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model_tensorboard.py
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import tensorflow as tf
#import tensorboard
from tensorboard.plugins.hparams import api as hp
from datetime import datetime
import matplotlib.pyplot as plt
import io
from HP_tunning.plot import *
from HP_tunning.my_tensorboard import HP_tunning_tensorboard_callback
from Model import *
from variable_init import *
default_HP = {
'CNN_1_kernal' : 16,
'CNN_1_RNN' : 64,
'CNN_2_kernal' : 32,
'CNN_2_RNN' : 128,
'CNN_3_kernal' : 64,
'CNN_3_RNN' : 256,
'CNN_4_kernal' : 128,
'CNN_4_RNN' : 256,
'CNN_5_kernal' : 128,
'CNN_5_RNN' : 256,
'DNN_1' : 100,
'DNN_2' : 100,
'DNN_rate' : 100,
'CNN_dropout' : 0.5,
'DNN_dropout' : 0.5,
'learning_rate' : 0.0001
}
class CNNLSTM_tensorboard:
def __init__(self, HP_dict, log_dir = 'tensorboard/', GPU=1, random_seed = 1234, verbose=1):
time_stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
GPU_stamp = '_GPU_{}'.format(GPU)
self.log_dir = log_dir + time_stamp + GPU_stamp
self.verbose = verbose
# HP
self.HP_dict = HP_dict
self.CNN_1_kernal = hp.HParam('CNN_1_kernal', hp.IntInterval(1, 1024))
self.CNN_1_RNN = hp.HParam('CNN_1_RNN', hp.IntInterval(1, 1024))
self.CNN_2_kernal = hp.HParam('CNN_2_kernal', hp.IntInterval(1, 1024))
self.CNN_2_RNN = hp.HParam('CNN_2_RNN', hp.IntInterval(1, 1024))
self.CNN_3_kernal = hp.HParam('CNN_3_kernal', hp.IntInterval(1, 1024))
self.CNN_3_RNN = hp.HParam('CNN_3_RNN', hp.IntInterval(1, 1024))
self.CNN_4_kernal = hp.HParam('CNN_4_kernal', hp.IntInterval(1, 1024))
self.CNN_4_RNN = hp.HParam('CNN_4_RNN', hp.IntInterval(1, 1024))
self.CNN_5_kernal = hp.HParam('CNN_5_kernal', hp.IntInterval(1, 1024))
self.CNN_5_RNN = hp.HParam('CNN_5_RNN', hp.IntInterval(1, 1024))
self.DNN_1 = hp.HParam('DNN_1', hp.IntInterval(1, 1024))
self.DNN_2 = hp.HParam('DNN_2', hp.IntInterval(1, 1024))
self.DNN_rate = hp.HParam('DNN_rate', hp.IntInterval(1, 1024))
self.CNN_dropout = hp.HParam('CNN_dropout', hp.RealInterval(0.001, 0.5))
self.DNN_dropout = hp.HParam('DNN_dropout', hp.RealInterval(0.001, 0.5))
self.learning_rate = hp.HParam('learning_rate',hp.RealInterval(1e-6, 1.0))
"""
self.CNN_1_kernal = HP_dict['CNN_1_kernal']
self.CNN_1_RNN = HP_dict['CNN_1_RNN']
self.CNN_2_kernal = HP_dict['CNN_2_kernal']
self.CNN_2_RNN = HP_dict['CNN_2_RNN']
self.CNN_3_kernal = HP_dict['CNN_3_kernal']
self.CNN_3_RNN = HP_dict['CNN_3_RNN']
self.CNN_4_kernal = HP_dict['CNN_4_kernal']
self.CNN_4_RNN = HP_dict['CNN_4_RNN']
self.CNN_5_kernal = HP_dict['CNN_5_kernal']
self.CNN_5_RNN = HP_dict['CNN_5_RNN']
self.DNN_1 = HP_dict['DNN_1']
self.DNN_2 = HP_dict['DNN_2']
self.DNN_rate = HP_dict['DNN_rate']
self.CNN_dropout = HP_dict['CNN_dropout' ]
self.DNN_dropout = HP_dict['DNN_dropout' ]
self.learning_rate = HP_dict['learning_rate']
"""
self.Metric_name_loss = 'rate_loss_test'
self.Metric_name_test_pearson = 'pearson_correlation_test'
self.Metric_name_test_spearman = 'spearman_correlation_test'
