chore: related #21
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@ -32,7 +32,7 @@ model = keras.Sequential([
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{{- if eq .LayerType 1}}
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{{- if eq .LayerType 1}}
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layers.Rescaling(1./255),
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layers.Rescaling(1./255),
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{{- else if eq .LayerType 2 }}
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{{- else if eq .LayerType 2 }}
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layers.Dense({{ .Shape }}, activation="relu"),
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layers.Dense({{ .Shape }}, activation="sigmoid"),
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{{- else if eq .LayerType 3}}
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{{- else if eq .LayerType 3}}
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layers.Flatten(),
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layers.Flatten(),
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{{- else }}
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{{- else }}
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@ -41,7 +41,17 @@ model = keras.Sequential([
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{{- end }}
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{{- end }}
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])
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])
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model.compile(loss=losses.SparseCategoricalCrossentropy(), optimizer=tf.keras.optimizers.Adam())
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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=100)
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his = model.fit(dataset, validation_data= dataset_validation, epochs=50)
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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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model.save("model.keras")
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