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mlp_test.py
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from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
from keras import optimizers
from keras import initializers
import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
import math
number_input = 64
number_classes = 10
hidden_layers = 1
index_layer = 0
neurons_hidden = 53
funct_activation = 'relu'
funct_activation_output = 'softmax'
initializer_kernel=initializers.random_uniform()
initializer_bias='ones'
learning_rate = 0.0055
loss_function = 'categorical_crossentropy'
net_metrics = ['accuracy']
epochs_number = 3
validation_split = 3
##################READ DATABASE - TRAIN#####################
train_read = genfromtxt('dataset/train.csv', delimiter=',')
tmp_train_data = np.array([])
tmp_train_label = np.array([])
for i in range(0, train_read.shape[0]):
last = train_read[i][-1]
tmp_train_data = np.append(tmp_train_data, train_read[i][:-1], axis=0)
tmp_train_label = np.append(tmp_train_label,last)
tmp_train_data = tmp_train_data.reshape(-1, 64)
tmp_train_label = tmp_train_label.reshape(-1,1)
#print(tmp_train_data.shape[0])
#print(tmp_train_label.shape[0])
split_train_validation_data = np.split(tmp_train_data, validation_split)
split_train_validation_label = np.split(tmp_train_label, validation_split)
train_data = np.concatenate((split_train_validation_data[0], split_train_validation_data[1]))
train_label = np.concatenate((split_train_validation_label[0], split_train_validation_label[1]))
validation_data = split_train_validation_data[2]
validation_label = split_train_validation_label[2]
#print(train_data)
#print(train_label)
#print(train_data.shape[0])
#print(train_label.shape[0])
#print(validation_data)
#print(validation_label)
#print(validation_data.shape[0])
#print(validation_label.shape[0])
train_label_one_hot = to_categorical(train_label)
validation_label_one_hot = to_categorical(validation_label)
##################READ DATABASE - TEST#####################
test_read = genfromtxt('dataset/test.csv', delimiter=',')
test_data = np.array([])
test_label = np.array([])
for i in range(0, test_read.shape[0]):
last = test_read[i][-1]
test_data = np.append(test_data, test_read[i][:-1], axis=0)
test_label = np.append(test_label,last)
test_data = test_data.reshape(-1, 64)
test_label = test_label.reshape(-1,1)
test_label_one_hot = to_categorical(test_label)
####################NORMALIZATION########################
train_min = np.amin(train_data)
train_max = np.amax(train_data)
'''
Normalization between a and b
x = (b - a)(x - min)/(max - min) + a
'''
train_data = (2*((train_data - train_min)/(train_max - train_min))) - 1
validation_data = (2*((validation_data - train_min)/(train_max - train_min))) - 1
test_data = (2*((test_data - train_min)/(train_max - train_min))) - 1
#####################CREATE MLP############################
mlp = Sequential()
#First Layer and Input
mlp.add(Dense(neurons_hidden,
kernel_initializer=initializer_kernel,
bias_initializer=initializer_bias,
activation=funct_activation,
input_dim=number_input))
#All other layers
for index_layer in range(1, hidden_layers):
mlp.add(Dense(neurons_hidden,
kernel_initializer=initializer_kernel,
bias_initializer=initializer_bias,
activation=funct_activation))
#Output Layer
mlp.add(Dense(number_classes,
kernel_initializer=initializer_kernel,
bias_initializer=initializer_bias,
activation=funct_activation_output))
net_optimizer = optimizers.RMSprop(lr=learning_rate)
mlp.compile(optimizer=net_optimizer, loss=loss_function, metrics=net_metrics)
history = mlp.fit(train_data, train_label_one_hot,epochs=epochs_number, verbose=1,validation_data=(validation_data, validation_label_one_hot))
#[test_loss, test_acc] = mlp.evaluate(test_data, test_label_one_hot)
#print("Evaluation result on Test Data : Loss = {}, accuracy = {}".format(test_loss, test_acc))
prediction = mlp.predict_classes(test_data, verbose=1)
prediction = prediction.reshape(-1,1)
#Confusion Matrix
confusion_matrix = [0] * number_classes
for i in range(number_classes):
confusion_matrix[i] = [0] * number_classes
for i in range(prediction.shape[0]):
test_index = int(test_label[i])
prediction_index = int(prediction[i])
confusion_matrix[prediction_index][test_index] += 1
for i in range(number_classes):
print(confusion_matrix[i])