def classifier(self, feature_vector, a, t):
'''Is this all I need? I think I know how to do this one at least'''
#a = np.log(np.linalg.det(self.var_class1))-np.log(np.linalg.det(self.var_class2))#-> Ideally want to do this but due to overflow using log difference
norm_const = 1e9 #Introducing a normalization constant to take care of overflow/underflow
b = np.matmul(
np.transpose(feature_vector - self.mean_class1),
np.linalg.inv(self.var_class1)
@ (feature_vector - self.mean_class1))
c = np.matmul(
np.transpose(feature_vector - self.mean_class2),
np.linalg.inv(self.var_class1)
@ (feature_vector - self.mean_class2))
# b = np.matmul(np.transpose(feature_vector-self.mean_class1),(feature_vector-self.mean_class1))
# c = np.matmul(np.transpose(feature_vector-self.mean_class2),(feature_vector-self.mean_class2))
#
print(act.sigmoid((a + b - c) / norm_const))
#print(a)
#print(type(a))
#print(c)
if act.sigmoid((a + b - c) / norm_const) <= t:
return 1, act.sigmoid((a + b - c) / norm_const)
else:
return 0, act.sigmoid((a + b - c) / norm_const)
def MOGBinClassifier(x, mu1, mu2, sigma1, sigma2, prior1, prior2, t=0.5):
'''
Parameters:
x: the input data is a numpy array of shape (featurelength,datapoints) model is evaluated on (1200,1000)
mu1,mu2: The mean of the classes which needs to be classified, shape->(1200,k=number of clusters)
sigma1,sigma2: the variance of the calsses which needs to be classified
prior1,prior2 are the priors of the MOG for classes to be classified
Return:
returns a lsit and a numoy array
'''
p1 = np.ndarray((x.shape[1], prior1.shape[1]))
#np.zeros((prior1.shape))
p2 = np.ndarray((x.shape[1], prior1.shape[1]))
print(prior1.shape)
print(prior1.shape[0])
for i in range(prior1.shape[1]):
p1[:, i] = np.nan_to_num(
(np.log(prior1[0, i] + realmin) +
g.logGaussPdf(x, mu1[:, i].reshape(mu1.shape[0], 1),
sigma1[:, :, i]))).reshape((p1.shape[0]))
for i in range(prior2.shape[1]):
p2[:, i] = np.nan_to_num(
(np.log(prior2[0, i] + realmin) +
g.logGaussPdf(x, mu2[:, i].reshape(mu2.shape[0], 1),
sigma2[:, :, i]))).reshape((p1.shape[0]))
#p1 -> number of data,number of gaussian
#p2 -> number of data, number of gaussian
p1 = np.nan_to_num(sc.logsumexp(p1, axis=1))
p2 = np.nan_to_num(sc.logsumexp(p2, axis=1))
pred = []
df1 = pd.DataFrame(p1)
df2 = pd.DataFrame(p2)
df1.to_csv('p1data.csv')
df2.to_csv('p2data.csv')
P1 = p1 / (p1 + p2)
P2 = p2 / (p1 + p2)
df1 = pd.DataFrame(P1)
df2 = pd.DataFrame(P2)
df1.to_csv('p1data.csv')
df2.to_csv('p2data.csv')
#normalizer = 1e12 #To normalize the log-likelihood
P1 = P1.reshape((p1.shape[0]))
P2 = P2.reshape((p2.shape[0]))
for i in range(P1.shape[0]):
if (P2[i] - P1[i]) > 0:
pred.append(1)
else:
pred.append(0)
sf = 100
return pred, act.sigmoid(sf * (P1 - P2))
def backprop(x, y, biases, weightsT, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and transposed weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_wT): tuple containing the gradient for all the biases
and weightsT. nabla_b and nabla_wT should be the same shape as
input biases and weightsT
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_wT = [np.zeros(wT.shape) for wT in weightsT]
### Implement here
# feedforward
# Here you need to store all the activations of all the units
# by feedforward pass
###
h_k = x
h_ks = [h_k]
a_ks = []
for b, wT in zip(biases, weightsT):
a_k = np.dot(wT, h_k) + b
a_ks.append(a_k)
h_k = sigmoid(a_k)
h_ks.append(h_k)
# compute the gradient of error respect to output
# activations[-1] is the list of activations of the output layer
#delta = (cost).df_wrt_a(h_ks[-1], y)
delta = (cost).df_wrt_a(h_ks[-1], y) * sigmoid_prime(a_ks[-1])
nabla_b[-1] = delta
nabla_wT[-1] = np.dot(delta, h_ks[-2].T)
