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feat: closes #40

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This commit is contained in:
Andre Henriques
2023-10-19 10:44:13 +01:00
parent f163e25fba
commit 2c3539b81a
4 changed files with 184 additions and 105 deletions
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94
logic/models/train/train.go
View File

@@ -17,6 +17,9 @@ import (
. "git.andr3h3nriqu3s.com/andr3/fyp/logic/utils"
)
const EPOCH_PER_RUN = 20;
const MAX_EPOCH = 100
func MakeDefenition(db *sql.DB, model_id string, target_accuracy int) (id string, err error) {
id = ""
rows, err := db.Query("insert into model_definition (model_id, target_accuracy) values ($1, $2) returning id;", model_id, target_accuracy)
@@ -34,6 +37,7 @@ func MakeDefenition(db *sql.DB, model_id string, target_accuracy int) (id string
type ModelDefinitionStatus int
const (
MODEL_DEFINITION_STATUS_CANCELD_TRAINING = -4
MODEL_DEFINITION_STATUS_FAILED_TRAINING = -3
MODEL_DEFINITION_STATUS_PRE_INIT ModelDefinitionStatus = 1
MODEL_DEFINITION_STATUS_INIT = 2
@@ -104,7 +108,8 @@ func generateCvs(c *Context, run_path string, model_id string) (count int, err e
return
}
func trainDefinition(c *Context, model *BaseModel, definition_id string) (accuracy float64, err error) {
func trainDefinition(c *Context, model *BaseModel, definition_id string, load_prev bool) (accuracy float64, err error) {
c.Logger.Warn("About to start training definition")
accuracy = 0
layers, err := c.Db.Query("select layer_type, shape from model_definition_layer where def_id=$1 order by layer_order asc;", definition_id)
if err != nil {
@@ -153,6 +158,9 @@ func trainDefinition(c *Context, model *BaseModel, definition_id string) (accura
return
}
// Copy result around
result_path := path.Join("savedData", model.Id, "defs", definition_id)
if err = tmpl.Execute(f, AnyMap{
"Layers": got,
"Size": got[0].Shape,
@@ -160,7 +168,10 @@ func trainDefinition(c *Context, model *BaseModel, definition_id string) (accura
"RunPath": run_path,
"ColorMode": model.ImageMode,
"Model": model,
"EPOCH_PER_RUN": EPOCH_PER_RUN,
"DefId": definition_id,
"LoadPrev": load_prev,
"LastModelRunPath": path.Join(getDir(), result_path, "model.keras"),
}); err != nil {
return
}
@@ -172,9 +183,6 @@ func trainDefinition(c *Context, model *BaseModel, definition_id string) (accura
return
}
// Copy result around
result_path := path.Join("savedData", model.Id, "defs", definition_id)
if err = os.MkdirAll(result_path, os.ModePerm); err != nil {
return
}
@@ -183,6 +191,10 @@ func trainDefinition(c *Context, model *BaseModel, definition_id string) (accura
return
}
if err = exec.Command("cp", "-r", path.Join(run_path, "model.keras"), path.Join(result_path, "model.keras")).Run(); err != nil {
return
}
accuracy_file, err := os.Open(path.Join(run_path, "accuracy.val"))
if err != nil {
return
@@ -194,7 +206,7 @@ func trainDefinition(c *Context, model *BaseModel, definition_id string) (accura
return
}
fmt.Println(string(accuracy_file_bytes))
c.Logger.Info("Model finished training!", "accuracy", accuracy)
accuracy, err = strconv.ParseFloat(string(accuracy_file_bytes), 64)
if err != nil {
@@ -205,8 +217,25 @@ func trainDefinition(c *Context, model *BaseModel, definition_id string) (accura
return
}
func remove[T interface{}](lst []T, i int) []T {
lng := len(lst)
if i >= lng {
return []T{}
}
if i+1 >= lng {
return lst[:lng-1]
}
if i == 0 {
return lst[1:]
}
return append(lst[:i], lst[i+1:]...)
}
func trainModel(c *Context, model *BaseModel) {
definitionsRows, err := c.Db.Query("select id, target_accuracy from model_definition where status=$1 and model_id=$2", MODEL_DEFINITION_STATUS_INIT, model.Id)
definitionsRows, err := c.Db.Query("select id, target_accuracy, epoch from model_definition where status=$1 and model_id=$2", MODEL_DEFINITION_STATUS_INIT, model.Id)
if err != nil {
c.Logger.Error("Failed to trainModel!Err:")
c.Logger.Error(err)
@@ -218,13 +247,14 @@ func trainModel(c *Context, model *BaseModel) {
type row struct {
id string
target_accuracy int
epoch int
}
definitions := []row{}
for definitionsRows.Next() {
var rowv row
if err = definitionsRows.Scan(&rowv.id, &rowv.target_accuracy); err != nil {
if err = definitionsRows.Scan(&rowv.id, &rowv.target_accuracy, &rowv.epoch); err != nil {
c.Logger.Error("Failed to train Model Could not read definition from db!Err:")
c.Logger.Error(err)
ModelUpdateStatus(c, model.Id, FAILED_TRAINING)
@@ -239,30 +269,58 @@ func trainModel(c *Context, model *BaseModel) {
return
}
for _, def := range definitions {
toTrain := len(definitions)
firstRound := true
var newDefinitions = []row{}
copy(newDefinitions, definitions)
for {
for i, def := range definitions {
ModelDefinitionUpdateStatus(c, def.id, MODEL_DEFINITION_STATUS_TRAINING)
accuracy, err := trainDefinition(c, model, def.id)
accuracy, err := trainDefinition(c, model, def.id, !firstRound)
if err != nil {
c.Logger.Error("Failed to train definition!Err:")
c.Logger.Error(err)
c.Logger.Error("Failed to train definition!Err:", "err", err)
ModelDefinitionUpdateStatus(c, def.id, MODEL_DEFINITION_STATUS_FAILED_TRAINING)
toTrain = toTrain - 1
newDefinitions = remove(newDefinitions, i)
continue
}
def.epoch += EPOCH_PER_RUN
int_accuracy := int(accuracy * 100)
if int_accuracy < def.target_accuracy {
if int_accuracy >= def.target_accuracy {
c.Logger.Info("Found a definition that reaches target_accuracy!")
_, err = c.Db.Exec("update model_definition set accuracy=$1, status=$2, epoch=$3 where id=$4", int_accuracy, MODEL_DEFINITION_STATUS_TRANIED, def.epoch, def.id)
if err != nil {
c.Logger.Error("Failed to train definition!Err:\n", "err", err)
ModelUpdateStatus(c, model.Id, FAILED_TRAINING)
return
}
_, err = c.Db.Exec("update model_definition set status=$1 where id!=$2 and model_id=$3 and status!=$4", MODEL_DEFINITION_STATUS_CANCELD_TRAINING, def.id, model.Id, MODEL_DEFINITION_STATUS_FAILED_TRAINING)
if err != nil {
c.Logger.Error("Failed to train definition!Err:\n", "err", err)
ModelUpdateStatus(c, model.Id, FAILED_TRAINING)
return
}
toTrain = 0
break
}
if def.epoch > MAX_EPOCH {
fmt.Printf("Failed to train definition! Accuracy less %d < %d\n", int_accuracy, def.target_accuracy)
ModelDefinitionUpdateStatus(c, def.id, MODEL_DEFINITION_STATUS_FAILED_TRAINING)
toTrain = toTrain - 1
newDefinitions = remove(newDefinitions, i)
continue
}
_, err = c.Db.Exec("update model_definition set accuracy=$1, status=$2 where id=$3", int_accuracy, MODEL_DEFINITION_STATUS_TRANIED, def.id)
if err != nil {
fmt.Printf("Failed to train definition!Err:\n")
fmt.Println(err)
ModelUpdateStatus(c, model.Id, FAILED_TRAINING)
return
}
copy(definitions, newDefinitions)
firstRound = false
if toTrain == 0 {
break
}
}

