Author: Yoong Wearn Lim
Date: 2022/02/18
- To predict binder and non-binder antibodies to CTLA-4 and PD-1, using Convolutional Neural Network (CNN).
- To generate synthetic antibody sequences (CDR3K + CDR3H) to CTLA-4 and PD-1, using Generative Adversarial Network (GAN).
Ensure that python3 and tensorflow version 2.4.0 are used.
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
library(reticulate)
use_virtualenv("py3-virtualenv")
py_config()## python: /home/ywlim/.virtualenvs/py3-virtualenv/bin/python
## libpython: /usr/lib/python3.6/config-3.6m-x86_64-linux-gnu/libpython3.6.so
## pythonhome: /home/ywlim/.virtualenvs/py3-virtualenv:/home/ywlim/.virtualenvs/py3-virtualenv
## virtualenv: /home/ywlim/.virtualenvs/py3-virtualenv/bin/activate_this.py
## version: 3.6.9 (default, Jul 17 2020, 12:50:27) [GCC 8.4.0]
## numpy: /home/ywlim/.virtualenvs/py3-virtualenv/lib/python3.6/site-packages/numpy
## numpy_version: 1.19.2
##
## python versions found:
## /home/ywlim/.virtualenvs/py3-virtualenv/bin/python
## /home/ywlim/.virtualenvs/r-reticulate/bin/python
## /home/ywlim/miniconda3/envs/r-reticulate/bin/python
## /home/ywlim/miniconda3/bin/python3
## /usr/bin/python3
## /usr/bin/python
## /home/ywlim/miniconda3/envs/ame/bin/python
## /home/ywlim/miniconda3/envs/ice_env/bin/python
## /home/ywlim/miniconda3/bin/python
library(keras)
library(tidyverse)
library(PepTools)
library(mltools)
library(caret)
library(pROC)
filter = dplyr::filter
select = dplyr::select
theme_set(theme_bw())Here we prepare CDR3K + CDR3H amino acid sequences for both targets:
- Load all pre- and post-FACS sorted sequences.
- Randomize the order of the sequences.
- Pad CDR3K and CDR3H with "-" so all sequences have the same length.
- Define binders and non-binders.
- Binders: post/pre fold change >= 1.8, post-sort frequency >= 0.01
- Non-binders: post/pre fold change < 1, pre-sort frequency >= 0.01
- Split sequences into training and testing sets.
all <- read_delim("CNN/all_ab_pre_post.txt", delim = "\t")
# shuffle order of data frame
set.seed(42)
rows <- sample(nrow(all))
all <- all[rows, ]
# padding
max_k <- max(nchar(all$CDR3K)) # 11
max_h <- max(nchar(all$CDR3H)) # 25
all$paddedh <- gsub("\\s", "-", format(all$CDR3H, width = max_h))
all$paddedk <- gsub("\\s", "-", format(all$CDR3K, width = max_k))
all$lengthh <- nchar(all$CDR3H)
all$lengthk <- nchar(all$CDR3K)
all$padded <- paste0(all$paddedk, all$paddedh)
# filter for only unique sequences
all2 <- all %>%
group_by(padded) %>%
top_n(1, wt = post)
all2 <- all2[!duplicated(all2$padded), ]
# define binders/non-binders
all2 <- all2 %>%
ungroup() %>%
mutate(enriched = ifelse((fc >= 1.8 & post >= 0.01), 1,
ifelse(fc < 1 & pre >= 0.01, 0, "ambi")))
all2 <- filter(all2, enriched != "ambi")
# split test train
split_train_test <- function(antigenx) {
mini <- all2 %>% filter(antigen == antigenx)
set.seed(22)
trainIndex <- createDataPartition(mini$enriched, p = .8,
list = FALSE,
times = 1)
df_train <- mini[ trainIndex, ]
df_test <- mini[-trainIndex, ]
my_list <- list(train = df_train, test = df_test)
return(my_list)
}
c1 <- split_train_test("CTLA-4")
p1 <- split_train_test("PD-1")
saveRDS(c1, file = "CNN/c1.RDS")
saveRDS(p1, file = "CNN/p1.RDS")c1$train$type <- "Train"
c1$test$type <- "Test"
c1_both <- bind_rows(c1$train, c1$test)
p1$train$type <- "Train"
p1$test$type <- "Test"
p1_both <- bind_rows(p1$train, p1$test)
both <- bind_rows(c1_both, p1_both)
count <- data.frame(table(both$antigen, both$type, both$enriched))
names(count) <- c("antigen", "type", "binder", "freq")
count$type <- factor(count$type, levels = c("Train", "Test"))
count$binder <- as.numeric(as.character(count$binder))
count$binder[count$binder == 0] <- "Non-binder"
count$binder[count$binder == 1] <- "Binder"
ggplot(count, aes(x = type, y = freq, fill = binder, label = freq)) +
facet_wrap(~ antigen, scales = "free_y") +
geom_col(position = "stack") +
geom_text(position = position_stack(vjust = .5)) +
theme_bw() +
labs(y = "# sequences")
Encode sequences into numerical matrices (images) using BLOSUM62 scores.
