Functions — github.com/alex-hh/deep-protein-generation

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Functions44 in github.com/alex-hh/deep-protein-generation

⨍ Functions 44 ◇ Types & classes 3

↓ 4 callersFunction_activation

A more general activation function, allowing to use just string (for prelu, leakyrelu and elu) and to add BN before applying the activation

utils/layers.py:9

↓ 4 callersMethoddecode

(self, z, remove_gaps=False, sample_func=None, conditions=None)

models/protcnn.py:115

↓ 4 callersFunctionload_gzdata

(filename, one_hot=True)

↓ 4 callersFunctionone_hot_generator

(seqlist, conditions=None, batch_size=32, padding=504, shuffle=True, alphabet=aa_letter

utils/data_loaders.py:31

↓ 3 callersFunctionConv1D

BN after AtrousConvolution1D and BEFORE activation function

utils/layers.py:51

↓ 3 callersFunctionto_one_hot

(seqlist, alphabet=aa_letters)

utils/data_loaders.py:12

↓ 2 callersMethodload_weights

(self, file='generative_models/weights/default.h5')

models/protcnn.py:90

↓ 2 callersFunctionoutput_fasta

(names, seqs, filepath)

↓ 2 callersFunctionright_pad

(seqlist, target_length=None)

utils/data_loaders.py:22

↓ 2 callersMethodsave_weights

(self, file='generative_models/weights/default.h5')

models/protcnn.py:95

↓ 2 callersFunctionseq_to_one_hot

(sequence, aa_key)

utils/data_loaders.py:6

↓ 2 callersFunctionto_string

(seqmat, remove_gaps=True)

utils/decoding.py:6

↓ 1 callersFunctionBatchNorm

(momentum=0.99, training=True)

utils/layers.py:42

↓ 1 callersFunctionDeconv1D

`strides` is the upsampling factor

utils/layers.py:83

↓ 1 callersMethod__init__

(self, original_dim=504, latent_dim=50, clipnorm=5, lr=0.001, n_conditions=0,

models/vaes.py:26

↓ 1 callersFunction_decode_ar

(generator, z, remove_gaps=False, alphabet_size=21, sample_func=None, conditions=None)

utils/decoding.py:24

↓ 1 callersFunction_decode_nonar

(generator, z, remove_gaps=False, alphabet_size=21, conditions=None)

utils/decoding.py:19

↓ 1 callersFunctioncnn_encoder

(original_dim, latent_dim, nchar=21, num_filters=21, kernel_size=2, BN=True, a

models/encoders.py:35

↓ 1 callersFunctioncond_mlp

(out_dim, n_layers=2, h=6, n_conditions=3, activation='prelu')

models/encoders.py:64

↓ 1 callersFunctionfc_decoder

(latent_dim, seqlen, decoder_hidden=[250], decoder_dropout=[0.], alphabet_size=21, n_conditions

models/decoders.py:13

↓ 1 callersFunctionfc_encoder

(seqlen, latent_dim, alphabet_size=21, encoder_hidden=[250,250,250], encoder_dropout=[0.7,0.,0.

models/encoders.py:10

↓ 1 callersMethodgenerate_variants_luxA

(self, num_samples, posterior_var_scale=1., temperature=0., solubility_level=No

models/protcnn.py:122

↓ 1 callersFunctiongreedy_decode

utils/decoding.py:16

↓ 1 callersFunctionluxa_batch_conds

(n_samples, solubility_level)

models/protcnn.py:27

↓ 1 callersFunctionmain

(weights_file, msa=True, num_samples=500, output_file=None, model_kwargs=None, temperature=0., poster

scripts/generate_variants.py:10

↓ 1 callersFunctionmain

(weights_file, msa=True, num_samples=3000, output_file=None, model_kwargs=None)

scripts/generate_from_prior.py:8

↓ 1 callersMethodprior_sample

(self, n_samples=1, mean=0, stddev=1, remove_gaps=False, batch_size=5000)

models/protcnn.py:100

↓ 1 callersFunctionread_gzfasta

(filepath, output_arr=False, encoding='utf-8')

↓ 1 callersFunctionrecurrent_sequence_decoder

(latent_dim, seqlen, ncell=512, alphabet_size=21, project_x=True,

models/decoders.py:55

↓ 1 callersFunctionsampler

(latent_dim, epsilon_std=1)

models/protcnn.py:21

↓ 1 callersFunctiontemp_sample_outputs

(preds, temperature=1.0)

utils/decoding.py:50

↓ 1 callersFunctionupsampler

(latent_vector, low_res_dim, min_deconv_dim=21, n_deconv=3, kernel_size=2, BN=True, dropout=None

models/decoders.py:41

(self, latent_dim=10, original_dim=360, n_conditions=0., activation='relu',

models/vaes.py:8

(self, n_conditions=0, autoregressive=True, lr=0.001, clipnorm=0., clipvalue=0., metrics=['ac

models/protcnn.py:36

(prots_oh, reconstructed)

utils/metrics.py:5

Functionbatch_temp_sample

(preds, temperature=1.0)

utils/decoding.py:43

Functionbatchnorm

(x, momentum=momentum, training=training)

utils/layers.py:43

Functioncompress_file

utils/layers.py:14

Functiongreedy_decode_1d

utils/decoding.py:10

models/protcnn.py:76

Functionread_fasta

(filepath, output_arr=False)

models/protcnn.py:79

Methodxent_loss

models/protcnn.py:73