393 lines
20 KiB
Python
393 lines
20 KiB
Python
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# -------------------------------------------------------#
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# Скрипт для сравнительного тестирования моделей с #
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# использованиеи слоя Dropout и без. #
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# При обучении моделей из обучающей выборки выделяется #
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# 10% выборки для валидации результатов. #
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# После обучения проводится проверка работоспособности #
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# модели на тестовой выборке (отдельный файл данных) #
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# -------------------------------------------------------#
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# Импорт библиотек
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import os
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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import matplotlib.pyplot as plt
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import MetaTrader5 as mt5
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# Подключаемся к терминалу MetaTrader 5
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if not mt5.initialize():
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print("initialize() failed, error code =",mt5.last_error())
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quit()
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# Запрашиваем путь в "песочницу"
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path=os.path.join(mt5.terminal_info().data_path,r'MQL5\Files')
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mt5.shutdown()
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# Загрузка обучающей выборки
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filename = os.path.join(path,'study_data.csv')
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filename_not_norm = os.path.join(path,'study_data_not_norm.csv')
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data = np.asarray( pd.read_table(filename,
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sep=',',
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header=None,
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skipinitialspace=True,
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encoding='utf-8',
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float_precision='high',
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dtype=np.float64,
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low_memory=False))
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# Разделение обучающей выборки на исходные данные и цели
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targets=2
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inputs=data.shape[1]-targets
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train_data=data[:,0:inputs]
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train_target=data[:,inputs:]
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#загрузка не нормированной обучающей выборки
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data = np.asarray( pd.read_table(filename_not_norm,
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sep=',',
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header=None,
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skipinitialspace=True,
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encoding='utf-8',
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float_precision='high',
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dtype=np.float64,
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low_memory=False))
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# Разделение не нормированной обучающей выборки на исходные данные и цели
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train_nn_data=data[:,0:inputs]
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train_nn_target=data[:,inputs:]
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del data
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# Созданиие первой модели с одним скрытым слоем
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model1 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(targets, activation=tf.nn.tanh)
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])
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callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=20)
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model1.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model1.summary()
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# Добавление Dropout в модель с одним скрытым слоем
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model1do = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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keras.layers.Dropout(0.3),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(targets, activation=tf.nn.tanh)
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])
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model1do.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model1do.summary()
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# Добавление пакетной нормазизации для исходных данных в модель с одним скрытым слоем
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model1bn = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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keras.layers.BatchNormalization(),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(targets, activation=tf.nn.tanh)
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])
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model1bn.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model1bn.summary()
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# Добавление Dropout в модель с пакетной нормазизацией исходных данных и одним скрытым слоем
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model1bndo = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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keras.layers.BatchNormalization(),
