Difference between revisions of "Team:Heidelberg/Sandbox1025"
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At least the two higher levels probably only work in python, but maybe an interactive version of what happens during one iteration is possible. A final more comfortable version of the script was started. | At least the two higher levels probably only work in python, but maybe an interactive version of what happens during one iteration is possible. A final more comfortable version of the script was started. | ||
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Experimentation with elements of a Predcel model based on distributions instead of scalars were started. The idea is that a population of phages does not have one fitness between 0 and 1 but rather has individuals that have different fitness values. In this more complex model the concentrations have to be calculated for each phage fitness value, depending on the amount of phages that have that fitness value. The fitness distribution is changed by mutation and by selection. The first naive approach for mutation was programmed. It simply substracts a given percentage of the difference between the amount of a fitness value and the mean amount from the amount of a fitness value. Obviously this is oversimplified and will therefore be replaced by a model based on the idea that every sequence that mutates gets better or worse with normally distributed changes. | Experimentation with elements of a Predcel model based on distributions instead of scalars were started. The idea is that a population of phages does not have one fitness between 0 and 1 but rather has individuals that have different fitness values. In this more complex model the concentrations have to be calculated for each phage fitness value, depending on the amount of phages that have that fitness value. The fitness distribution is changed by mutation and by selection. The first naive approach for mutation was programmed. It simply substracts a given percentage of the difference between the amount of a fitness value and the mean amount from the amount of a fitness value. Obviously this is oversimplified and will therefore be replaced by a model based on the idea that every sequence that mutates gets better or worse with normally distributed changes. | ||
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Revision as of 21:02, 20 October 2017
Optopace
No entry for this subproject this week.Software
KW34 ===== Word2Vec Embeddings on Proteinsequences --------------------- We rewrote a word2vec implementation from tensorflows tutorials that implements Efficient Estimation of Word Representations in Vector Space, ICLR 2013 (Mikolov, et. al.). The model is a skipgram model with negative sample that uses custom ops written in C. The code was adapted to our needs, mainly by changing datatypes in the C kernels and writing a different evaluation function based on predicting the nearest words to the most frequent words instead of using analogies. Two new datasets were generated based on both swissprot and uniprot. Training of 4mer embeddings in 50, 100 and 200 dimensions were started but have not been calculated yet. Visualisation of the first checkpoints is possible via tensorboard [Visualisation of an example embedding via tensorboard](170820ai-vistestemb). IMPLEMENTATION OF SQUEEZENET Architecture --------------------------------- With implamentation of a new architecture based on Sequeeze-net (Forrest N. Iandola, 2017), relying on 1x1 convolutions we were able to grasp the 299 as well as the 637 classes dataset. The new model architecture looks the following: - InputLayer model_valid/input_layer_valid: (64, 20, 1000, 1) - PadLayer model_valid/block1/pad_layer_valid: paddings:[[0, 0], [0, 0], [3, 3], [0, 0]] mode:CONSTANT - Conv2dLayer model_valid/block1/cnn_layer_valid: shape:[20, 7, 1, 128] strides:[1, 5, 1, 1] pad:VALID act:prelu - Conv1dLayer model_valid/block2/cnn_layer_valid: shape:[6, 128, 128] stride:1 pad:SAME act:prelu - Conv1dLayer model_valid/1x1_I/1x1_valid: shape:[1, 128, 64] stride:1 pad:SAME act:prelu - BatchNormLayer model_valid/1x1_I/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/block3/cnn_layer_valid: shape:[5, 64, 256] stride:1 pad:SAME act:prelu - PoolLayer model_valid/block3/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool - BatchNormLayer model_valid/block3/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/block4/cnn_layer_valid: shape:[5, 256, 256] stride:1 pad:SAME act:prelu - PoolLayer model_valid/block4/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool - BatchNormLayer model_valid/block4/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/1x1_II/1x1_valid: shape:[1, 256, 128] stride:1 pad:SAME act:prelu - BatchNormLayer model_valid/1x1_II/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/block5/cnn_layer_valid: shape:[5, 128, 256] stride:1 pad:SAME act:prelu - PoolLayer model_valid/block5/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool - BatchNormLayer model_valid/block5/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/block6/cnn_layer_valid: shape:[5, 256, 512] stride:1 pad:SAME act:prelu - PoolLayer model_valid/block6/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool - BatchNormLayer model_valid/block6/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/1x1_III/1x1_valid: shape:[1, 512, 256] stride:1 pad:SAME act:prelu - BatchNormLayer