more work on the fyp
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70b4141223
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cbfd49c5fb
@ -237,7 +237,7 @@ func trainDefinitionExp(c *Context, model *BaseModel, definition_id string, load
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return
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}
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layers, err := c.Db.Query("select layer_type, shape from model_definition_layer where def_id=$1 order by layer_order asc;", definition_id)
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layers, err := c.Db.Query("select layer_type, shape, exp_type from model_definition_layer where def_id=$1 order by layer_order asc;", definition_id)
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if err != nil {
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return
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}
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@ -246,22 +246,35 @@ func trainDefinitionExp(c *Context, model *BaseModel, definition_id string, load
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type layerrow struct {
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LayerType int
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Shape string
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ExpType int
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LayerNum int
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}
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got := []layerrow{}
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remove_top_count := 1
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i := 1
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for layers.Next() {
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var row = layerrow{}
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if err = layers.Scan(&row.LayerType, &row.Shape); err != nil {
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if err = layers.Scan(&row.LayerType, &row.Shape, &row.ExpType); err != nil {
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return
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}
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row.LayerNum = i
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if row.ExpType == 2 {
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remove_top_count += 1
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}
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row.Shape = shapeToSize(row.Shape)
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got = append(got, row)
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i += 1
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}
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got = append(got, layerrow{
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LayerType: LAYER_DENSE,
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Shape: fmt.Sprintf("%d", exp.end - exp.start),
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Shape: fmt.Sprintf("%d", exp.end-exp.start),
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ExpType: 2,
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LayerNum: i,
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})
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// Generate run folder
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@ -287,7 +300,7 @@ func trainDefinitionExp(c *Context, model *BaseModel, definition_id string, load
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}
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defer f.Close()
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tmpl, err := template.New("python_model_template.py").ParseFiles("views/py/python_model_template.py")
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tmpl, err := template.New("python_model_template-exp.py").ParseFiles("views/py/python_model_template.py")
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if err != nil {
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return
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}
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@ -307,6 +320,7 @@ func trainDefinitionExp(c *Context, model *BaseModel, definition_id string, load
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"LoadPrev": load_prev,
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"LastModelRunPath": path.Join(getDir(), result_path, "model.keras"),
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"SaveModelPath": path.Join(getDir(), result_path),
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"RemoveTopCount": remove_top_count,
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}); err != nil {
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return
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}
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177
views/py/python_model_template-exp.py
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177
views/py/python_model_template-exp.py
Normal file
@ -0,0 +1,177 @@
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import tensorflow as tf
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import random
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import pandas as pd
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from tensorflow import keras
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from tensorflow.data import AUTOTUNE
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from keras import layers, losses, optimizers
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import requests
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class NotifyServerCallback(tf.keras.callbacks.Callback):
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def on_epoch_end(self, epoch, log, *args, **kwargs):
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requests.get(f'http://localhost:8000/model/epoch/update?model_id={{.Model.Id}}&epoch={epoch + 1}&accuracy={log["accuracy"]}&definition={{.DefId}}')
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DATA_DIR = "{{ .DataDir }}"
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image_size = ({{ .Size }})
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df = pd.read_csv("{{ .RunPath }}/train.csv", dtype=str)
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keys = tf.constant(df['Id'].dropna())
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values = tf.constant(list(map(int, df['Index'].dropna())))
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table = tf.lookup.StaticHashTable(
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initializer=tf.lookup.KeyValueTensorInitializer(
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keys=keys,
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values=values,
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),
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default_value=tf.constant(-1),
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name="Indexes"
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)
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DATA_DIR_PREPARE = DATA_DIR + "/"
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#based on https://www.tensorflow.org/tutorials/load_data/images
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def pathToLabel(path):
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path = tf.strings.regex_replace(path, DATA_DIR_PREPARE, "")
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{{ if eq .Model.Format "png" }}
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path = tf.strings.regex_replace(path, ".png", "")
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{{ else if eq .Model.Format "jpeg" }}
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path = tf.strings.regex_replace(path, ".jpeg", "")
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{{ else }}
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ERROR
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{{ end }}
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return table.lookup(tf.strings.as_string([path]))
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def decode_image(img):
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{{ if eq .Model.Format "png" }}
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img = tf.io.decode_png(img, channels={{.ColorMode}})
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{{ else if eq .Model.Format "jpeg" }}
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img = tf.io.decode_jpeg(img, channels={{.ColorMode}})