# function to encode peptides
load(file = "BLOSUM62_with_deletion.Rdata")
pep_encode_blosum <- function(pep) {
bl62_prob <- BLOSUM62
p_mat <- pep %>% pep_mat
n_peps <- length(pep)
l_peps <- nchar(pep[1])
l_enc <- ncol(bl62_prob)
o_tensor <- array(data = NA, dim = c(n_peps, l_peps, l_enc))
for (i in 1:n_peps) {
pep_i_residues <- p_mat[i, ]
pep_img <- bl62_prob[pep_i_residues, ]
o_tensor[i, , ] <- pep_img
}
return(o_tensor)
}# CTLA-4
c1_train <- pep_encode_blosum(c1$train$padded)
c1_test <- pep_encode_blosum(c1$test$padded)
# reshape
c1_train <- array_reshape(c1_train, c(nrow(c1_train), ncol(c1_train), 22, 1))
c1_test <- array_reshape(c1_test, c(nrow(c1_test), ncol(c1_test), 22, 1))
num_classes <- 2
c1_train_y <- c1$train %>% pull(enriched) %>% array %>% keras::to_categorical(num_classes = num_classes)
c1_test_y <- c1$test %>% pull(enriched) %>% array %>% keras::to_categorical(num_classes = num_classes)
# PD-1
p1_train <- pep_encode_blosum(p1$train$padded)
p1_test <- pep_encode_blosum(p1$test$padded)
# reshape
p1_train <- array_reshape(p1_train, c(nrow(p1_train), ncol(p1_train), 22, 1))
p1_test <- array_reshape(p1_test, c(nrow(p1_test), ncol(p1_test), 22, 1))
num_classes <- 2
p1_train_y <- p1$train %>% pull(enriched) %>% array %>% keras::to_categorical(num_classes = num_classes)
p1_test_y <- p1$test %>% pull(enriched) %>% array %>% keras::to_categorical(num_classes = num_classes)
saveRDS(p1_train, file = "CNN/p1_train.RDS")
saveRDS(p1_test, file = "CNN/p1_test.RDS")
saveRDS(p1_train_y, file = "CNN/p1_train_y.RDS")
saveRDS(p1_test_y, file = "CNN/p1_test_y.RDS")
saveRDS(c1_train, file = "CNN/c1_train.RDS")
saveRDS(c1_test, file = "CNN/c1_test.RDS")
saveRDS(c1_train_y, file = "CNN/c1_train_y.RDS")
saveRDS(c1_test_y, file = "CNN/c1_test_y.RDS")Here we build CNN models to predict binders vs. non-binders. We train the CTLA-4 model and PD-1 model separately.
model <- keras_model_sequential() %>%
layer_conv_2d(filters = 64, kernel_size = c(3, 3),
activation = "relu", input_shape = c(36, 22, 1), padding = "same") %>%
layer_dropout(rate = 0.5) %>%
layer_max_pooling_2d(pool_size = c(2, 2), padding = "same") %>%
layer_conv_2d(filters = 32, kernel_size = c(3, 4),
activation = "relu", padding = "same") %>%
layer_dropout(rate = 0.4) %>%
layer_max_pooling_2d(pool_size = c(2, 2), padding = "same") %>%
layer_conv_2d(filters = 32, kernel_size = c(4, 4),
activation = "relu", padding = "same") %>%
layer_dropout(rate = 0.3) %>%
layer_flatten() %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dropout(rate = 0.4) %>%
layer_dense(units = 2, activation = "softmax")
summary(model)## Model: "sequential"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## conv2d_2 (Conv2D) (None, 36, 22, 64) 640
## ________________________________________________________________________________
## dropout_3 (Dropout) (None, 36, 22, 64) 0
## ________________________________________________________________________________
## max_pooling2d_1 (MaxPooling2D) (None, 18, 11, 64) 0
## ________________________________________________________________________________
## conv2d_1 (Conv2D) (None, 18, 11, 32) 24608
## ________________________________________________________________________________
## dropout_2 (Dropout) (None, 18, 11, 32) 0
## ________________________________________________________________________________
## max_pooling2d (MaxPooling2D) (None, 9, 6, 32) 0
## ________________________________________________________________________________
## conv2d (Conv2D) (None, 9, 6, 32) 16416
## ________________________________________________________________________________
## dropout_1 (Dropout) (None, 