def checkNNGradients(lambd):
input_layer_size = 3
hidden_layer_size = 5
num_labels = 3
m = 5
layers = [3, 5, 3]
# In this point we generate a number of random data
Theta = []
Theta.append(debugInitializeWeights(hidden_layer_size, input_layer_size))
Theta.append(debugInitializeWeights(num_labels, hidden_layer_size))
X = debugInitializeWeights(m, input_layer_size - 1)
y = remainder(arange(m) + 1, num_labels)
# Unroll parameters
nn_params = unroll_params(Theta)
# Compute Numerical Gradient
numgrad = computeNumericalGradient(nn_params, layers, X, y, num_labels,
lambd)
# Compute Analytical Gradient (BackPropagation)
truegrad = backwards(nn_params, layers, X, y, num_labels, lambd)
print concatenate(([numgrad], [truegrad]), axis=0).transpose()
print "The above two columns must be very similar.\n(Left-Numerical Gradient, Right-Analytical Gradient (BackPropagation)\n"
diff = linalg.norm(numgrad - truegrad) / linalg.norm(numgrad + truegrad)
print "\nNote: If the implementation of the backpropagation is correct, the relative different must be quite small (less that 1e-09)."
print "Relative difference: " + str(diff) + "\n"
def checkNNGradients(lambd):
input_layer_size = 3;
hidden_layer_size = 5;
num_labels = 3;
m = 5;
layers = [3, 5, 3]
# In this point we generate a number of random data
Theta = []
Theta.append(debugInitializeWeights(hidden_layer_size, input_layer_size))
Theta.append(debugInitializeWeights(num_labels, hidden_layer_size))
X = debugInitializeWeights(m, input_layer_size - 1)
y = remainder(arange(m)+1, num_labels)
# Unroll parameters
nn_params = unroll_params(Theta)
# Compute Numerical Gradient
numgrad = computeNumericalGradient(nn_params,layers, X, y, num_labels, lambd)
# Compute Analytical Gradient (BackPropagation)
truegrad = backwards(nn_params, layers, X, y, num_labels, lambd)
print concatenate(([numgrad], [truegrad]), axis = 0).transpose()
print "The above two columns must be very similar.\n(Left-Numerical Gradient, Right-Analytical Gradient (BackPropagation)\n"
diff = linalg.norm(numgrad - truegrad) / linalg.norm(numgrad + truegrad)
print "\nNote: If the implementation of the backpropagation is correct, the relative different must be quite small (less that 1e-09)."
print "Relative difference: " + str(diff) + "\n"
def checkNNCost(lambd):
input_layer_size = 3;
hidden_layer_size = 5;
num_labels = 3;
m = 5;
layers = [3, 5, 3]
Theta = []
Theta.append(debugInitializeWeights(hidden_layer_size, input_layer_size))
Theta.append(debugInitializeWeights(num_labels, hidden_layer_size))
nn_params = unroll_params(Theta)
X = debugInitializeWeights(m, input_layer_size - 1)
y = remainder(arange(m)+1, num_labels)
cost = costFunction(nn_params, layers, X, y, num_labels, lambd)
print 'Cost: ' + str(cost)
def checkNNCost(lambd):
input_layer_size = 3
hidden_layer_size = 5
num_labels = 3
m = 5
layers = [3, 5, 3]
Theta = []
Theta.append(debugInitializeWeights(hidden_layer_size, input_layer_size))
Theta.append(debugInitializeWeights(num_labels, hidden_layer_size))
nn_params = unroll_params(Theta)
X = debugInitializeWeights(m, input_layer_size - 1)
y = remainder(arange(m)+1, num_labels)
cost = costFunction(nn_params, layers, X, y, num_labels, lambd)
print 'Cost: ' + str(cost)
