131 lines
3.4 KiB
Python
131 lines
3.4 KiB
Python
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_begin(self, epoch, *args, **kwargs):
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if (epoch % 5) == 0:
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# TODO change this
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requests.get(f'http://localhost:8000/model/epoch/update?model_id={{.Model.Id}}&epoch={epoch}&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, ".jpg", "")
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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) -> tf.data.Dataset:
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#ds = ds.cache()
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ds = ds.shuffle(buffer_size= 1000)
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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) -> 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)
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return ds
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seed = random.randint(0, 100000000)
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batch_size = 100
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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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image_count = len(list_ds)
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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)
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dataset_validation = prepare_dataset(val_ds)
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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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layers.Rescaling(1./255),
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{{- else if eq .LayerType 2 }}
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layers.Dense({{ .Shape }}, activation="sigmoid"),
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{{- else if eq .LayerType 3}}
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layers.Flatten(),
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{{- else }}
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ERROR
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{{- end }}
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{{- end }}
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])
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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=[NotifyServerCallback()])
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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, "model")
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model.save("model.keras")
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