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worked on #32

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This commit is contained in:
Andre Henriques 2023-10-21 00:26:52 +01:00
parent ff9aca2699
commit 805be22388
5 changed files with 179 additions and 77 deletions
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2
logic/models/edit.go
View File

@ -101,7 +101,7 @@ func handleEdit(handle *Handle) {
type defrow struct {
Status int
EpochProgress int
Accuracy int
Accuracy float64
}
def_rows, err := c.Db.Query("select status, epoch_progress, accuracy from model_definition where model_id=$1", model.Id)

101
logic/models/train/train.go
View File

@ -53,6 +53,7 @@ const (
LAYER_INPUT LayerType = 1
LAYER_DENSE = 2
LAYER_FLATTEN = 3
LAYER_SIMPLE_BLOCK = 4
)
func ModelDefinitionUpdateStatus(c *Context, id string, status ModelDefinitionStatus) (err error) {
@ -207,13 +208,13 @@ func trainDefinition(c *Context, model *BaseModel, definition_id string, load_pr
return
}
c.Logger.Info("Model finished training!", "accuracy", accuracy)
accuracy, err = strconv.ParseFloat(string(accuracy_file_bytes), 64)
if err != nil {
return
}
c.Logger.Info("Model finished training!", "accuracy", accuracy)
os.RemoveAll(run_path)
return
}
@ -286,12 +287,11 @@ func trainModel(c *Context, model *BaseModel) {
continue
}
def.epoch += EPOCH_PER_RUN
accuracy = accuracy * 100
int_accuracy := int(accuracy * 100)
if int_accuracy >= def.target_accuracy {
if accuracy >= float64(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)
_, err = c.Db.Exec("update model_definition set accuracy=$1, status=$2, epoch=$3 where id=$4", 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)
@ -310,13 +310,19 @@ func trainModel(c *Context, model *BaseModel) {
}
if def.epoch > MAX_EPOCH {
fmt.Printf("Failed to train definition! Accuracy less %d < %d\n", int_accuracy, def.target_accuracy)
fmt.Printf("Failed to train definition! Accuracy less %f < %d\n", 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, epoch=$2 where id=$3", accuracy, 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
}
}
copy(definitions, newDefinitions)
firstRound = false
@ -418,7 +424,7 @@ func removeFailedDataPoints(c *Context, model *BaseModel) (err error) {
}
// This generates a definition
func generateDefinition(c *Context, model *BaseModel, number_of_classes int, complexity int) *Error {
func generateDefinition(c *Context, model *BaseModel, target_accuracy int, number_of_classes int, complexity int) *Error {
var err error = nil
failed := func() *Error {
ModelUpdateStatus(c, model.Id, FAILED_PREPARING_TRAINING)
@ -426,28 +432,32 @@ func generateDefinition(c *Context, model *BaseModel, number_of_classes int, co
return c.Error500(err)
}
def_id, err := MakeDefenition(c.Db, model.Id, 0)
def_id, err := MakeDefenition(c.Db, model.Id, target_accuracy)
if err != nil {
return failed()
}
order := 1;
// Note the shape for now is no used
err = MakeLayer(c.Db, def_id, 1, LAYER_INPUT, fmt.Sprintf("%d,%d,1", model.Width, model.Height))
err = MakeLayer(c.Db, def_id, order, LAYER_INPUT, fmt.Sprintf("%d,%d,1", model.Width, model.Height))
if err != nil {
return failed()
}
order++;
if complexity == 0 {
err = MakeLayer(c.Db, def_id, 4, LAYER_FLATTEN, "")
err = MakeLayer(c.Db, def_id, order, LAYER_FLATTEN, "")
if err != nil {
return failed()
}
order++;
loop := int(math.Log2(float64(number_of_classes)))
loop := int(math.Log2(float64(number_of_classes))/2)
for i := 0; i < loop; i++ {