9, 6, 32) 0
## ________________________________________________________________________________
## flatten (Flatten) (None, 1728) 0
## ________________________________________________________________________________
## dense_1 (Dense) (None, 64) 110656
## ________________________________________________________________________________
## dropout (Dropout) (None, 64) 0
## ________________________________________________________________________________
## dense (Dense) (None, 2) 130
## ================================================================================
## Total params: 152,450
## Trainable params: 152,450
## Non-trainable params: 0
## ________________________________________________________________________________
# duplicate model
# note we have to use "clone_model", can't just do model_c1 <- model (this is just a pointer)
model_c1 <- clone_model(model)
model_p1 <- clone_model(model)
# need to compile each one separately
model_c1 %>% compile(
loss = 'categorical_crossentropy',
optimizer = 'adam',
metrics = 'accuracy'
)
model_p1 %>% compile(
loss = 'categorical_crossentropy',
optimizer = 'adam',
metrics = 'accuracy'
)
epochs <- 30
batch_size <- 50
validation_split <- 0.2
# fit models
c1_hist <- model_c1 %>% fit(
c1_train, c1_train_y,
epochs = epochs,
batch_size = batch_size,
validation_split = validation_split)
p1_hist <- model_p1 %>% fit(
p1_train, p1_train_y,
epochs = epochs,
batch_size = batch_size,
validation_split = validation_split)
model_c1 %>% save_model_tf("CNN/model_c1")
model_p1 %>% save_model_tf("CNN/model_p1")Now that the models are fully trained, we evaluate them using the reserved 20% test sets.
pred_c1 <- model_c1 %>% predict_proba(c1_test) %>% as.data.frame()
names(pred_c1) <- c("nb_prob", "b_prob")
y_real_c1 <- c1_test_y %>% apply(1,function(x){ return( which(x==1) - 1) })
y_pred_c1 <- model_c1 %>% keras::predict_classes(c1_test)
perf_c1 <- model_c1 %>% evaluate(c1_test, c1_test_y)
acc_c1 <- perf_c1[[2]] %>% round(3) * 100
mcc_c1 <- mltools::mcc(preds = as.vector(y_pred_c1), actuals = as.vector(y_real_c1)) %>% round(2)
results_c1 <- tibble(y_real = y_real_c1,
y_pred = y_pred_c1,
b_prob = pred_c1$b_prob)
results_c1 <- results_c1 %>%
mutate(class = ifelse(y_real == 0 & y_pred == 0, "TN",
ifelse(y_real == 0 & y_pred == 1, "FP",
ifelse(y_real == 1 & y_pred == 1, "TP",
ifelse(y_real == 1 & y_pred == 0, "FN", NA)))))
res_c1 <- table(results_c1[, 1:2]) %>%
as.data.frame() %>%
mutate(y_real = as.character(y_real),
y_pred = as.character(y_pred))
res_c1$y_real[res_c1$y_real == 0] <- "Non-binder"
res_c1$y_pred[res_c1$y_pred == 0] <- "Non-binder"
res_c1$y_real[res_c1$y_real == 1] <- "Binder"
res_c1$y_pred[res_c1$y_pred == 1] <- "Binder"
res_c1$class <- c("TN", "FN", "FP", "TP")
# generate confusion matrix
ggplot(res_c1, aes(x = y_pred, y = y_real, label = Freq, color = class)) +
geom_text(size = 7) +
geom_tile(color = "black", fill = NA) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, color = "black"),
axis.text.y = element_text(color = "black"),
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(y = "Measured (FACS)", x = "Predicted", title = "CTLA-4",
subtitle = paste0("Accuracy=", acc_c1, "\nMCC=", mcc_c1)) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(color = FALSE)
# density plot showing binder probabilities
ggplot(results_c1, aes(x = b_prob, color = class)) +
geom_density(fill = NA) +
theme(axis.text = element_text(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(y = "Density", x = "Binder probability", title = "CTLA-4", color = "Type")
# roc curve
roc_obj_c1 <- roc(y_real_c1, results_c1$b_prob)