### Implement here
# backward pass
# Here you need to implement the backward pass to compute the
# gradient for each weight and bias
###
for i in range(2, num_layers):
delta = np.dot(weightsT[-i + 1].T, delta) * sigmoid_prime(a_ks[-i])
nabla_b[-i] = delta
nabla_wT[-i] = np.dot(delta, h_ks[-i - 1].T)
# for i in range(num_layers-2,-1,-1):
# delta = np.multiply(delta,sigmoid_prime(a_ks[i]))
# nabla_b[i] = delta
# nabla_wT[i] = (np.dot(h_ks[i],delta.T)).T
# delta = np.dot(weightsT[i].T,delta)
return (nabla_b, nabla_wT)
def backprop(x, y, biases, weights, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_w): tuple containing the gradient for all the biases
and weights. nabla_b and nabla_w should be the same shape as
input biases and weights
"""
temp = (np.transpose(((np.transpose(weights[0])) * x))) + biases[0]
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_w = [np.zeros(w.shape) for w in weights]
### Implement here
# feedforward
# Here you need to store all the activations of all the units
# by feedforward pass
###
# Feed forward code. @TODO still need to figure out what to pass the cost delta function - Zach Johnston
h = []
h.append(x)
#print(h[0].shape)
a = []
for k in range(1, num_layers):
a.append(np.dot(weights[k - 1], h[k - 1]) + biases[k - 1])
h.append(sigmoid(a[k - 1]))
# compute the gradient of error respect to output
# activations[-1] is the list of activations of the output layer
#delta = (cost).delta(activations[-1], y)
delta = (cost).delta(h[-1], y)
### Implement here
# backward pass
# Here you need to implement the backward pass to compute the
# gradient for each weight and bias
###
#for layer in range((num_layers-1),0,-1): # Backpropagate the error
# error_prev = np.multiply(np.dot(np.transpose(weights[layer-1]),error_prev),sigmoid_prime(h[layer-1]))
# error.append(error_prev)
# nabla_b.append(error_prev)
nabla_b[-1] = delta
nabla_w[-1] = np.dot(delta, h[-2].transpose())
for layer in range(2, num_layers):
delta = np.dot(weights[-layer + 1].transpose(), delta) * sigmoid_prime(
a[-layer])
nabla_b[-layer] = delta
nabla_w[-layer] = np.dot(delta, h[-layer - 1].transpose())
return (nabla_b, nabla_w)
def backprop(x, y, biases, weights, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_w): tuple containing the gradient for all the biases
and weights. nabla_b and nabla_w should be the same shape as
input biases and weights
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_w = [np.zeros(w.shape) for w in weights]
### Implement here
# feedforward
# Here you need to store all the activations of all the units
# by feedforward pass
activations = [x] # List to store the activation values
zs = [] # List to store the matrix multiplications
val = x
for i in range(num_layers - 1):
val = np.dot(weights[i], val) + biases[i]
zs.append(val)
val = sigmoid(val)
activations.append(val)
###
# compute the gradient of error respect to output
# activations[-1] is the list of activations of the output layer
delta = (cost).delta(activations[-1], y)
### Implement here
# backward pass
# Here you need to implement the backward pass to compute the
# gradient for each weight and bias
# backward pass
nabla_b[-1] = delta
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
for l in range(2, num_layers):
z = zs[-l]
delta = np.dot(weights[-l + 1].transpose(), delta) * sigmoid_prime(z)
nabla_b[-l] = delta
nabla_w[-l] = np.dot(delta, activations[-l - 1].transpose())
return (nabla_b, nabla_w)
def backprop(x, y, biases, weights, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_w): tuple containing the gradient for all the biases
and weights. nabla_b and nabla_w should be the same shape as
