Commit 5df14c66 authored Jan 27, 2023 by Dennis Willers

Umbenennen in activation_function_2

parent e880a33e

main.py

+2 −2

Original line number	Original line	Diff line number	Diff line
	@@ -24,7 +24,7 @@ def run_cross_validation():
	if optimization_method.name in config['knn']['exception_optimization_method']:		if optimization_method.name in config['knn']['exception_optimization_method']:
	continue		continue
	for activation_function_2 in Aktivierungsfunktion:		for activation_function_2 in Aktivierungsfunktion:
	if activation_function_2.name in config['knn']['exception_activation_funktion_1']:		if activation_function_2.name in config['knn']['exception_activation_funktion_2']:
	continue		continue
	for activation_function_128 in Aktivierungsfunktion:		for activation_function_128 in Aktivierungsfunktion:
	if check_if_kombination_is_not_allowed(		if check_if_kombination_is_not_allowed(
	@@ -38,7 +38,7 @@ def run_cross_validation():
	r = 1		r = 1
	config_knn = ConfigKNN(		config_knn = ConfigKNN(
	excluded_folder=Markt.Kein_Markt,		excluded_folder=Markt.Kein_Markt,
	activation_function_1_units=activation_function_2,		activation_function_2_units=activation_function_2,
	activation_function_128_units=activation_function_128,		activation_function_128_units=activation_function_128,
	optimization_method=optimization_method		optimization_method=optimization_method
	)		)

ressources/config/config.yaml

+1 −1

Original line number	Original line	Diff line number	Diff line
	@@ -7,7 +7,7 @@ knn:
	epochs: 10		epochs: 10
	exception_optimization_method:		exception_optimization_method:
	['SGD']		['SGD']
	exception_activation_funktion_1:		exception_activation_funktion_2:
	['ReLU']		['ReLU']
	ignoreKnnCombinations:		ignoreKnnCombinations:
	[		[

src/knn/configKNN.py

+4 −4

Original line number	Original line	Diff line number	Diff line
	@@ -7,16 +7,16 @@ class ConfigKNN:
	def __init__(self,		def __init__(self,
	excluded_folder=Markt.Kein_Markt,		excluded_folder=Markt.Kein_Markt,
	activation_function_128_units=Aktivierungsfunktion.ReLU,		activation_function_128_units=Aktivierungsfunktion.ReLU,
	activation_function_1_units=Aktivierungsfunktion.sigmoid,		activation_function_2_units=Aktivierungsfunktion.sigmoid,
	optimization_method=Optimierungsverfahren.SGD):		optimization_method=Optimierungsverfahren.SGD):
	self.excluded_folder = excluded_folder		self.excluded_folder = excluded_folder
	self.activation_function_128_units = activation_function_128_units		self.activation_function_128_units = activation_function_128_units
	self.activation_function_1_units = activation_function_1_units		self.activation_function_2_units = activation_function_2_units
	self.optimization_method = optimization_method		self.optimization_method = optimization_method

	def __str__(self):		def __str__(self):
	return "optimization_method: " \		return "optimization_method: " \
	+ self.optimization_method.name + " - activation_function_128_units: " \		+ self.optimization_method.name + " - activation_function_128_units: " \
	+ self.activation_function_128_units.name + ' - activation_function_1_units: ' \		+ self.activation_function_128_units.name + ' - activation_function_2_units: ' \
	+ self.activation_function_1_units.name + ' - excluded_folder: ' \		+ self.activation_function_2_units.name + ' - excluded_folder: ' \
	+ self.excluded_folder.name		+ self.excluded_folder.name

src/knn/defineKNN.py

+1 −1

Original line number	Original line	Diff line number	Diff line
	@@ -14,7 +14,7 @@ def define_model(config_knn):
	# Die Fully-Connected-Schichten werden definiert		# Die Fully-Connected-Schichten werden definiert
	flat = tf.keras.layers.Flatten()(model.layers[-1].output)		flat = tf.keras.layers.Flatten()(model.layers[-1].output)
	output_layer_1 = get_next_layer(128, config_knn.activation_function_128_units, flat)		output_layer_1 = get_next_layer(128, config_knn.activation_function_128_units, flat)
	output_layer_2 = get_next_layer(2, config_knn.activation_function_1_units, output_layer_1)		output_layer_2 = get_next_layer(2, config_knn.activation_function_2_units, output_layer_1)

	# Die Schichten des Modells werden definiert		# Die Schichten des Modells werden definiert
	model = tf.keras.models.Model(inputs=model.inputs, outputs=output_layer_2)		model = tf.keras.models.Model(inputs=model.inputs, outputs=output_layer_2)

src/result/createExcelFile.py

+2 −2

Original line number	Original line	Diff line number	Diff line
	@@ -25,7 +25,7 @@ def create_excel_result(worksheet, r, callback, config, config_knn, evaluate_met
	r = r + 1		r = r + 1
	worksheet.cell(row=r, column=2).value = config_knn.optimization_method.name		worksheet.cell(row=r, column=2).value = config_knn.optimization_method.name
	worksheet.cell(row=r, column=3).value = config_knn.activation_function_128_units.name		worksheet.cell(row=r, column=3).value = config_knn.activation_function_128_units.name
	worksheet.cell(row=r, column=4).value = config_knn.activation_function_1_units.name		worksheet.cell(row=r, column=4).value = config_knn.activation_function_2_units.name
	worksheet.cell(row=r, column=5).value = config_knn.excluded_folder.name		worksheet.cell(row=r, column=5).value = config_knn.excluded_folder.name
	r = r + 2		r = r + 2

	@@ -148,7 +148,7 @@ def save_excel(workbook, config, config_knn):
	now = datetime.now()		now = datetime.now()
	date_time = now.strftime("%d_%m_%y__%H_%M")		date_time = now.strftime("%d_%m_%y__%H_%M")
	file_name = config_knn.optimization_method.name + "_" + \		file_name = config_knn.optimization_method.name + "_" + \
	config_knn.activation_function_1_units.name + "_" + \		config_knn.activation_function_2_units.name + "_" + \
	config_knn.activation_function_128_units.name + "_" + date_time + ".xlsx"		config_knn.activation_function_128_units.name + "_" + date_time + ".xlsx"
	# Speichern Sie die Arbeitsmappe		# Speichern Sie die Arbeitsmappe
	workbook.save(config["result"]["excel_path"] + file_name)		workbook.save(config["result"]["excel_path"] + file_name)