plot.roc(roc_obj_c1, print.auc = TRUE, main = "CTLA-4 ROC curve")
pred_p1 <- model_p1 %>% predict_proba(p1_test) %>% as.data.frame()
names(pred_p1) <- c("nb_prob", "b_prob")
y_real_p1 <- p1_test_y %>% apply(1,function(x){ return( which(x==1) - 1) })
y_pred_p1 <- model_p1 %>% keras::predict_classes(p1_test)
perf_p1 <- model_p1 %>% evaluate(p1_test, p1_test_y)
acc_p1 <- perf_p1[[2]] %>% round(3) * 100
mcc_p1 <- mltools::mcc(preds = as.vector(y_pred_p1), actuals = as.vector(y_real_p1)) %>% round(2)
results_p1 <- tibble(y_real = y_real_p1,
y_pred = y_pred_p1,
b_prob = pred_p1$b_prob)
results_p1 <- results_p1 %>%
mutate(class = ifelse(y_real == 0 & y_pred == 0, "TN",
ifelse(y_real == 0 & y_pred == 1, "FP",
ifelse(y_real == 1 & y_pred == 1, "TP",
ifelse(y_real == 1 & y_pred == 0, "FN", NA)))))
res_p1 <- table(results_p1[, 1:2]) %>%
as.data.frame() %>%
mutate(y_real = as.character(y_real),
y_pred = as.character(y_pred))
res_p1$y_real[res_p1$y_real == 0] <- "Non-binder"
res_p1$y_pred[res_p1$y_pred == 0] <- "Non-binder"
res_p1$y_real[res_p1$y_real == 1] <- "Binder"
res_p1$y_pred[res_p1$y_pred == 1] <- "Binder"
res_p1$class <- c("TN", "FN", "FP", "TP")
# generate confusion matrix
ggplot(res_p1, aes(x = y_pred, y = y_real, label = Freq, color = class)) +
geom_text(size = 7) +
geom_tile(color = "black", fill = NA) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, color = "black"),
axis.text.y = element_text(color = "black"),
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(y = "Measured (FACS)", x = "Predicted", title = "PD-1",
subtitle = paste0("Accuracy=", acc_p1, "\nMCC=", mcc_p1)) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(color = FALSE)
# density plot showing binder probabilities
ggplot(results_p1, aes(x = b_prob, color = class)) +
geom_density(fill = NA) +
theme(axis.text = element_text(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(y = "Density", x = "Binder probability", title = "PD-1", color = "Type")
# roc curve
roc_obj_p1 <- roc(y_real_p1, results_p1$b_prob)
plot.roc(roc_obj_p1, print.auc = TRUE, main = "PD-1 ROC curve")
Here we build GAN models to generate anti-CTLA-4 and anti-PD-1 CDR3 sequences. We will train a separate model for each V gene, and we will train light and heavy chain separately. In total, we will train 15 models.
Previously, we annotated full length light and heavy chain sequences using ANARCI.
We used the AHo numbering scheme, which is based on the structural alignments of the 3D structures of the antibody variable regions. A quote from https://plueckthun.bioc.uzh.ch/antibody/Numbering/NumFrame.html:
An important difference to the numbering scheme presented here (AHo) is that in the IMGT scheme insertions and deletions "grow" unidirectionally, as in the original Chothia definition (Chothia & Lesk, 1987), while in the AHo scheme, insertions and deletions are placed symmetrically around the key position marked in yellow. Furthermore, length variations in CDR 1 and CDR 2 are represented by a single gap in IMGT and by one or two gaps in AHo.
After AHo annotation, the CDR3 regions were extracted from the sequences. All sequences now have the same sequence length.
Here we encode the previously prepared CDR3 sequences into "images".
# previously prepared CDR3 sequences, by target, chain, and V gene
seq_all <- readRDS(file = "GAN/seq_all.RDS")
encoded <- list()
length(encoded) <- 15
names(encoded) <- names(seq_all)
for (i in 1:15) {
seq_encoded <- pep_encode_blosum(seq_all[[i]])
# reshape
encoded[[i]] <- array_reshape(seq_encoded, c(nrow(seq_encoded), ncol(seq_encoded), 22, 1))
}
saveRDS(encoded, file = "GAN/seq_all_encoded.RDS")