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keras.layers.Dropout(0.3),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(targets, activation=tf.nn.tanh)
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])
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model1bndo.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model1bndo.summary()
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# Созданиие модели с тремя скрытыми слоями
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model2 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(targets, activation=tf.nn.tanh)
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])
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model2.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model2.summary()
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# Добавление Dropout в модель с тремя скрытыми слоями
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model2do = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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keras.layers.Dropout(0.3),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dropout(0.3),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dropout(0.3),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(targets, activation=tf.nn.tanh)
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])
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model2do.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model2do.summary()
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# Добавление пакетной нормазизации для исходных данных и скрытых слоёв 2-й модели
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model2bn = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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keras.layers.BatchNormalization(),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.BatchNormalization(),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.BatchNormalization(),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(targets, activation=tf.nn.tanh)
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])
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model2bn.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model2bn.summary()
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# Добавление Dropout в модель с пакетной нормазизацией исходных данных
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# и 3-х скрытых слоёв
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model2bndo = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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keras.layers.BatchNormalization(),
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keras.layers.Dropout(0.3),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.BatchNormalization(),
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keras.layers.Dropout(0.3),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.BatchNormalization(),
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keras.layers.Dropout(0.3),
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keras.layers.Dense(40, activation=tf.nn.swish),
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keras.layers.Dense(targets, activation=tf.nn.tanh)
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])
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model2bndo.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model2bndo.summary()
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# Обучение первой модели на нормализованных данных
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history1 = model1.fit(train_data, train_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.1,
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shuffle=True)
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model1.save(os.path.join(path,'perceptron1.h5'))
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history1do = model1do.fit(train_data, train_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.1,
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shuffle=True)
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model1do.save(os.path.join(path,'perceptron1do.h5'))
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history1bn = model1bn.fit(train_nn_data, train_nn_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.1,
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shuffle=True)
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model1bn.save(os.path.join(path,'perceptron1bn.h5'))
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history1bndo = model1bndo.fit(train_nn_data, train_nn_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.1,
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shuffle=True)
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model1bndo.save(os.path.join(path,'perceptron1bndo.h5'))
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history2 = model2.fit(train_data, train_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.1,
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shuffle=True)
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model2.save(os.path.join(path,'perceptron2.h5'))
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history2do = model2do.fit(train_data, train_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.1,
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shuffle=True)
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model2do.save(os.path.join(path,'perceptron2do.h5'))
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history2bn = model2bn.fit(train_nn_data, train_nn_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.1,
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shuffle=True)
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model2bn.save(os.path.join(path,'perceptron2bn.h5'))
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history2bndo = model2bndo.fit(train_nn_data, train_nn_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.1,
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shuffle=True)