input biases and weights
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_w = [np.zeros(w.shape) for w in weights]
### Implement here
# feedforward
# Here you need to store all the activations of all the units
# by feedforward pass
###
#
a =[np.zeros(b.shape) for b in biases]
h_s = [np.zeros(b.shape) for b in biases]
h0=[x]
activations=h0+h_s
for k in range(num_layers -1):
a[k]= biases[k] + np.dot(weights[k], activations[k])
activations[k+1] = sigmoid(a[k])
# compute the gradient of error respect to output
# activations[-1] is the list of activations of the output layer
delta = (cost).delta(activations[-1], y)
nabla_b[-1] = delta
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
### Implement here
# backward pass
# Here you need to implement the backward pass to compute the
# gradient for each weight and bias
###
for k in range(2, num_layers):
delta = np.dot(weights[-k +1].T, delta) *sigmoid_prime(a[-k])
nabla_b[-k] = delta
nabla_w[-k] = np.dot(delta, activations[-k-1].T)
return (nabla_b, nabla_w)
def backprop(x, y, biases, weightsT, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and transposed weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_wT): tuple containing the gradient for all the biases
and weightsT. nabla_b and nabla_wT should be the same shape as
input biases and weightsT
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_wT = [np.zeros(wT.shape) for wT in weightsT]
### Implement here
# feedforward
activations = [x]
a = [x]
for i in range(0, num_layers - 1):
a.append(biases[i] + np.dot(weightsT[i], activations[i]))
activations.append(sigmoid(a[-1]))
# Here you need to store all the activations of all the units
# by feedforward pass
###
# compute the gradient of error respect to output
# activations[-1] is the list of activations of the output layer
delta = (cost).df_wrt_a(activations[-1], y)
### Implement here
G = delta
for k in range(num_layers - 1, 0, -1):
nabla_b[k - 1] = G
nabla_wT[k - 1] = np.dot(G, np.transpose(activations[k - 1]))
G = np.dot(np.transpose(weightsT[k - 1]), G)
G = np.multiply(G, sigmoid_prime(a[k - 1]))
# backward pass
# Here you need to implement the backward pass to compute the
# gradient for each weight and bias
###
return (nabla_b, nabla_wT)
def backprop(x, y, biases, weightsT, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and transposed weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_wT): tuple containing the gradient for all the biases
and weightsT. nabla_b and nabla_wT should be the same shape as
input biases and weightsT
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_wT = [np.zeros(wT.shape) for wT in weightsT]
inp = []
inp.append(x)
# print(h[0].shape)
a = []
for k in range(1, num_layers):
a.append(np.dot(weightsT[k - 1], inp[k - 1]) + biases[k - 1])
inp.append(sigmoid(a[k - 1]))
### Implement here
# feedforward
# Here you need to store all the activations of all the units
# by feedforward pass
###
# compute the gradient of error respect to output
# activations[-1] is the list of activations of the output layer
delta = (cost).df_wrt_a(inp[-1], y)
### Implement here
# backward pass
# Here you need to implement the backward pass to compute the
# gradient for each weight and bias
###
nabla_b[-1] = delta
nabla_wT[-1] = np.dot(delta, inp[-2].transpose())
for layer in range(2, num_layers):
delta = np.dot(weightsT[1 - layer].transpose(), delta) * sigmoid_prime(
a[-layer])
nabla_b[-layer] = delta
nabla_wT[-layer] = np.dot(delta, inp[-1 - layer].transpose())
return (nabla_b, nabla_wT)
def test_sigmoid():
z = np.arange(-10, 10, 0.1)
y = act.sigmoid(z)
y_p = act.sigmoid_prime(z)
plt.figure()
plt.subplot(1, 2, 1)
plt.plot(z, y)
