60 lines
1.3 KiB
Python
60 lines
1.3 KiB
Python
import tensorflow as tf
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import random
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from tensorflow import keras
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from keras import layers, losses, optimizers
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seed = random.randint(0, 100000000)
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batch_size = 100
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dataset = keras.utils.image_dataset_from_directory(
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"{{ .DataDir }}",
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color_mode="rgb",
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validation_split=0.2,
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label_mode='int',
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seed=seed,
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subset="training",
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image_size=({{ .Size }}),
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batch_size=batch_size)
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dataset_validation = keras.utils.image_dataset_from_directory(
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"{{ .DataDir }}",
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color_mode="rgb",
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validation_split=0.2,
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label_mode='int',
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seed=seed,
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subset="validation",
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image_size=({{ .Size }}),
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batch_size=batch_size)
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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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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=70)
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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", save_format="tf")
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