forked from rinkugohil30897/NeuroBook
131 lines
No EOL
9 KiB
Python
131 lines
No EOL
9 KiB
Python
# -------------------------------------------------------#
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# Template for creating and testing different models of #
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# neural networks using the same dataset. #
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# When training models, from training dataset, script #
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# allocates 10% to validate the outputs. #
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# After training, the script tests the performance #
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# of the model on a test dataset (separate data file) #
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# -------------------------------------------------------#
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# Import Libraries
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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 as mp
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import matplotlib.pyplot as plt
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import matplotlib.font_manager as fm
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import MetaTrader5 as mt5
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# Add fonts
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font_list=fm.findSystemFonts()
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for f in font_list:
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if(f.__contains__('ClearSans')):
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fm.fontManager.addfont(f)
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# Set parameters for output graphs
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mp.rcParams.update({'font.family':'serif',
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'font.serif':'Clear Sans',
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'axes.titlesize': 'x-large',
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'axes.labelsize':'medium',
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'xtick.labelsize':'small',
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'ytick.labelsize':'small',
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'legend.fontsize':'small',
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'figure.figsize':[6.0,4.0],
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'axes.titlecolor': '#707070',
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'axes.labelcolor': '#707070',
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'axes.edgecolor': '#707070',
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'xtick.labelcolor': '#707070',
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'ytick.labelcolor': '#707070',
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'xtick.color': '#707070',
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'ytick.color': '#707070',
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'text.color': '#707070',
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'lines.linewidth': 0.8,
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'axes.linewidth': 0.5
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})
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# connect to the MetaTrader 5 terminal
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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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# request the path to the sandbox
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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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# Load training dataset
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filename = os.path.join(path,'study_data.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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# Split training dataset to input data and target
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inputs=data.shape[1]-2
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targerts=2
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train_data=data[:,0:inputs]
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train_target=data[:,inputs:]
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# create a neural network model
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model = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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# Fill the model with a description of the neural layers
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])
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model.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5)
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history = model.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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# save the trained model
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model.save(os.path.join(path,'model.h5'))
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# plot model training results
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plt.plot(history.history['loss'], label='Train')
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plt.plot(history.history['val_loss'], label='Validation')
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plt.ylabel('$MSE$ $Loss$')
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plt.xlabel('$Epochs$')
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plt.title('Model training dynamics')
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plt.legend(loc='upper right')
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plt.figure()
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plt.plot(history.history['accuracy'], label='Train')
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plt.plot(history.history['val_accuracy'], label='Validation')
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plt.ylabel('$Accuracy$')
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plt.xlabel('$Epochs$')
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plt.title('Model training dynamics')
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plt.legend(loc='lower right')
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# Load testing dataset
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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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# Split test dataset to input data and target
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test_data=test[:,0:inputs]
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test_target=test[:,inputs:]
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# check model results on a test dataset
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test_loss, test_acc = model.evaluate(test_data, test_target)
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# Log testing results
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print('Model in test')
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print('Test accuracy:', test_acc)
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print('Test loss:', test_loss)
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# output of graphs
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plt.show() |