err = MakeLayer(c.Db, def_id, 5, LAYER_DENSE, fmt.Sprintf("%d,1", number_of_classes*(loop - i)))
err = MakeLayer(c.Db, def_id, order, LAYER_DENSE, fmt.Sprintf("%d,1", number_of_classes*(loop-i)))
order++;
if err != nil {
ModelUpdateStatus(c, model.Id, FAILED_PREPARING_TRAINING)
// TODO improve this response
@ -455,6 +465,38 @@ func generateDefinition(c *Context, model *BaseModel, number_of_classes int, co
}
}
} else if (complexity == 1) {
loop := int((math.Log(float64(model.Width))/math.Log(float64(10))))
if loop == 0 {
loop = 1;
}
for i := 0; i < loop; i++ {
err = MakeLayer(c.Db, def_id, order, LAYER_SIMPLE_BLOCK, "")
order++;
if err != nil {
return failed();
}
}
err = MakeLayer(c.Db, def_id, order, LAYER_FLATTEN, "")
if err != nil {
return failed()
}
order++;
loop = int((math.Log(float64(number_of_classes))/math.Log(float64(10)))/2)
if loop == 0 {
loop = 1;
}
for i := 0; i < loop; i++ {
err = MakeLayer(c.Db, def_id, order, LAYER_DENSE, fmt.Sprintf("%d,1", number_of_classes*(loop-i)))
order++;
if err != nil {
return failed();
}
}
} else {
c.Logger.Error("Unkown complexity", "complexity", complexity)
return failed()
@ -468,7 +510,7 @@ func generateDefinition(c *Context, model *BaseModel, number_of_classes int, co
return nil
}
func generateDefinitions(c *Context, model *BaseModel, number_of_models int) *Error {
func generateDefinitions(c *Context, model *BaseModel, target_accuracy int, number_of_models int) *Error {
cls, err := model_classes.ListClasses(c.Db, model.Id)
if err != nil {
ModelUpdateStatus(c, model.Id, FAILED_PREPARING_TRAINING)
@ -481,10 +523,19 @@ func generateDefinitions(c *Context, model *BaseModel, number_of_models int) *Er
return c.Error500(err)
}
for i := 0; i < number_of_models; i++ {
// TODO handle incrisea the complexity
generateDefinition(c, model, len(cls), 0)
if (number_of_models == 1) {
if (model.Width < 100 && model.Height < 100 && len(cls) < 30) {
generateDefinition(c, model, target_accuracy, len(cls), 0)
} else {
generateDefinition(c, model, target_accuracy, len(cls), 1)
}
} else {
// TODO handle incrisea the complexity
for i := 0; i < number_of_models; i++ {
generateDefinition(c, model, target_accuracy, len(cls), 0)
}
}
return nil
}
@ -551,7 +602,7 @@ func handleTrain(handle *Handle) {
return ErrorCode(nil, 400, c.AddMap(nil))
}
full_error := generateDefinitions(c, model, number_of_models)
full_error := generateDefinitions(c, model, accuracy, number_of_models)
if full_error != nil {
return full_error
}
@ -573,11 +624,15 @@ func handleTrain(handle *Handle) {
f := r.URL.Query()
if !CheckId(f, "model_id") || !CheckId(f, "definition") || CheckEmpty(f, "epoch") {
c.Logger.Warn("Invalid: model_id or definition or epoch")
accuracy := 0.0
if !CheckId(f, "model_id") || !CheckId(f, "definition") || CheckEmpty(f, "epoch") || !CheckFloat64(f, "accuracy", &accuracy){
c.Logger.Warn("Invalid: model_id or definition or epoch or accuracy")
return c.UnsafeErrorCode(nil, 400, nil)
}
accuracy = accuracy * 100
model_id := f.Get("model_id")
def_id := f.Get("definition")
epoch, err := strconv.Atoi(f.Get("epoch"))
@ -610,7 +665,9 @@ func handleTrain(handle *Handle) {
return c.UnsafeErrorCode(nil, 400, nil)
}
_, err = c.Db.Exec("update model_definition set epoch_progress=$1 where id=$2", epoch, def_id)
c.Logger.Info("Updated model_definition!", "model", model_id, "progress", epoch, "accuracy", accuracy)
_, err = c.Db.Exec("update model_definition set epoch_progress=$1, accuracy=$2 where id=$3", epoch, accuracy, def_id)
if err != nil {
return c.Error500(err)
}