model_valid/1x1_III/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/block7/cnn_layer_valid: shape:[5, 256, 516] stride:1 pad:SAME act:prelu - PoolLayer model_valid/block7/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool - BatchNormLayer model_valid/block7/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/block8/cnn_layer_valid: shape:[5, 516, 1024] stride:1 pad:SAME act:prelu - PoolLayer model_valid/block8/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool - BatchNormLayer model_valid/block8/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/1x1_IV/cnn_layer_valid: shape:[1, 1024, 512] stride:1 pad:SAME act:prelu - BatchNormLayer model_valid/1x1_IV/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/block9/cnn_layer_valid: shape:[5, 512, 1024] stride:1 pad:SAME act:prelu - PoolLayer model_valid/block9/pool_layer_valid: ksize:[2] strides:[2] padding:VALID pool:pool - BatchNormLayer model_valid/block9/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - Conv1dLayer model_valid/outlayer/cnn_layer_valid: shape:[1, 1024, 637] stride:1 pad:SAME act:prelu - BatchNormLayer model_valid/outlayer/batchnorm_layer_valid: decay:0.900000 epsilon:0.000010 act:identity is_train:False - MeanPool1d global_avg_pool: filter_size:[7] strides:1 padding:valid The architecture is fully convolutional, ending in an average pooling layer as outlayer, with the channels dimension corresponding to the number of classes. All inputs were 1-hot encoded and zero padded to a boxsize of 1000 positions.Optopace
No entry for this subproject this week.Software
KW35 ====== Performance of the Squeezenet Architecture - singlelabel 599 ------- The model was run successfully on the old 599 classes dataset. Parameters: lr = E-2, batchsize=64, epsilon=0.1 [ROC](DeeProtein_TFRECORDS_PURECONV_1x1tuned_750k_restored750kfull_sce_adam_1dconv637_1000_one_hot_padded_64_0.001_0.1.roc_16.svg) [Precision](DeeProtein_TFRECORDS_PURECONV_1x1tuned_750k_restored750kfull_sce_adam_1dconv637_1000_one_hot_padded_64_0.001_0.1.precision_16.svg) Performance of the Squeezenet Architecture - singlelabel 679 ------- The model was run successfully on the 679 classes dataset. Parameters: lr = E-5, batchsize=64, epsilon=0.1 [ROC](DeeProtein_TFRECORDS_PURECONV_1x1tuned_restored679_sce_adam_1dconv_EC_679_1000_one_hot_padded_64_0.001_0.1.roc_9.svg) [Precision](DeeProtein_TFRECORDS_PURECONV_1x1tuned_restored679_sce_adam_1dconv_EC_679_1000_one_hot_padded_64_0.001_0.1.precision_9.svg) Performance of the Squeezenet Architecture - Multilabel 1084 ------- The model was run successfully on the 1084 GO-classes dataset. Parameters: lr = E-3, batchsize=64, epsilon=0.1 [ROC](DeeProtein_TFRECORDS_PURECONV_1x1LARGE_MULTI_restored1084_sce_adam_1dconv_EC_1084_1000_one_hot_padded_64_0.0001_0.1.roc_39.svg) [Precision](DeeProtein_TFRECORDS_PURECONV_1x1LARGE_MULTI_restored1084_sce_adam_1dconv_EC_1084_1000_one_hot_padded_64_0.0001_0.1.precision_39.svg) Corrected datasets for missing classes, reworte ```eval()``` to enclude the whole validation set ------------------------ - Dataset 637, was missing 138 classes due to the min. length requirement in the ```DatasetGenerator``` class. The requirement was lowered to 175AA. Further the ```DatasetGenerator``` class was rewritten, to ensure to contain 5 samples from every class in the validation set. - the ```eval()``` function of ```DeeProtein``` was rewritten to perform the validaion on the _whole_ validation set at given steps. Performance on 679 classes with minlength 175: lr=0.01, e=0.1, batchsize=64 [ROC](DeeProtein_TFRECORDS_PURECONV_1x1tuned_restored637750kfull_sce_adam_1dconv679_1000_one_hot_padded_64_0.01_0.1.roc_32.svg) [Precision](DeeProtein_TFRECORDS_PURECONV_1x1tuned_restored637750kfull_sce_adam_1dconv679_1000_one_hot_padded_64_0.01_0.1.precision_32.svg) lr=0.001, e=0.1, batchsize=64 [ROC](DeeProtein_TFRECORDS_PURECONV_1x1tuned_restored637750kfull_sce_adam_1dconv679_1000_one_hot_padded_64_0.01_0.1.roc_32.svg) [Precision](DeeProtein_TFRECORDS_PURECONV_1x1tuned_restored637750kfull_sce_adam_1dconv679_1000_one_hot_padded_64_0.01_0.1.precision_32.svg) Reinitialization with pretrained parameters and lower learning rate allowed finetuning of the classifier. Especially as the validation set is uniformally distributed (in contrast to the training set) the classifier can be considered as trained. ROC/ACC/AUC-metrics -------- ROC and AUC was added to be calculated on the fly (after validation on the whole validation set.). Training models on the embedded sequences ------------------ We generated batches from the word embeddings (dim=100, kmer-length=3) for the 679(EC) and the 1084 mulilabel network. However training proceeds much more slowly as the parametersize is 5 times the size of the one-hot network. Multilabel-classification -------- In order to be able to perform multilabel classification, we rewrite the input pipeline (```DatasetGenerator, BatchGenerator, TFrecordsgenerator```) and generated two datasets with 339 and 1084 classes respectively. The considered labels were chosen solely based on their polulation. As the GO-term hierarchy follows a directed acyclic graph (DAG) we looked up all parent nodes for each leaf nodes and included the total set of annotations for each sequence. First models were run after extending the network for 2 convolutional and 2 1x1 layers on the 1084 classes dataset. Results were disenchanting. Comparison of datasets ---------------- Total seqs after filtering (EC): 220488 Total seqs after filtering (GO): 235767