plt.title('sigmoid')
plt.subplot(1, 2, 2)
plt.plot(z, y_p)
plt.title('derivative sigmoid')
plt.show()
def backprop(x, y, biases, weights, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_w): tuple containing the gradient for all the biases
and weights. nabla_b and nabla_w should be the same shape as
input biases and weights
"""
# initial zero list for store gradient of biases and weights
nabla_b = []
nabla_w = []
### Implement here
# feedforward
# Here you need to store all the activations of all the units
# by feedforward pass
###
activations = [x] #store activations
h = [] #store the z vectors
for i in range(num_layers - 1):
ai = biases[i] + np.dot(weights[i], activations[i])
h.append(ai)
activations.append(sigmoid(ai))
# compute the gradient of error respect to output
# activations[-1] is the list of activations of the output layer
g = (cost).delta(activations[-1],
y) #delta for output layer for cross entropy cost
nabla_w = [np.dot(g, np.transpose(activations[-2]))]
nabla_b = [g]
### Implement here
# backward pass
# Here you need to implement the backward pass to compute the
# gradient for each weight and bias
###
for i in range(2, num_layers):
g = np.dot(weights[-i + 1].transpose(), g) * sigmoid_prime(h[-i])
nabla_b = [g] + nabla_b
nabla_w = [np.dot(g, np.transpose(activations[-i - 1]))] + nabla_w
return (nabla_b, nabla_w)
def FAbinClassifier(x, mu1, mu2, sigma1, sigma2, phi1, phi2, t=0.5):
'''
Parameters:
x: the input data is a numpy array of shape (featurelenth,number of datapoints) model is evaluated on (1200,1000)
mu1,mu2: The mean of classes which needs to be classified, shape ->(featurelength,)
sigma1,sigma2 : the variance of the calsses which needs to be classified
its a diagonal mat with shape ->(1200,) only diagonal elements are passed
phi1,phi2: factor matrix with shape (1200,3)
'''
pred = []
mu_Class1 = mu1.reshape(mu1.shape[0], 1)
mu_Class2 = mu2.reshape(mu2.shape[0], 1)
sigmaclass1 = np.matmul(phi1, np.transpose(phi1)) + np.diag(sigma1)
sigmaclass2 = np.matmul(phi2, np.transpose(phi2)) + np.diag(sigma2)
# x_minus_mu1 = x - mu_Class1.reshape((mu_Class1.shape[0],1))
# x_minus_mu2 = x - mu_Class2.reshape((mu_Class2.shape[0],1))
#
# (sign,logdet1) = np.linalg.slogdet(sigma)
# (sign,logdet2) = np.linalg.slogdet(simga)
# inv_Sigma1
p1 = g.logGaussPdf(x, mu_Class1, sigmaclass1)
p2 = g.logGaussPdf(x, mu_Class2, sigmaclass2)
# p1 = sc.softmax(p1)
# p2 = sc.softmax(p2)
#
# p1 = sc.multivariate_normal.pdf(x,mu_Class1.reshape((1200)),sigmaclass1)
# p2 = sc.multivariate_normal.pdf(x,mu_Class2.reshape((1200)),sigmaclass2)
P1 = p1 / (p1 + p2)
P2 = p2 / (p1 + p2)
dfp1 = pd.DataFrame(P1)
dfp2 = pd.DataFrame(P2)
dfp1.to_csv('P1_FA_.csv')
dfp2.to_csv('P2_FA_.csv')
P1 = P1.reshape(p1.shape[0])
P2 = P2.reshape(p2.shape[0])
for i in range(p1.shape[0]):
if P2[i] - P1[i] < 0:
pred.append(1)
else:
pred.append(0)
return pred, act.sigmoid(1000 * (P2 - P1))
def backprop(x, y, biases, weightsT, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and transposed weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_wT): tuple containing the gradient for all the biases
and weightsT. nabla_b and nabla_wT should be the same shape as
input biases and weightsT
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_w = [np.zeros(w.shape) for w in weightsT]
# feedforward
activation = x
activations = [x] # list to store all the activations, layer by layer
zs = [] # list to store all the z vectors, layer by layer
for b, w in zip(biases, weightsT):
z = np.dot(w, activation) + b
zs.append(z)
activation = sigmoid(z)
activations.append(activation)
# backward pass
delta = (cost).df_wrt_a(activations[-1], y) * sigmoid_prime(zs[-1])