15
logic/utils/utils.go
View File

@ -31,6 +31,21 @@ func CheckNumber(f url.Values, path string, number *int) bool {
return true
}
func CheckFloat64(f url.Values, path string, number *float64) bool {
if CheckEmpty(f, path) {
fmt.Println("here", path)
fmt.Println(f.Get(path))
return false
}
n, err := strconv.ParseFloat(f.Get(path), 64)
if err != nil {
fmt.Println(err)
return false
}
*number = n
return true
}
func CheckId(f url.Values, path string) bool {
return !CheckEmpty(f, path) && IsValidUUID(f.Get(path))
}

2
sql/models.sql
View File

@ -44,7 +44,7 @@ create table if not exists model_data_point (
create table if not exists model_definition (
id uuid primary key default gen_random_uuid(),
model_id uuid references models (id) on delete cascade,
accuracy integer default 0,
accuracy real default 0,
target_accuracy integer not null,
epoch integer default 0,
-- TODO add max epoch

52
views/py/python_model_template.py
View File

@ -7,10 +7,8 @@ from keras import layers, losses, optimizers
import requests
class NotifyServerCallback(tf.keras.callbacks.Callback):
def on_epoch_begin(self, epoch, *args, **kwargs):
if (epoch % 5) == 0:
# TODO change this
requests.get(f'http://localhost:8000/model/epoch/update?model_id={{.Model.Id}}&epoch={epoch}&definition={{.DefId}}')
def on_epoch_end(self, epoch, log, *args, **kwargs):
requests.get(f'http://localhost:8000/model/epoch/update?model_id={{.Model.Id}}&epoch={epoch}&accuracy={log["accuracy"]}&definition={{.DefId}}')
DATA_DIR = "{{ .DataDir }}"
@ -84,7 +82,7 @@ def filterDataset(path):
seed = random.randint(0, 100000000)
batch_size = 100
batch_size = 64
# Read all the files from the direcotry
list_ds = tf.data.Dataset.list_files(str(f'{DATA_DIR}/*'), shuffle=False)
@ -102,23 +100,55 @@ val_ds = list_ds.take(val_size)
dataset = prepare_dataset(train_ds)
dataset_validation = prepare_dataset(val_ds)
track = 0
def addBlock(
b_size: int,
filter_size: int,
kernel_size: int = 3,
top: bool = True,
pooling_same: bool = False,
pool_func=layers.MaxPool2D
):
global track
model = keras.Sequential(name=f"{track}-{b_size}-{filter_size}-{kernel_size}")
track += 1
for _ in range(b_size):
model.add(layers.Conv2D(
filter_size,
kernel_size,
padding="same"
))
model.add(layers.ReLU())
if top:
if pooling_same:
model.add(pool_func(padding="same", strides=(1, 1)))
else:
model.add(pool_func())
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())
model.add(layers.Dropout(0.4))
return model
{{ if .LoadPrev }}
model = tf.keras.saving.load_model('{{.LastModelRunPath}}')
{{ else }}
model = keras.Sequential([
model = keras.Sequential()
{{- range .Layers }}
{{- if eq .LayerType 1}}
layers.Rescaling(1./255),
model.add(layers.Rescaling(1./255))
{{- else if eq .LayerType 2 }}
layers.Dense({{ .Shape }}, activation="sigmoid"),
model.add(layers.Dense({{ .Shape }}, activation="sigmoid"))
{{- else if eq .LayerType 3}}
layers.Flatten(),
model.add(layers.Flatten())
{{- else if eq .LayerType 4}}
model.add(addBlock(2, 128, 3, pool_func=layers.AveragePooling2D))
{{- else }}
ERROR
{{- end }}
{{- end }}
])
{{ end }}
model.compile(
@ -126,7 +156,7 @@ model.compile(
optimizer=tf.keras.optimizers.Adam(),
metrics=['accuracy'])
his = model.fit(dataset, validation_data= dataset_validation, epochs={{.EPOCH_PER_RUN}}, callbacks=[NotifyServerCallback()])
his = model.fit(dataset, validation_data= dataset_validation, epochs={{.EPOCH_PER_RUN}}, callbacks=[NotifyServerCallback()], use_multiprocessing = True)
acc = his.history["accuracy"]