head(str(encoded))## List of 15
## $ 1 CTLA4 heavy IGHV3-33*01 : num [1:307, 1:32, 1:22, 1] 0.267 0.533 0.533 0.533 0.533 ...
## $ 2 CTLA4 heavy IGHV1-18*04 : num [1:70, 1:32, 1:22, 1] 0.533 0.2 0.533 0.533 0.267 ...
## $ 3 CTLA4 heavy IGHV3-20*01 : num [1:39, 1:32, 1:22, 1] 0.267 0.533 0.533 0.533 0.2 ...
## $ 4 CTLA4 heavy IGHV4-39*01 : num [1:36, 1:32, 1:22, 1] 0.533 0.533 0.533 0.533 0.533 ...
## $ 5 CTLA4 light IGKV3-20*01 : num [1:292, 1:32, 1:22, 1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
## $ 6 CTLA4 light IGKV1D-39*01: num [1:54, 1:32, 1:22, 1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
## $ 7 CTLA4 light IGKV1-17*01 : num [1:37, 1:32, 1:22, 1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
## $ 8 CTLA4 light IGKV1-16*01 : num [1:34, 1:32, 1:22, 1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
## $ 9 PD1 heavy IGHV4-4*07 : num [1:81, 1:32, 1:22, 1] 0.533 0.533 0.533 0.533 0.533 ...
## $ 10 PD1 heavy IGHV3-33*03 : num [1:80, 1:32, 1:22, 1] 0.267 0.533 0.533 0.533 0.267 ...
## $ 11 PD1 heavy IGHV1-18*04 : num [1:67, 1:32, 1:22, 1] 0.533 0.533 0.533 0.533 0.533 ...
## $ 12 PD1 light IGKV1-17*01 : num [1:104, 1:32, 1:22, 1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
## $ 13 PD1 light IGKV1-6*02 : num [1:43, 1:32, 1:22, 1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
## $ 14 PD1 light IGKV3-15*01 : num [1:38, 1:32, 1:22, 1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
## $ 15 PD1 light IGKV1-9*01 : num [1:32, 1:32, 1:22, 1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
## NULL
# encoded is a list with 15 items
str(encoded[[1]])## num [1:307, 1:32, 1:22, 1] 0.267 0.533 0.533 0.533 0.533 ...
# num [1:307, 1:32, 1:22, 1] 0.267 0.533 0.533 0.533 0.533 ...32 positions (row), 22 possible amino acids (including X and -) (columns).
h <- 32; w <- 22; c <- 1; l <- 100
loss_list <- list()
# 1 model for each target/chain/V gene
# 15 models total
for (modelx in 1:15) {
# generator network
gi <- layer_input(shape = l)
go <- gi %>% layer_dense(units = 16 * 11 * 128) %>%
layer_activation_leaky_relu() %>%
layer_reshape(target_shape = c(16, 11, 128)) %>%
layer_conv_2d(filters = 64,
kernel_size = c(2, 2),
padding = "same") %>%
layer_activation_leaky_relu() %>%
layer_conv_2d_transpose(filters = 32,
kernel_size = c(2, 2),
strides = 2,
padding = "same") %>%
layer_activation_leaky_relu() %>%
layer_conv_2d(filters = 128,
kernel_size = c(5, 6),
padding = "same") %>%
layer_activation_leaky_relu() %>%
layer_conv_2d(filters = 1,
kernel_size = c(6, 6),
activation = "tanh",
padding = "same")
g <- keras_model(gi, go)
summary(g)
# discriminator network
di <- layer_input(shape = c(h, w, c))
do <- di %>%
layer_conv_2d(filters = 96, kernel_size = 3) %>%
layer_activation_leaky_relu() %>%
layer_flatten() %>%
layer_dropout(rate = 0.3) %>%
layer_dense(units = 1, activation = "sigmoid")
d <- keras_model(di, do)
summary(d)
# compile discriminator
d %>% compile(optimizer = "rmsprop",
loss = "binary_crossentropy")
# freeze weights and compile
freeze_weights(d)
gani <- layer_input(shape = l)
gano <- gani %>% g %>% d
gan <- keras_model(gani, gano)
gan %>% compile(optimizer = "rmsprop",
loss = "binary_crossentropy")
summary(gan)
start <- 1; dloss <- NULL; gloss <- NULL
# use batch size = 20, train 100 rounds
b <- 20
for (i in 1:100) {
# random noise
noise <- matrix(rnorm(b * l), nrow = b, ncol = l)
fake <- g %>% predict(noise)
# combine real and fake images
stop <- start + b - 1
# 50 random real images
rowx <- sample(nrow(encoded[[modelx]]), size = b)
real <- encoded[[modelx]][rowx,,, , drop = FALSE] # no need to reshape since we use drop=FALSE
rows <- nrow(real)
both <- array(0, dim = c(rows * 2, dim(real)[-1]))
both[1:rows,,,] <- fake
both[(rows + 1):(rows * 2),,,] <- real
labels <- rbind(matrix(runif(b, 0.9, 1), nrow = b, ncol = 1),
matrix(runif(b, 0, 0.1), nrow = b, ncol = 1)) # note the noise in truth labels
# train discriminator
dloss[i] <- d %>% train_on_batch(both, labels)
# train generator using gan
fakeAsReal <- array(runif(b, 0, 0.1), dim = c(b, 1))
gloss[i] <- gan %>% train_on_batch(noise, fakeAsReal)
}
# save the generator
save_model_tf(g, filepath = paste0("GAN/GAN_model_", modelx))
# monitor loss
res <- data.frame(iteration = seq(1:100), dloss = dloss, gloss = gloss)
res <- res %>%
gather(type, value, 2:3)
res$id <- modelx
loss_list[[modelx]] <- res
}## Model: "model"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_1 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_2 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_3 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_5 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_2 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose (Conv2DTranspose) (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_1 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_4 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_3 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_1"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_2 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_6 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_4 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_1 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_4 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_3 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_2"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_3 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_1 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_3"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_4 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_4 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_8 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_1 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_9 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_7 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_1 (Conv2DTranspose (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_6 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_8 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_5 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_7 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_4"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_5 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_10 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_9 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_2 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_5 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_5 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_5"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_6 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_3 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_4 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_6"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_7 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_6 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_13 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_2 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_13 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_12 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_2 (Conv2DTranspose (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_11 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_12 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_10 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_11 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_7"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_8 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_14 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_14 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_3 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_6 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_7 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_8"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_9 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_6 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_7 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_9"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_10 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_8 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_18 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_3 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_17 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_17 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_3 (Conv2DTranspose (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_16 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_16 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_15 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_15 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_10"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_11 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_18 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_19 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_4 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_7 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_9 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_11"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_12 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_9 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_10 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_12"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_13 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_10 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_23 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_4 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_21 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_22 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_4 (Conv2DTranspose (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_21 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_20 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_20 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_19 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_13"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_14 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_22 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_24 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_5 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_8 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_11 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_14"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_15 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_12 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_13 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_15"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_16 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_12 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_28 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_5 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_25 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_27 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_5 (Conv2DTranspose (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_26 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_24 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_25 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_23 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_16"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_17 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_26 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_29 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_6 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_9 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_13 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_17"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_18 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_15 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_16 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_18"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_19 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_14 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_33 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_6 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_29 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_32 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_6 (Conv2DTranspose (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_31 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_28 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_30 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_27 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_19"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_20 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_30 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_34 