self.Metric_name_bench_pearson = 'pearson_correlation_bench'
self.Metric_name_bench_spearman = 'spearman_correlation_bench'
with tf.summary.create_file_writer(self.log_dir + '/hp_tunning').as_default():
hp.hparams_config(
hparams=[self.CNN_1_kernal, self.CNN_1_RNN,
self.CNN_2_kernal, self.CNN_2_RNN,
self.CNN_3_kernal, self.CNN_3_RNN,
self.CNN_4_kernal, self.CNN_4_RNN,
self.CNN_5_kernal, self.CNN_5_RNN,
self.DNN_1, self.DNN_2, self.DNN_rate,
self.CNN_dropout, self.DNN_dropout,
self.learning_rate],
metrics=[hp.Metric(self.Metric_name_loss, display_name='Loss'),
hp.Metric(self.Metric_name_test_pearson, display_name='Pearson Correlation - Test data'),
hp.Metric(self.Metric_name_test_spearman, display_name='Spearman Correlation - Test data'),
hp.Metric(self.Metric_name_bench_pearson, display_name='Pearson Correlation - Bench data'),
hp.Metric(self.Metric_name_bench_spearman, display_name='Spearman Correlation - Bench data')]
)
# Model
self.model = CNNLSTM_SpCas9(
HP= HP_dict,
random_seed = random_seed
)
self.data = self.model.SpCas9_data
self.bench_data = self.model.bench_data_endo
# Callbacks
test_input = self.data['test']['seq']
test_indel_true = self.data['test']['indel_rate']
bench_input = self.bench_data['total']['seq']
bench_indel_true = self.bench_data['total']['indel_rate']
self.callbacks = HP_tunning_tensorboard_callback(log_dir=self.log_dir)
test_data_correlation_plot = self.callbacks.callback_log_plot(
model = self.model.CNNLSTM_regression_model,
input_data = test_input,
true_data = test_indel_true,
plot = plot_scatter,
plot_name = 'Test data correlation'
)
bench_data_correlation_plot = self.callbacks.callback_log_plot(
model = self.model.CNNLSTM_regression_model,
input_data = bench_input,
true_data = bench_indel_true,
plot = plot_scatter,
plot_name = 'Endo data correlation'
)
callbacks_plot = [test_data_correlation_plot, bench_data_correlation_plot]
self.callbacks_list = self.callbacks.TB_callbacks(plot_callbacks = callbacks_plot)
def training(self,):
self.model.model_train(
callback= self.callbacks_list,
verbose=self.verbose
)
pass
def evaluate(self,):
model_evaluate = self.model.model_evaluate(verbose=0)
self.evaluate_total_loss = model_evaluate['total_loss']
self.evaluate_test_loss = model_evaluate['test_rate_loss']
self.evaluate_test_pearson = model_evaluate['test_data_pearson']
self.evaluate_test_spearman = model_evaluate['test_data_spearman']
self.evaluate_bench_pearson = model_evaluate['bench_data_pearson']
self.evaluate_bench_spearman = model_evaluate['bench_data_spearman']
#print(model_evaluate)
self.H_param(
loss= self.evaluate_test_loss,
pearson= self.evaluate_test_pearson,
spearman= self.evaluate_test_spearman,
bench_pearson= self.evaluate_bench_pearson,
bench_spearman= self.evaluate_bench_spearman,
step=1
)
pass
def H_param(self, loss, pearson, spearman, bench_pearson=None, bench_spearman=None,step=3):
hparams = {
'CNN_1_kernal' : self.HP_dict['CNN_1_kernal'] ,
'CNN_1_RNN' : self.HP_dict['CNN_1_RNN'] ,
'CNN_2_kernal' : self.HP_dict['CNN_2_kernal'] ,