nabla_b[-1] = delta
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
# Note that the variable l in the loop below is used a little
# differently to the notation in Chapter 2 of the book. Here,
# l = 1 means the last layer of neurons, l = 2 is the
# second-last layer, and so on. It's a renumbering of the
# scheme in the book, used here to take advantage of the fact
# that Python can use negative indices in lists.
for l in range(2, num_layers):
z = zs[-l]
sp = sigmoid_prime(z)
delta = np.dot(weightsT[-l + 1].transpose(), delta) * sp
nabla_b[-l] = delta
nabla_w[-l] = np.dot(delta, activations[-l - 1].transpose())
return (nabla_b, nabla_w)
def backprop(x, y, biases, weightsT, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and transposed weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_wT): tuple containing the gradient for all the biases
and weightsT. nabla_b and nabla_wT should be the same shape as
input biases and weightsT
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_wT = [np.zeros(wT.shape) for wT in weightsT]
activations = [[], []]
tempx = x
for supercount in range(len(biases)):
for counts in range(len(biases[supercount])):
activations[supercount].append(
sigmoid(
np.dot(weightsT[supercount][counts], tempx) +
biases[supercount][counts]))
tempx = activations[supercount]
delta = (cost).df_wrt_a(activations[1], y)
for i in range(len(biases) - 1, -1, -1):
activationsD = []
for j in range(len(activations[i])):
activationsD.append(sigmoid_prime(activations[i][j]))
delta = np.multiply(delta, activationsD)
nabla_b[i] = delta
if (i == 0):
nabla_wT[i] = np.dot(nabla_b[i], np.transpose(x))
else:
nabla_wT[i] = np.dot(nabla_b[i], np.transpose(activations[i - 1]))
delta = np.dot(np.transpose(weightsT[i]), delta)
return (nabla_b, nabla_wT)
def StudnetT():
trainface,testface,trainnonface,testnonface=load_data()
pca_face=PCA(30)
f_train_pca,array_trainf = data_pca(trainface)
f_test_pca,array_testf = data_pca(testface)
nf_train_pca,array_trainnf = data_pca(trainnonface)
nf_test_pca,array_testnf = data_pca(testnonface)
(mu_f,sig_f,nu_f)= Stud.fit_t(f_train_pca,0.01)
(mu_nf,sig_nf,nu_nf)= Stud.fit_t(nf_train_pca,0.01)
px_face_pf = T.StudTpdf(f_test_pca,mu_f,sig_f,nu_f)
px_nonface_pf = T.StudTpdf(nf_test_pca,mu_f,sig_f,nu_f)
px_face_pnf = T.StudTpdf(f_test_pca,mu_nf,sig_nf,nu_nf)
px_nonface_pnf = T.StudTpdf(nf_test_pca,mu_nf,sig_nf,nu_nf)
Prob_face = np.concatenate((px_face_pf,px_nonface_pf))
Prob_nonface = np.concatenate((px_face_pnf,px_nonface_pnf))
df = pd.concat([testface,testnonface],ignore_index = True)
print(Prob_face.shape)
pred = []
for i in range(Prob_face.shape[0]):
if Prob_face[i] > Prob_nonface[i]:
pred.append(1)
else:
pred.append(0)
Prob_faceact = Prob_face/(Prob_face+Prob_nonface)
Prob_nonfaceact = Prob_nonface/(Prob_face+Prob_nonface)
df['Predictions'] = pred
df['Probability'] = act.sigmoid(5*(Prob_nonfaceact-Prob_faceact))
df.to_csv('studentT.csv')
roc = ROC.ROC(1200)
roc.Classifier_Details(df)
roc.ROC_plotter(df,'StudentT')
print(mu_f.shape)
print(sig_nf.shape)
def backprop(x, y, biases, weightsT, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and transposed weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_wT): tuple containing the gradient for all the biases
and weightsT. nabla_b and nabla_wT should be the same shape as
input biases and weightsT
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_wT = [np.zeros(wT.shape) for wT in weightsT]
