#Normalise CV data from 0..255 to 0..1
X_CV = DATA_CV[:, 1:].T / 255.0
#Empty list to collect CV errors
CV_error = []
#Let's iterate!
for i in xrange(iterations):
#Get miniBatch of defined size from whole DATA
X = getBatch(DATA, batchSize, validDataIndexes)
#Train on given data/labels
AE.trainCalc(X, X, iteration=1, debug=True, errorCollect=True)
#Check CV error every *checkCvEvery* cycles
if i % checkCvEvery == 0:
#Caclculate CV error give CV data/labels
CV_error.append(NNsupport.crossV(X_CV, X_CV, AE))
#Print current CV error
print 'CV error: ', CV_error[-1]
#Draw how error and accuracy evolves vs iterations
Graph.Builder(name='AE_error.png', error=AE.errorArray, cv=CV_error, legend_on=True)
#Visualise hidden layers weights
AE.weightsVisualizer(folder='.', size=(28, 28))
#Plot predicted decision boundary
plot(x, y_predicted, 'g.', markeredgewidth=0, label='Predicted boundary')
#Plot original decision boundary
plot(x, y, 'r.', markeredgewidth=0, label='Original boundary')
#Plot raw data
plot(data[0, :], data[1, :], 'b,', label='data')
#Draw legend
legend(loc='upper right', fontsize=10, numpoints=3, shadow=True, fancybox=True)
#Eanble grid
grid()
#Save plot to file
savefig('data' + str(i) + '.png', dpi=120)
#Close and clear current plot
close()
#Estimate Neural Network error (square error, "distance" between real and predicted value) on cross-validation
cv_err.append(NNsupport.crossV(CV_labels, CV, NN))
#Estimate network's accuracy
accuracy = np.mean(CV_labels == np.round(NN.out))
acc.append(accuracy)
#Draw how error and accuracy evolves vs iterations
Graph.Builder(name='NN_error.png', error=NN.errorArray, cv=cv_err, accuracy=acc, legend_on=True)
# Normalise CV data from 0..255 to 0..1
X_CV = DATA_CV[:, 1:].T / 255.0
# Empty list to collect CV errors
CV_error = []
# Let's iterate!
for i in xrange(iterations):
# Get miniBatch of defined size from whole DATA
X = getBatch(DATA, batchSize, validDataIndexes)
# Train on given data/labels
AE.trainCalc(X, X, iteration=1, debug=True, errorCollect=True)
# Check CV error every *checkCvEvery* cycles
if i % checkCvEvery == 0:
# Caclculate CV error give CV data/labels
CV_error.append(NNsupport.crossV(X_CV, X_CV, AE))
# Print current CV error
print "CV error: ", CV_error[-1]
# Draw how error and accuracy evolves vs iterations
Graph.Builder(name="AE_error.png", error=AE.errorArray, cv=CV_error, legend_on=True)
# Visualise hidden layers weights
AE.weightsVisualizer(folder=".", size=(28, 28))