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_7 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_10 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_15 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_20"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_21 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_18 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_19 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_21"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_22 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_16 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_38 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_7 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_33 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_37 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_7 (Conv2DTranspose (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_36 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_32 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_35 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_31 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_22"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_23 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_34 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_39 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_8 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_11 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_17 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_23"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_24 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_21 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_22 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_24"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_25 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_18 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_43 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_8 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_37 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_42 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_8 (Conv2DTranspose (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_41 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_36 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_40 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_35 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_25"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_26 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_38 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_44 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_9 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_12 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_19 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_26"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_27 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_24 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_25 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_27"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_28 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_20 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_48 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_9 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_41 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_47 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_9 (Conv2DTranspose (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_46 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_40 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_45 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_39 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_28"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_29 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_42 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_49 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_10 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_13 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_21 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_29"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_30 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_27 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_28 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_30"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_31 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_22 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_53 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_10 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_45 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_52 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_10 (Conv2DTranspos (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_51 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_44 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_50 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_43 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_31"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_32 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_46 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_54 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_11 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_14 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_23 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_32"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_33 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_30 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_31 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_33"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_34 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_24 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_58 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_11 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_49 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_57 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_11 (Conv2DTranspos (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_56 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_48 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_55 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_47 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_34"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_35 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_50 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_59 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_12 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_15 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_25 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_35"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_36 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_33 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_34 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_36"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_37 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_26 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_63 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_12 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_53 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_62 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_12 (Conv2DTranspos (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_61 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_52 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_60 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_51 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_37"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_38 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_54 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_64 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_13 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_16 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_27 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_38"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_39 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_36 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_37 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_39"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_40 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_28 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_68 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_13 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_57 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_67 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_13 (Conv2DTranspos (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_66 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_56 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_65 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_55 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_40"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_41 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_58 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_69 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_14 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_17 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_29 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_41"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_42 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_39 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_40 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
## Model: "model_42"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_43 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## dense_30 (Dense) (None, 22528) 2275328
## ________________________________________________________________________________
## leaky_re_lu_73 (LeakyReLU) (None, 22528) 0
## ________________________________________________________________________________
## reshape_14 (Reshape) (None, 16, 11, 128) 0
## ________________________________________________________________________________
## conv2d_61 (Conv2D) (None, 16, 11, 64) 32832
## ________________________________________________________________________________
## leaky_re_lu_72 (LeakyReLU) (None, 16, 11, 64) 0
## ________________________________________________________________________________
## conv2d_transpose_14 (Conv2DTranspos (None, 32, 22, 32) 8224
## ________________________________________________________________________________
## leaky_re_lu_71 (LeakyReLU) (None, 32, 22, 32) 0
## ________________________________________________________________________________
## conv2d_60 (Conv2D) (None, 32, 22, 128) 123008
## ________________________________________________________________________________
## leaky_re_lu_70 (LeakyReLU) (None, 32, 22, 128) 0
## ________________________________________________________________________________
## conv2d_59 (Conv2D) (None, 32, 22, 1) 4609
## ================================================================================
## Total params: 2,444,001
## Trainable params: 2,444,001
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_43"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_44 (InputLayer) [(None, 32, 22, 1)] 0
## ________________________________________________________________________________
## conv2d_62 (Conv2D) (None, 30, 20, 96) 960
## ________________________________________________________________________________
## leaky_re_lu_74 (LeakyReLU) (None, 30, 20, 96) 0
## ________________________________________________________________________________
## flatten_15 (Flatten) (None, 57600) 0
## ________________________________________________________________________________
## dropout_18 (Dropout) (None, 57600) 0
## ________________________________________________________________________________
## dense_31 (Dense) (None, 1) 57601
## ================================================================================
## Total params: 58,561
## Trainable params: 58,561
## Non-trainable params: 0
## ________________________________________________________________________________
## Model: "model_44"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## input_45 (InputLayer) [(None, 100)] 0
## ________________________________________________________________________________
## model_42 (Functional) (None, 32, 22, 1) 2444001
## ________________________________________________________________________________
## model_43 (Functional) (None, 1) 58561
## ================================================================================
## Total params: 2,502,562
## Trainable params: 2,444,001
## Non-trainable params: 58,561
## ________________________________________________________________________________
Use the fully trained GAN models to generate 100 sequences for each model.
