒďĒ)ĀĒ}Ē(hĆ**generate_config**Ēh]ĒhĆgenerate_configĒÖĒĀĒ}Ē(hhhh3ubah}Ē(h]Ēh!]Ēh#]Ēh%]Ēh']Ēuh)h1hh-ubhĆK generate all the configuration that can be used in learning and inference.ĒÖĒĀĒ}Ē(hĆK generate all the configuration that can be used in learning and inference.Ēhh-h
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literal_blockĒďĒ)ĀĒ}Ē(hXĮutils.generate_config(
       drug_encoding,
       target_encoding,
       result_folder = "./result/",
       input_dim_drug = 1024,
       input_dim_protein = 8420,
       hidden_dim_drug = 256,
       hidden_dim_protein = 256,
       cls_hidden_dims = [1024, 1024, 512],
       mlp_hidden_dims_drug = [1024, 256, 64],
       mlp_hidden_dims_target = [1024, 256, 64],
       batch_size = 256,
       train_epoch = 10,
       test_every_X_epoch = 20,
       LR = 1e-4,
       transformer_emb_size_drug = 128,
       transformer_intermediate_size_drug = 512,
       transformer_num_attention_heads_drug = 8,
       transformer_n_layer_drug = 8,
       transformer_emb_size_target = 128,
       transformer_intermediate_size_target = 512,
       transformer_num_attention_heads_target = 8,
       transformer_n_layer_target = 4,
       transformer_dropout_rate = 0.1,
       transformer_attention_probs_dropout = 0.1,
       transformer_hidden_dropout_rate = 0.1,
       mpnn_hidden_size = 50,
       mpnn_depth = 3,
       cnn_drug_filters = [32,64,96],
       cnn_drug_kernels = [4,6,8],
       cnn_target_filters = [32,64,96],
       cnn_target_kernels = [4,8,12],
       rnn_Use_GRU_LSTM_drug = 'GRU',
       rnn_drug_hid_dim = 64,
       rnn_drug_n_layers = 2,
       rnn_drug_bidirectional = True,
       rnn_Use_GRU_LSTM_target = 'GRU',
       rnn_target_hid_dim = 64,
       rnn_target_n_layers = 2,
       rnn_target_bidirectional = True
       )Ēh]ĒhXĮutils.generate_config(
       drug_encoding,
       target_encoding,
       result_folder = "./result/",
       input_dim_drug = 1024,
       input_dim_protein = 8420,
       hidden_dim_drug = 256,
       hidden_dim_protein = 256,
       cls_hidden_dims = [1024, 1024, 512],
       mlp_hidden_dims_drug = [1024, 256, 64],
       mlp_hidden_dims_target = [1024, 256, 64],
       batch_size = 256,
       train_epoch = 10,
       test_every_X_epoch = 20,
       LR = 1e-4,
       transformer_emb_size_drug = 128,
       transformer_intermediate_size_drug = 512,
       transformer_num_attention_heads_drug = 8,
       transformer_n_layer_drug = 8,
       transformer_emb_size_target = 128,
       transformer_intermediate_size_target = 512,
       transformer_num_attention_heads_target = 8,
       transformer_n_layer_target = 4,
       transformer_dropout_rate = 0.1,
       transformer_attention_probs_dropout = 0.1,
       transformer_hidden_dropout_rate = 0.1,
       mpnn_hidden_size = 50,
       mpnn_depth = 3,
       cnn_drug_filters = [32,64,96],
       cnn_drug_kernels = [4,6,8],
       cnn_target_filters = [32,64,96],
       cnn_target_kernels = [4,8,12],
       rnn_Use_GRU_LSTM_drug = 'GRU',
       rnn_drug_hid_dim = 64,
       rnn_drug_n_layers = 2,
       rnn_drug_bidirectional = True,
       rnn_Use_GRU_LSTM_target = 'GRU',
       rnn_target_hid_dim = 64,
       rnn_target_n_layers = 2,
       rnn_target_bidirectional = True
       )ĒÖĒĀĒ}Ē(hhhhNubah}Ē(h]Ēh!]Ēh#]Ēh%]Ēh']ĒĆ	xml:spaceĒĆpreserveĒĆforceĒČĆlanguageĒĆpythonĒĆhighlight_argsĒ}Ēuh)hLhh*hK	hhh
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