'CNN_2_RNN' : self.HP_dict['CNN_2_RNN'] ,
'CNN_3_kernal' : self.HP_dict['CNN_3_kernal'] ,
'CNN_3_RNN' : self.HP_dict['CNN_3_RNN'] ,
'CNN_4_kernal' : self.HP_dict['CNN_4_kernal'] ,
'CNN_4_RNN' : self.HP_dict['CNN_4_RNN'] ,
'CNN_5_kernal' : self.HP_dict['CNN_5_kernal'] ,
'CNN_5_RNN' : self.HP_dict['CNN_5_RNN'] ,
'DNN_1' : self.HP_dict['DNN_1'] ,
'DNN_2' : self.HP_dict['DNN_2'] ,
'DNN_rate' : self.HP_dict['DNN_rate'] ,
'CNN_dropout' : self.HP_dict['CNN_dropout'] ,
'DNN_dropout' : self.HP_dict['DNN_dropout'] ,
'learning_rate' : self.HP_dict['learning_rate'],
}
with tf.summary.create_file_writer(self.log_dir + '/hp_tunning').as_default():
hp.hparams(hparams=hparams)
#print(loss, pearson, spearman)
tf.summary.scalar(self.Metric_name_loss, loss, step=step)
tf.summary.scalar(self.Metric_name_test_pearson, pearson, step=step)
tf.summary.scalar(self.Metric_name_test_spearman, spearman, step=step)
if bench_pearson is not None:
tf.summary.scalar(self.Metric_name_bench_pearson, bench_pearson, step=step)
tf.summary.scalar(self.Metric_name_bench_spearman, bench_spearman, step=step)
class SpCas9_tensorboard:
def __init__(self, HP_dict, log_dir = 'tensorboard/', GPU=1, random_seed = 1234, verbose=1):
time_stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
GPU_stamp = '_GPU_{}'.format(GPU)
self.log_dir = log_dir + time_stamp + GPU_stamp
self.verbose = verbose
self._HP_dict = HP_dict
self._random_seed = random_seed
self.TB_HParam_define()
self.Model_define()
self.callbacks()
def TB_HParam_define(self):
# HP
self._CNN_1_kernal = hp.HParam('CNN_1_kernal', hp.IntInterval(1, 1024))
self._CNN_1_RNN = hp.HParam('CNN_1_RNN', hp.IntInterval(1, 1024))
self._CNN_2_kernal = hp.HParam('CNN_2_kernal', hp.IntInterval(1, 1024))
self._CNN_2_RNN = hp.HParam('CNN_2_RNN', hp.IntInterval(1, 1024))
self._CNN_3_kernal = hp.HParam('CNN_3_kernal', hp.IntInterval(1, 1024))
self._CNN_3_RNN = hp.HParam('CNN_3_RNN', hp.IntInterval(1, 1024))
self._CNN_4_kernal = hp.HParam('CNN_4_kernal', hp.IntInterval(1, 1024))
self._CNN_4_RNN = hp.HParam('CNN_4_RNN', hp.IntInterval(1, 1024))
self._CNN_5_kernal = hp.HParam('CNN_5_kernal', hp.IntInterval(1, 1024))
self._CNN_5_RNN = hp.HParam('CNN_5_RNN', hp.IntInterval(1, 1024))
self._DNN_1 = hp.HParam('DNN_1', hp.IntInterval(1, 1024))
self._DNN_2 = hp.HParam('DNN_2', hp.IntInterval(1, 1024))
self._DNN_rate = hp.HParam('DNN_rate', hp.IntInterval(1, 1024))
self._CNN_dropout = hp.HParam('CNN_dropout', hp.RealInterval(0.001, 0.5))
self._DNN_dropout = hp.HParam('DNN_dropout', hp.RealInterval(0.001, 0.5))
self._learning_rate = hp.HParam('learning_rate',hp.RealInterval(1e-6, 1.0))
"""
self.CNN_1_kernal = HP_dict['CNN_1_kernal']
self.CNN_1_RNN = HP_dict['CNN_1_RNN']
self.CNN_2_kernal = HP_dict['CNN_2_kernal']
self.CNN_2_RNN = HP_dict['CNN_2_RNN']
self.CNN_3_kernal = HP_dict['CNN_3_kernal']
self.CNN_3_RNN = HP_dict['CNN_3_RNN']
self.CNN_4_kernal = HP_dict['CNN_4_kernal']
self.CNN_4_RNN = HP_dict['CNN_4_RNN']
self.CNN_5_kernal = HP_dict['CNN_5_kernal']