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model2bndo.save(os.path.join(path,'perceptron2bndo.h5'))
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# Отрисовка результатов обучениия моделей с 1-ним скрытым слоем
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plt.figure(figsize=[6,3.5])
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plt.plot(history1.history['loss'], label='Train Normalized inputs')
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plt.plot(history1.history['val_loss'], label='Validation Normalized inputs')
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plt.plot(history1do.history['loss'], label='Train Normalized inputs vs Dropout')
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plt.plot(history1do.history['val_loss'], label='Validation Normalized inputs vs Dropout')
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plt.plot(history1bn.history['loss'], label='Train Not Normalized inputs\nwith BatchNormalization')
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plt.plot(history1bn.history['val_loss'], label='Validation Not Normalized inputs\nwith BatchNormalization')
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plt.plot(history1bndo.history['loss'], label='Train Not Normalized inputs\nwith BatchNormalization and Dropout')
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plt.plot(history1bndo.history['val_loss'], label='Validation Not Normalized inputs\nwith BatchNormalization and Dropout')
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plt.ylabel('$MSE$ $Loss$',fontsize='small')
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plt.xlabel('$Epochs$',fontsize='small')
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plt.title('Dinamic of Models train\n1 hidden layer',fontsize='medium')
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plt.legend(loc='upper right',fontsize='small')
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plt.figure(figsize=[6,3.5])
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plt.plot(history1.history['accuracy'], label='Train Normalized inputs')
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plt.plot(history1.history['val_accuracy'], label='Validation Normalized inputs')
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plt.plot(history1do.history['accuracy'], label='Train Normalized inputs vs Dropout')
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plt.plot(history1do.history['val_accuracy'], label='Validation Normalized inputs vs Dropout')
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plt.plot(history1bn.history['accuracy'], label='Train Not Normalized inputs\nwith BatchNormalization')
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plt.plot(history1bn.history['val_accuracy'], label='Validation Not Normalized inputs\nwith BatchNormalization')
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plt.plot(history1bndo.history['accuracy'], label='Train Not Normalized inputs\nwith BatchNormalization and Dropout')
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plt.plot(history1bndo.history['val_accuracy'], label='Validation Not Normalized inputs\nwith BatchNormalization and Dropout')
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plt.ylabel('$Accuracy$',fontsize='small')
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plt.xlabel('$Epochs$',fontsize='small')
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plt.title('Dinamic of Models train\n1 hidden layer',fontsize='medium')
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plt.legend(loc='lower right',fontsize='small')
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# Отрисовка результатов обучениия моделей с 3-мя скрытыми слоями
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plt.figure(figsize=[6,3.5])
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plt.plot(history2.history['loss'], label='Train Normalized inputs')
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plt.plot(history2.history['val_loss'], label='Validation Normalized inputs')
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plt.plot(history2do.history['loss'], label='Train Normalized inputs vs Dropout')
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plt.plot(history2do.history['val_loss'], label='Validation Normalized inputs vs Dropout')
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plt.plot(history2bn.history['loss'], label='Train Not Normalized inputs\nwith BatchNormalization')
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plt.plot(history2bn.history['val_loss'], label='Validation Not Normalized inputs\nwith BatchNormalization')
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plt.plot(history2bndo.history['loss'], label='Train Not Normalized inputs\nwith BatchNormalization and Dropout')
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plt.plot(history2bndo.history['val_loss'], label='Validation Not Normalized inputs\nwith BatchNormalization and Dropout')
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plt.ylabel('$MSE$ $Loss$',fontsize='small')
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plt.xlabel('$Epochs$',fontsize='small')
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plt.title('Dinamic of Models train\n3 hidden layer',fontsize='medium')
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plt.legend(loc='upper right',fontsize='small')
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plt.figure(figsize=[6,3.5])
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plt.plot(history2.history['accuracy'], label='Train Normalized inputs')
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plt.plot(history2.history['val_accuracy'], label='Validation Normalized inputs')
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plt.plot(history2do.history['accuracy'], label='Train Normalized inputs vs Dropout')
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plt.plot(history2do.history['val_accuracy'], label='Validation Normalized inputs vs Dropout')
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plt.plot(history2bn.history['accuracy'], label='Train Not Normalized inputs\nwith BatchNormalization')
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plt.plot(history2bn.history['val_accuracy'], label='Validation Not Normalized inputs\nwith BatchNormalization')