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{{ else }}
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ERROR
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{{ end }}
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return tf.image.resize(img, image_size)
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def process_path(path):
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label = pathToLabel(path)
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img = tf.io.read_file(path)
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img = decode_image(img)
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return img, label
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def configure_for_performance(ds: tf.data.Dataset, size: int) -> tf.data.Dataset:
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#ds = ds.cache()
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ds = ds.shuffle(buffer_size=size)
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ds = ds.batch(batch_size)
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ds = ds.prefetch(AUTOTUNE)
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return ds
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def prepare_dataset(ds: tf.data.Dataset, size: int) -> tf.data.Dataset:
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ds = ds.map(process_path, num_parallel_calls=AUTOTUNE)
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ds = configure_for_performance(ds, size)
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return ds
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def filterDataset(path):
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path = tf.strings.regex_replace(path, DATA_DIR_PREPARE, "")
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{{ if eq .Model.Format "png" }}
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path = tf.strings.regex_replace(path, ".png", "")
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{{ else if eq .Model.Format "jpeg" }}
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path = tf.strings.regex_replace(path, ".jpeg", "")
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{{ else }}
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ERROR
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{{ end }}
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return tf.reshape(table.lookup(tf.strings.as_string([path])), []) != -1
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seed = random.randint(0, 100000000)
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batch_size = 64
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# Read all the files from the direcotry
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list_ds = tf.data.Dataset.list_files(str(f'{DATA_DIR}/*'), shuffle=False)
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list_ds = list_ds.filter(filterDataset)
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image_count = len(list(list_ds.as_numpy_iterator()))
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list_ds = list_ds.shuffle(image_count, seed=seed)
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val_size = int(image_count * 0.3)
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train_ds = list_ds.skip(val_size)
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val_ds = list_ds.take(val_size)
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dataset = prepare_dataset(train_ds, image_count)
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dataset_validation = prepare_dataset(val_ds, val_size)
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track = 0
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def addBlock(
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b_size: int,
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filter_size: int,
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kernel_size: int = 3,
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top: bool = True,
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pooling_same: bool = False,
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pool_func=layers.MaxPool2D,
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layerNum = 0
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):
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global track
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# model = keras.Sequential(name=f"{track}-{b_size}-{filter_size}-{kernel_size}")
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model = keras.Sequential(name=f"layer{layerNum}")
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track += 1
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for _ in range(b_size):
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model.add(layers.Conv2D(
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filter_size,
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kernel_size,
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padding="same"
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))
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model.add(layers.ReLU())
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if top:
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if pooling_same:
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model.add(pool_func(padding="same", strides=(1, 1)))
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else:
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model.add(pool_func())
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model.add(layers.BatchNormalization())
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model.add(layers.LeakyReLU())
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model.add(layers.Dropout(0.4))
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return model
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{{ if .LoadPrev }}
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model = tf.keras.saving.load_model('{{.LastModelRunPath}}')
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{{ else }}
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model = keras.Sequential()
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{{- range .Layers }}
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{{- if eq .LayerType 1}}
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model.add(layers.Rescaling(1./255, name="layer{{ .LayerNum }}"))
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{{- else if eq .LayerType 2 }}
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model.add(layers.Dense({{ .Shape }}, activation="sigmoid", name="layer{{ .LayerNum }}"))
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{{- else if eq .LayerType 3}}
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model.add(layers.Flatten(name="layer{{ .LayerNum }}"))
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{{- else if eq .LayerType 4}}
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model.add(addBlock(2, 128, 3, pool_func=layers.AveragePooling2D, layerNum={{.LayerNum}}))
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{{- else }}
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ERROR
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{{- end }}
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{{- end }}
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{{ end }}
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model.compile(
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loss=losses.SparseCategoricalCrossentropy(),
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optimizer=tf.keras.optimizers.Adam(),
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metrics=['accuracy'])
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his = model.fit(dataset, validation_data= dataset_validation, epochs={{.EPOCH_PER_RUN}}, callbacks=[
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NotifyServerCallback(),
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tf.keras.callbacks.EarlyStopping("loss", mode="min", patience=5)], use_multiprocessing = True)
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acc = his.history["accuracy"]
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f = open("accuracy.val", "w")
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f.write(str(acc[-1]))
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f.close()
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tf.saved_model.save(model, "{{ .SaveModelPath }}/model")
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model.save("{{ .SaveModelPath }}/model.keras")
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