self.CNN_5_RNN = HP_dict['CNN_5_RNN']
self.DNN_1 = HP_dict['DNN_1']
self.DNN_2 = HP_dict['DNN_2']
self.DNN_rate = HP_dict['DNN_rate']
self.CNN_dropout = HP_dict['CNN_dropout' ]
self.DNN_dropout = HP_dict['DNN_dropout' ]
self.learning_rate = HP_dict['learning_rate']
"""
self._Metric_name_loss = 'rate_loss_test'
self._Metric_name_test_pearson = 'pearson_correlation_test'
self._Metric_name_test_spearman = 'spearman_correlation_test'
self._Metric_name_bench_pearson = 'pearson_correlation_bench'
self._Metric_name_bench_spearman = 'spearman_correlation_bench'
with tf.summary.create_file_writer(self.log_dir + '/hp_tunning').as_default():
hp.hparams_config(
hparams=[self._CNN_1_kernal, self._CNN_1_RNN,
self._CNN_2_kernal, self._CNN_2_RNN,
self._CNN_3_kernal, self._CNN_3_RNN,
self._CNN_4_kernal, self._CNN_4_RNN,
self._CNN_5_kernal, self._CNN_5_RNN,
self._DNN_1, self._DNN_2, self._DNN_rate,
self._CNN_dropout, self._DNN_dropout,
self._learning_rate],
metrics=[hp.Metric(self._Metric_name_loss, display_name='Loss'),
hp.Metric(self._Metric_name_test_pearson, display_name='Pearson Correlation - Test data'),
hp.Metric(self._Metric_name_test_spearman, display_name='Spearman Correlation - Test data'),
hp.Metric(self._Metric_name_bench_pearson, display_name='Pearson Correlation - Bench data'),
hp.Metric(self._Metric_name_bench_spearman, display_name='Spearman Correlation - Bench data')]
)
def Model_define(self):
# Model
self._model_define = SpCas9_MODEL(
HP= self._HP_dict,
random_seed = self._random_seed
)
self.model = self._model_define.model
self.model_regression = self._model_define.model_regression()
# Data
self.data = self._model_define.SpCas9_data
self.bench_data = self._model_define.bench_data_endo
def callbacks(self):
# Callbacks
test_input = self.data['test']['seq']
test_indel_true = self.data['test']['indel_rate']
bench_input = self.bench_data['total']['seq']
bench_indel_true = self.bench_data['total']['indel_rate']
self.__callbacks = HP_tunning_tensorboard_callback(log_dir=self.log_dir)
test_data_correlation_plot = self.__callbacks.callback_log_plot(
model = self.model_regression,
input_data = test_input,
true_data = test_indel_true,
plot = plot_scatter,
plot_name = 'Test data correlation'
)
bench_data_correlation_plot = self.__callbacks.callback_log_plot(
model = self.model_regression,
input_data = bench_input,
true_data = bench_indel_true,
plot = plot_scatter,
plot_name = 'Endo data correlation'
)
callbacks_plot = [test_data_correlation_plot, bench_data_correlation_plot]
self._callbacks_list = self.__callbacks.TB_callbacks(plot_callbacks = callbacks_plot)
def training(self,):
self._model_define.model_train(
callback= self._callbacks_list,
verbose=self.verbose
)
pass
def evaluate(self,):
self.model_evaluation = self._model_define.model_evaluate(verbose=0)
#print(model_evaluate)
self._H_param(
loss = self.model_evaluation.test_rate_loss,
pearson = self.model_evaluation.test_pearson,
spearman = self.model_evaluation.test_spearman,
bench_pearson = self.model_evaluation.bench_pearson,