myseq_all <- list()
for (j in 1:15) {
# load previously saved generator
g <- load_model_tf(paste0("GAN/GAN_model_", j))
myseq <- list()
for (i in 1:100) {
noise <- matrix(rnorm(b * l), nrow = b, ncol = l)
# generate fake image using the generator
fake <- g %>% predict(noise)
f <- fake[1,,,]
dim(f) <- c(32, 22, 1)
myseq[[i]] <- f
}
myseq_all[[j]] <- myseq
}Here we decode the generated CDR3 images back into amino acid sequences.
aa_order <- c("A", "R", "N", "D", "C", "Q", "E", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V", "X", "-")
decode_seq_cdr3 <- function(seq_list) {
aa_list <- list()
for (i in 1:length(seq_list)) {
peptide1 <- as.data.frame(seq_list[[i]])
colnames(peptide1) <- aa_order
# remove column for amino acid X
# it was previously added for padding so that we have an even shape
peptide1 <- peptide1 %>% select(-X)
# which amino acid has the highest score (decoding)
aa <- colnames(peptide1)[apply(peptide1, 1, which.max)]
# find where the gaps are
gaps <- which(aa %in% "-")
# first and last gap in CDR3 region
gap_first <- gaps[gaps > 1][1]
gap_last <- gaps[gaps < 32]
gap_last <- gap_last[length(gap_last)]
# replace spurious aa flanked by gaps in CDR3
aa[gap_first:gap_last] <- "-"
aa_string <- paste(aa, collapse = "")
# remove gaps
aa_string <- gsub("-", "", aa_string)
aa_list[[i]] <- aa_string
}
all_aa <- data.frame(aa = do.call(rbind, aa_list))
return(all_aa)
}gen_seq <- list()
for (i in 1:15) {
temp <- decode_seq_cdr3(myseq_all[[i]])
gen_seq[[i]] <- temp$aa
}
names(gen_seq) <- names(seq_all)Example generated sequences:
for (i in 1:15) {
print(names(gen_seq)[i])
print(gen_seq[[i]][!duplicated(gen_seq[[i]])] %>% head(10))
} ## [1] "1 CTLA4 heavy IGHV3-33*01"
## [1] "ARGGYIGSFDY" "AKGGYLGPFDY" "ARGGVLNGHFDY" "ARGADVGGYYDY"
## [5] "AGGKHLKHGHFDY" "AKSGYIGSFDY" "AKGGYWNGAFDY" "ARGGGLGTYDY"
## [9] "ARKGKIQFFDY" "ARAGHVNGPFDY"
## [1] "2 CTLA4 heavy IGHV1-18*04"
## [1] "ARVGANGFDY" "ARINANWFDY" "ARGEGNWFDY" "ARGDANNFDY" "ARVGGSWFDY"
## [6] "ARTNGNQWDY" "ARVGGKVFDY" "ARGWGNFFDY" "ARIGANWFDY" "ARIGGNWFDY"
## [1] "3 CTLA4 heavy IGHV3-20*01"
## [1] "ARDGWKLLDDY" "ARDGWKLVDDY" "ARDGWELLDDY" "ARDGWKRNLGMDY"
## [5] "ARDGWKRNLLDDY" "ARDGWRLLDDY" "ARDGWKRLLDDY" "ARDGWKLMDDY"
## [9] "ARDGWNLLDDY" "ARDGWKFLFMDY"