1
sql/models.sql
View File

@@ -40,7 +40,6 @@ create table if not exists model_data_point (
status_message text
);
-- drop table if exists model_definition;
-- drop table if exists model_definition;
create table if not exists model_definition (
id uuid primary key default gen_random_uuid(),

33
views/models/edit.html
View File

@@ -434,19 +434,36 @@
{{/* TODO improve this */}}
Training the model...<br/>
{{/* TODO Add progress status on definitions */}}
<table>
<thead>
<tr>
<th>
Status
</th>
<th>
EpochProgress
</th>
<th>
Accuracy
</th>
</tr>
</thead>
<tbody>
{{ range .Defs}}
<div>
<div>
<tr>
<td>
{{.Status}}
</div>
<div>
</td>
<td>
{{.EpochProgress}}
</div>
<div>
</td>
<td>
{{.Accuracy}}
</div>
</div>
</td>
</tr>
{{ end }}
</tbody>
</table>
{{/* TODO Add ability to stop training */}}
</div>
{{/* Model Ready */}}

11
views/py/python_model_template.py
View File

@@ -93,6 +93,10 @@ val_ds = list_ds.take(val_size)
dataset = prepare_dataset(train_ds)
dataset_validation = prepare_dataset(val_ds)
{{ if .LoadPrev }}
model = tf.keras.saving.load_model('{{.LastModelRunPath}}')
{{ else }}
model = keras.Sequential([
{{- range .Layers }}
{{- if eq .LayerType 1}}
@@ -106,13 +110,14 @@ model = keras.Sequential([
{{- end }}
{{- end }}
])
{{ end }}
model.compile(
loss=losses.SparseCategoricalCrossentropy(),
optimizer=tf.keras.optimizers.Adam(),
metrics=['accuracy'])
his = model.fit(dataset, validation_data= dataset_validation, epochs=50, callbacks=[NotifyServerCallback()])
his = model.fit(dataset, validation_data= dataset_validation, epochs={{.EPOCH_PER_RUN}}, callbacks=[NotifyServerCallback()])
acc = his.history["accuracy"]
@@ -120,6 +125,6 @@ f = open("accuracy.val", "w")
f.write(str(acc[-1]))
f.close()
tf.saved_model.save(model, "model")
# model.save("model.keras", save_format="tf")
tf.saved_model.save(model, "model")
model.save("model.keras")