bench_spearman = self.model_evaluation.bench_spearman,
step=1
)
pass
def _H_param(self, loss, pearson, spearman, bench_pearson=None, bench_spearman=None,step=3):
hparams = {
'CNN_1_kernal' : self._HP_dict['CNN_1_kernal'] ,
'CNN_1_RNN' : self._HP_dict['CNN_1_RNN'] ,
'CNN_2_kernal' : self._HP_dict['CNN_2_kernal'] ,
'CNN_2_RNN' : self._HP_dict['CNN_2_RNN'] ,
'CNN_3_kernal' : self._HP_dict['CNN_3_kernal'] ,
'CNN_3_RNN' : self._HP_dict['CNN_3_RNN'] ,
'CNN_4_kernal' : self._HP_dict['CNN_4_kernal'] ,
'CNN_4_RNN' : self._HP_dict['CNN_4_RNN'] ,
'CNN_5_kernal' : self._HP_dict['CNN_5_kernal'] ,
'CNN_5_RNN' : self._HP_dict['CNN_5_RNN'] ,
'DNN_1' : self._HP_dict['DNN_1'] ,
'DNN_2' : self._HP_dict['DNN_2'] ,
'DNN_rate' : self._HP_dict['DNN_rate'] ,
'CNN_dropout' : self._HP_dict['CNN_dropout'] ,
'DNN_dropout' : self._HP_dict['DNN_dropout'] ,
'learning_rate' : self._HP_dict['learning_rate'],
}
with tf.summary.create_file_writer(self.log_dir + '/hp_tunning').as_default():
hp.hparams(hparams=hparams)
#print(loss, pearson, spearman)
tf.summary.scalar(self._Metric_name_loss, loss, step=step)
tf.summary.scalar(self._Metric_name_test_pearson, pearson, step=step)
tf.summary.scalar(self._Metric_name_test_spearman, spearman, step=step)
if bench_pearson is not None:
tf.summary.scalar(self._Metric_name_bench_pearson, bench_pearson, step=step)
tf.summary.scalar(self._Metric_name_bench_spearman, bench_spearman, step=step)
class CNNLSTM_3_layer_tensorboard(SpCas9_tensorboard):
def __init__(self, HP_dict, log_dir='tensorboard/', GPU=1, random_seed=1234, verbose=1):
super().__init__(HP_dict, log_dir=log_dir, GPU=GPU, random_seed=random_seed, verbose=verbose)
def TB_HParam_define(self):
self._CNN_1_kernal = hp.HParam('CNN_1_kernal', hp.IntInterval(1, 1024))
self._CNN_1_RNN = hp.HParam('CNN_1_RNN', hp.IntInterval(1, 1024))
self._CNN_2_kernal = hp.HParam('CNN_2_kernal', hp.IntInterval(1, 1024))
self._CNN_2_RNN = hp.HParam('CNN_2_RNN', hp.IntInterval(1, 1024))
self._CNN_3_kernal = hp.HParam('CNN_3_kernal', hp.IntInterval(1, 1024))
self._CNN_3_RNN = hp.HParam('CNN_3_RNN', hp.IntInterval(1, 1024))
self._DNN_1 = hp.HParam('DNN_1', hp.IntInterval(1, 1024))
self._DNN_2 = hp.HParam('DNN_2', hp.IntInterval(1, 1024))
self._DNN_rate = hp.HParam('DNN_rate', hp.IntInterval(1, 1024))
self._CNN_dropout = hp.HParam('CNN_dropout', hp.RealInterval(0.001, 0.5))
self._DNN_dropout = hp.HParam('DNN_dropout', hp.RealInterval(0.001, 0.5))
self._learning_rate = hp.HParam('learning_rate',hp.RealInterval(1e-6, 1.0))
self._Metric_name_loss = 'rate_loss_test'
self._Metric_name_test_pearson = 'pearson_correlation_test'
self._Metric_name_test_spearman = 'spearman_correlation_test'
self._Metric_name_bench_pearson = 'pearson_correlation_bench'
self._Metric_name_bench_spearman = 'spearman_correlation_bench'
with tf.summary.create_file_writer(self.log_dir + '/hp_tunning').as_default():
hp.hparams_config(
hparams=[self._CNN_1_kernal, self._CNN_1_RNN,
self._CNN_2_kernal, self._CNN_2_RNN,