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plt.plot(history2bndo.history['accuracy'], label='Train Not Normalized inputs\nwith BatchNormalization and Dropout')
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plt.plot(history2bndo.history['val_accuracy'], label='Validation Not Normalized inputs\nwith BatchNormalization and Dropout')
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plt.ylabel('$Accuracy$',fontsize='small')
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plt.xlabel('$Epochs$',fontsize='small')
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plt.title('Dinamic of Models train\n3 hidden layer',fontsize='medium')
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plt.legend(loc='lower right',fontsize='small')
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# Загрузка тестовой выборки
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test_filename = os.path.join(path,'test_data.csv')
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test = np.asarray( pd.read_table(test_filename,
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sep=',',
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header=None,
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skipinitialspace=True,
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encoding='utf-8',
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float_precision='high',
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dtype=np.float64,
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low_memory=False))
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# Разделение тестовой выборки на исходные данные и цели
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test_data=test[:,0:inputs]
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test_target=test[:,inputs:]
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test_filename = os.path.join(path,'test_data_not_norm.csv')
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test = np.asarray( pd.read_table(test_filename,
|
||
|
|
sep=',',
|
||
|
|
header=None,
|
||
|
|
skipinitialspace=True,
|
||
|
|
encoding='utf-8',
|
||
|
|
float_precision='high',
|
||
|
|
dtype=np.float64,
|
||
|
|
low_memory=False))
|
||
|
|
# Разделение тестовой выборки на исходные данные и цели
|
||
|
|
test_nn_data=test[:,0:inputs]
|
||
|
|
test_nn_target=test[:,inputs:]
|
||
|
|
|
||
|
|
del test
|
||
|
|
|
||
|
|
# Проверка результатов моделей на тестовой выборке
|
||
|
|
test_loss1, test_acc1 = model1.evaluate(test_data, test_target, verbose=2)
|
||
|
|
test_loss1do, test_acc1do = model1do.evaluate(test_data, test_target, verbose=2)
|
||
|
|
test_loss1bn, test_acc1bn = model1bn.evaluate(test_nn_data, test_nn_target, verbose=2)
|
||
|
|
test_loss1bndo, test_acc1bndo = model1bndo.evaluate(test_nn_data, test_nn_target, verbose=2)
|
||
|
|
test_loss2, test_acc2 = model2.evaluate(test_data, test_target, verbose=2)
|
||
|
|
test_loss2do, test_acc2do = model2do.evaluate(test_data, test_target, verbose=2)
|
||
|
|
test_loss2bn, test_acc2bn = model2bn.evaluate(test_nn_data, test_nn_target, verbose=2)
|
||
|
|
test_loss2bndo, test_acc2bndo = model2bndo.evaluate(test_nn_data, test_nn_target, verbose=2)
|
||
|
|
|
||
|
|
# Вывод результатов тестирования в журнал
|
||
|
|
print('Model 1 hidden layer')
|
||
|
|
print('Test accuracy:', test_acc1)
|
||
|
|
print('Test loss:', test_loss1)
|
||
|
|
|
||
|
|
print('Model 1 hidden layer vs Dropout')
|
||
|
|
print('Test accuracy:', test_acc1do)
|
||
|
|
print('Test loss:', test_loss1do)
|
||
|
|
|
||
|
|
print('Model 1 hidden layer with BatchNormalization')
|
||
|
|
print('Test accuracy:', test_acc1bn)
|
||
|
|
print('Test loss:', test_loss1bn)
|
||
|
|
|
||
|
|
print('Model 1 hidden layer with BatchNormalization and Dropout')
|
||
|
|
print('Test accuracy:', test_acc1bndo)
|
||
|
|
print('Test loss:', test_loss1bndo)
|
||
|
|
|
||
|
|
print('Model 3 hidden layers')
|
||
|
|
print('Test accuracy:', test_acc2)
|
||
|
|
print('Test loss:', test_loss2)
|
||
|
|
|
||
|
|
print('Model 3 hidden layers vs Dropout')
|
||
|
|
print('Test accuracy:', test_acc2do)
|
||
|
|
print('Test loss:', test_loss2do)
|
||
|
|
|
||
|
|
print('Model 3 hidden layer with BatchNormalization')
|
||
|
|
print('Test accuracy:', test_acc2bn)
|
||
|
|
print('Test loss:', test_loss2bn)
|
||
|
|
|
||
|
|
print('Model 3 hidden layer with BatchNormalization and Dropout')
|
||
|
|
print('Test accuracy:', test_acc2bndo)
|
||
|
|
print('Test loss:', test_loss2bndo)
|
||
|
|
|
||
|
|
plt.figure(figsize=[6,3.5])
|
||
|
|
plt.bar(['Normalized inputs','\n\nNormalized inputs\nvs Dropout',
|
||
|
|
'Not Normalized inputs\nvs BatchNornalization',
|
||
|
|
'\n\nNot Normalized inputs\nvs BatchNornalization and Dropout'],
|
||
|
|
[test_loss1,test_loss1do,
|
||
|
|
test_loss1bn,test_loss1bndo])
|
||
|
|
plt.ylabel('$MSE$ $Loss$')
|
||
|
|
plt.title('Result of test\n1 hidden layer')
|
||
|
|
plt.figure(figsize=[6,3.5])
|
||
|
|
plt.bar(['Normalized inputs','\n\nNormalized inputs\nvs Dropout',
|
||
|
|
'Not Normalized inputs\nvs BatchNornalization',
|
||
|
|
'\n\nNot Normalized inputs\nvs BatchNornalization and Dropout'],
|
||
|
|
[test_loss2,test_loss2do,
|
||
|
|
test_loss2bn,test_loss2bndo])
|
||
|
|
plt.ylabel('$MSE$ $Loss$')
|
||
|
|
plt.title('Result of test\n3 hidden layers')
|
||
|
|
plt.figure(figsize=[6,3.5])
|
||
|
|
plt.bar(['Normalized inputs','\n\nNormalized inputs\nvs Dropout',
|
||
|
|
'Not Normalized inputs\nvs BatchNornalization',
|
||
|
|
'\n\nNot Normalized inputs\nvs BatchNornalization and Dropout'],
|
||
|
|
[test_acc1,test_acc1do,
|
||
|
|
test_acc1bn,test_acc1bndo])
|
||
|
|
plt.ylabel('$Accuracy$')
|
||
|
|
plt.title('Result of test\n1 hidden layer')
|
||
|
|
|
||
|
|
plt.figure(figsize=[6,3.5])
|
||
|
|
plt.bar(['Normalized inputs','\n\nNormalized inputs\nvs Dropout',
|
||
|
|
'Not Normalized inputs\nvs BatchNornalization',
|
||
|
|
'\n\nNot Normalized inputs\nvs BatchNornalization and Dropout'],
|
||
|
|
[test_acc2,test_acc2do,
|
||
|
|
test_acc2bn,test_acc2bndo])
|
||
|
|
plt.ylabel('$Accuracy$')
|
||
|
|
plt.title('Result of test\n3 hidden layers')
|
||
|
|
|
||
|
|
plt.show()
|