forked from rosh/NeuroBook
223 lines
10 KiB
Python
223 lines
10 KiB
Python
 | # -------------------------------------------------------#
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# Script for the creation and comparative testing of #
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# a fully connected perceptron model with different #
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# convolutional models using the same dataset. #
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# The script creates three models: #
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# - fully connected perceptron with three hidden layers #
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# & regularization. #
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# - 1-dimensional convolutional layer #
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# - 2-dimensional convolutional layer #
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# When training models, from training dataset, script #
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# allocates 1% 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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# Load training dataset
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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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path=os.path.join(mt5.terminal_info().data_path,r'MQL5\Files')
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mt5.shutdown()
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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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callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=10)
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# Creating a perceptron model with three hidden layers and regularization
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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, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(targerts, activation=tf.nn.tanh)
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])
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model1.summary()
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#keras.utils.plot_model(model1, show_shapes=True)
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# Add a 1D convolutional layer to the model
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model2 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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# Reformat tensor to a 3-dimensional one. Specify 2 dimensions as 3rd one is defined by batch size
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keras.layers.Reshape((-1,4)),
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# Convolutional later with 8 filters
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keras.layers.Conv1D(8,1,1,activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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# Pooling layer
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keras.layers.MaxPooling1D(2,strides=1),
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# Reformat tensor to a 2-dimensional one for fully connected layers
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keras.layers.Flatten(),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(targerts, activation=tf.nn.tanh)
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])
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model2.summary()
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#keras.utils.plot_model(model2, show_shapes=True)
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# Replace the convolutional layer in the model with a 2-dimensional one
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model3 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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# Reformat tensor into 4-dimensional. Specify 3 dimensions as the 4th dimension is determined by the batch size
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keras.layers.Reshape((-1,4,1)),
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# Convolutional later with 8 filters
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keras.layers.Conv2D(8,(3,1),1,activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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# Pooling layer
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keras.layers.MaxPooling2D((2,1),strides=1),
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# Reformat tensor to a 2-dimensional one for fully connected layers
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keras.layers.Flatten(),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(targerts, activation=tf.nn.tanh)
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])
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model3.summary()
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#keras.utils.plot_model(model3, show_shapes=True)
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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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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=0.01,
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shuffle=True)
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model1.save(os.path.join(path,'convolution1.h5'))
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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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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=0.01,
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shuffle=True)
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model2.save(os.path.join(path,'convolution2.h5'))
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model3.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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history3 = model3.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=0.01,
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shuffle=True)
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model3.save(os.path.join(path,'convolution3.h5'))
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# Render model training results
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plt.figure()
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plt.plot(history1.history['loss'], label='Perceptron train')
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plt.plot(history1.history['val_loss'], label='Perceptron validation')
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plt.plot(history2.history['loss'], label='Conv1D train')
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plt.plot(history2.history['val_loss'], label='Conv1D validation')
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plt.plot(history3.history['loss'], label='Conv2D train')
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plt.plot(history3.history['val_loss'], label='Conv2D 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',ncol=3)
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plt.figure()
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plt.plot(history1.history['accuracy'], label='Perceptron train')
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plt.plot(history1.history['val_accuracy'], label='Perceptron validation')
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plt.plot(history2.history['accuracy'], label='Conv1D train')
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plt.plot(history2.history['val_accuracy'], label='Conv1D validation')
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plt.plot(history3.history['accuracy'], label='Conv2D train')
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plt.plot(history3.history['val_accuracy'], label='Conv2D 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',ncol=3)
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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_loss1, test_acc1 = model1.evaluate(test_data, test_target, verbose=2)
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test_loss2, test_acc2 = model2.evaluate(test_data, test_target, verbose=2)
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test_loss3, test_acc3 = model3.evaluate(test_data, test_target, verbose=2)
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# Log testing results
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print('Perceptron model')
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print('Test accuracy:', test_acc1)
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print('Test loss:', test_loss1)
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print('Conv1D model')
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print('Test accuracy:', test_acc2)
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print('Test loss:', test_loss2)
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print('Conv2D model')
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print('Test accuracy:', test_acc3)
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print('Test loss:', test_loss3)
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plt.figure()
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plt.bar(['Perceptron','Conv1D', 'Conv2D'],[test_loss1,test_loss2,test_loss3])
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plt.ylabel('$MSE$ $loss$')
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plt.title('Test results')
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plt.figure()
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plt.bar(['Perceptron','Conv1D', 'Conv2D'],[test_acc1,test_acc2,test_acc3])
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plt.ylabel('$Accuracy$')
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plt.title('Test results')
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plt.show()
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