self._CNN_3_kernal, self._CNN_3_RNN,
self._DNN_1, self._DNN_2, self._DNN_rate,
self._CNN_dropout, self._DNN_dropout,
self._learning_rate],
metrics=[hp.Metric(self._Metric_name_loss, display_name='Loss'),
hp.Metric(self._Metric_name_test_pearson, display_name='Pearson Correlation - Test data'),
hp.Metric(self._Metric_name_test_spearman, display_name='Spearman Correlation - Test data'),
hp.Metric(self._Metric_name_bench_pearson, display_name='Pearson Correlation - Bench data'),
hp.Metric(self._Metric_name_bench_spearman, display_name='Spearman Correlation - Bench data')]
)
def Model_define(self):
# Model
self._model_define = CNNLSTM_3_layer_SpCas9(
HP= self._HP_dict,
random_seed = self._random_seed
)
self.model = self._model_define.model
self.model_regression = self._model_define.model_regression()
# Data
self.data = self._model_define.SpCas9_data
self.bench_data = self._model_define.bench_data_endo
def callbacks(self):
return super().callbacks()
def training(self):
return super().training()
def evaluate(self):
return super().evaluate()
def _H_param(self, loss, pearson, spearman, bench_pearson=None, bench_spearman=None, step=3):
hparams = {
'CNN_1_kernal' : self._HP_dict['CNN_1_kernal'] ,
'CNN_1_RNN' : self._HP_dict['CNN_1_RNN'] ,
'CNN_2_kernal' : self._HP_dict['CNN_2_kernal'] ,
'CNN_2_RNN' : self._HP_dict['CNN_2_RNN'] ,
'CNN_3_kernal' : self._HP_dict['CNN_3_kernal'] ,
'CNN_3_RNN' : self._HP_dict['CNN_3_RNN'] ,
'DNN_1' : self._HP_dict['DNN_1'] ,
'DNN_2' : self._HP_dict['DNN_2'] ,
'DNN_rate' : self._HP_dict['DNN_rate'] ,
'CNN_dropout' : self._HP_dict['CNN_dropout'] ,
'DNN_dropout' : self._HP_dict['DNN_dropout'] ,
'learning_rate' : self._HP_dict['learning_rate'],
}
with tf.summary.create_file_writer(self.log_dir + '/hp_tunning').as_default():
hp.hparams(hparams=hparams)
#print(loss, pearson, spearman)
tf.summary.scalar(self._Metric_name_loss, loss, step=step)
tf.summary.scalar(self._Metric_name_test_pearson, pearson, step=step)
tf.summary.scalar(self._Metric_name_test_spearman, spearman, step=step)
if bench_pearson is not None:
tf.summary.scalar(self._Metric_name_bench_pearson, bench_pearson, step=step)
tf.summary.scalar(self._Metric_name_bench_spearman, bench_spearman, step=step)
class CNNLSTM_regression_3_layer_tensorboard(CNNLSTM_3_layer_tensorboard):
def __init__(self, HP_dict, log_dir='tensorboard/', GPU=1, random_seed=1234, verbose=1):
super().__init__(HP_dict, log_dir=log_dir, GPU=GPU, random_seed=random_seed, verbose=verbose)
def TB_HParam_define(self):
return super().TB_HParam_define()
def Model_define(self):
# Model
self._model_define = CNNLSTM_regression_3_layer_SpCas9(
HP= self._HP_dict,
random_seed = self._random_seed
)
self.model = self._model_define.model
self.model_regression = self._model_define.model_regression()
# Data
self.data = self._model_define.SpCas9_data
self.bench_data = self._model_define.bench_data_endo
def callbacks(self):
return super().callbacks()
def training(self):
return super().training()
def evaluate(self):
return super().evaluate()