## [1] "4 CTLA4 heavy IGHV4-39*01"
## [1] "ARIRVYYGNRHGGYYFDY" "ARHKIGLWSFEY" "ARRRFELGEYYFDY"
## [4] "ARRRYYYGSRGGYYFDY" "ARRHYQYSSGGWSFDY" "AKKKVQLFSYGFDY"
## [7] "ARKRVQVYYWSFDY" "ARKAHYHGNFYYGYWFFDY" "ARYKIGLGAYHYSYFFDY"
## [10] "AKRYYGGGYYFDY"
## [1] "5 CTLA4 light IGKV3-20*01"
## [1] "QQYGSSPYT" "QQYGSSPWT" "QQYGSSPFT" "QQYGQSPYT" "QQYGNSPWT" "QQYGNSPYT"
## [7] "QRYGSSPYT" "QQYGNSPFT" "QQYGGSPWT" "QQYGASPWT"
## [1] "6 CTLA4 light IGKV1D-39*01"
## [1] "QQSYSTPYT" "QQSYSTPFT" "QRSYSTPYT" "QQAYATPYT" "QQNYSTPYT" "QQAYSTPYT"
## [7] "QRAYSTPYT" "RRAYATPYT" "QQAYSTPFT" "QQGYSTPYT"
## [1] "7 CTLA4 light IGKV1-17*01"
## [1] "LQHNSYPWT" "LQHNSHPWT" "LQHSSYPWT" "MQHNSYPWT" "VQHNSYPWT" "LKHNSYPWT"
## [7] "VQHHSYPWT" "LQHNNYPWT" "LQQNSYPWT" "LQYNSYPWT"
## [1] "8 CTLA4 light IGKV1-16*01"
## [1] "QQYKSYPYT" "QQYNNYPYT" "QQYKNYPYT" "QQYNSYPYT" "QQYRNYPYT" "QQYRAYPYT"
## [7] "QQYQSYPYT" "QQYRSYPYT" "QQYNAYPYT"
## [1] "9 PD1 heavy IGHV4-4*07"
## [1] "ARDGYGNGNRYYGMDV" "ARDYYGSGRRYYGMDV" "ARDWYSSGSRYYGMDV"
## [4] "ARDYYGNGRRYYGMDV" "ARDYYGSGGRYYGMDV" "ARDGGGGGGGFYYGMDV"
## [7] "ARDYHGSGRYYYGMDV" "ARDFYSSSSRYYGMDV" "ARDYYGSGNRYYGMDV"
## [10] "ARDYYGAGRRYYGMDV"
## [1] "10 PD1 heavy IGHV3-33*03"
## [1] "AGGGNHYGDY" "AGGGNHHGDY" "AAAGNYFGDY" "AGGGAYYWDY" "AGGGHYYGDY"
## [6] "AGGPNHFGDY" "AGGPTYYWDY" "AGGGSYYGDY" "AGNGTYYGDY" "AGGGTYYGDY"
## [1] "11 PD1 heavy IGHV1-18*04"
## [1] "ARDTYDILTSYYKNVFDY" "ARDYYDVHTSYYYYIMDY" "ARAYYDVVSGYYFVFDY"
## [4] "ARGGGLRLHVFDY" "AREGYYSSGGAGLLGFDE" "ARQHYYAVTGVYYYIFDY"
## [7] "ARNVGVFDY" "ARGTSEVSKKYHHHVFDY" "ARGGGYGHGGHHVYIFDY"
## [10] "ARGHHYNNYYIFDY"
## [1] "12 PD1 light IGKV1-17*01"
## [1] "LQYNSYPYT" "LQHNSYPYT" "VQYNSYPYT" "LQHNNYPYT" "LQYGSYPYT" "MQHNAYPYT"
## [7] "MQHNEYPYT" "LQYNSFPWT" "LQHNSYPWT" "LQYHSYPYT"
## [1] "13 PD1 light IGKV1-6*02"
## [1] "LQDYNYPYT" "LQDYNYPHT" "LQDYNYPWT" "LQDYNYPRT" "LQDYNYPNT" "LEDYNYPFT"
## [7] "LRDYNYPWT" "LRDYNYPFT" "LQDFNYPYT" "LEDYNYPYT"
## [1] "14 PD1 light IGKV3-15*01"
## [1] "QQYNNWPYT" "QQYNTWPYT" "QQYNNSWPYT" "QQYNNPWPYT" "QQYNNWPRT"
## [6] "QQYNNWWPYT"
## [1] "15 PD1 light IGKV1-9*01"
## [1] "QQLNSYPYT" "QQMNSYPYT" "QQLNSYPFT" "QQLHSYPYT" "QQQNSYPYT" "QQLNNYPFT"
## [7] "QQINSYPYT" "QQVNSYPFT"
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