activations = []
activations.append(x)
for k in range(0, num_layers - 1):
activations.append(
sigmoid(np.dot(weightsT[k], activations[k]) + biases[k]))
delta = (cost).df_wrt_a(activations[-1], y)
for i in range(num_layers - 2, -1, -1):
if i == num_layers - 2:
nabla_b[i] = delta
nabla_wT[i] = np.dot(delta, np.transpose(activations[-2]))
else:
delta = np.dot(np.transpose(weightsT[i + 1]),
delta) * sigmoid_prime(
np.dot(weightsT[i], activations[i]) + biases[i])
nabla_b[i] = delta
nabla_wT[i] = np.dot(delta, np.transpose(activations[i]))
return (nabla_b, nabla_wT)
def backprop(x, y, biases, weights, cost, num_layers):
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_w = [np.zeros(w.shape) for w in weights]
activation = x
activations = [x]
before_activations = []
for bias, weight in zip(biases, weights):
before_activation = np.dot(weight, activation) + bias
before_activations.append(before_activation)
activation = sigmoid(before_activation)
activations.append(activation)
delta = (cost).delta(activations[-1], y)
for l in range(1, num_layers):
if l != 1:
delta = np.dot(weights[-l + 1].transpose(), delta) * sigmoid_prime(
before_activations[-l])
nabla_b[-l] = delta
nabla_w[-l] = np.dot(delta, activations[-l - 1].transpose())
return (nabla_b, nabla_w)
def feedforward(self, a):
"""Return the output of the network if ``a`` is input."""
for b, wT in zip(self.biases, self.weightsT):
a = sigmoid(np.dot(wT, a)+b)
return a
def backprop(x, y, biases, weightsT, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and transposed weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_wT): tuple containing the gradient for all the biases
and weightsT. nabla_b and nabla_wT should be the same shape as
input biases and weightsT
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_wT = [np.zeros(wT.shape) for wT in weightsT]
### Implement here
# feedforward
# Here you need to store all the activations of all the units
# by feedforward pass
pre_act = [np.zeros(b.shape) for b in biases] # h^k
pre_act.insert(0, np.nan)
activations = [np.zeros(b.shape) for b in biases] # a^k
activations.insert(0, x)
# pre_act[0] = np.matmul(weightsT[0], x) + biases[0] # first activation is from input layer: x = h^(k-1) -> a^k
# activations[0] = sigmoid(pre_act[0]) # hit activation layer with sigmoid function for each element h
for i in range(
0, num_layers - 1
): # use previous activations layer output as current layers inputs
# previous layers outputs to make new one h^(k-1) -> a^k
pre_act[i + 1] = np.matmul(weightsT[i], activations[i]) + biases[i]
# # hit activation layer with sigmoid function for each element: g(a^k) -> h^k
activations[i + 1] = sigmoid(pre_act[i + 1])
###
# compute the gradient of error respect to output
# activations[-1] is the list of activations of the output layer
delta = (cost).df_wrt_a(activations[-1], y)
### Implement here
# backward pass
# Here you need to implement the backward pass to compute the
# gradient for each weight and bias
# first output layer ######$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
G = delta
nabla_b[-1] = G
nabla_wT[-1] = np.transpose(np.matmul(activations[-2], np.transpose(G)))
G = np.matmul(np.transpose(weightsT[-1]), G)
# Restart Algorithm
for n in reversed(range(0, num_layers - 2)):
G = np.multiply(G, sigmoid_prime(pre_act[n + 1]))
nabla_b[n] = G
nabla_wT[n] = np.transpose(np.matmul(activations[n], np.transpose(G)))
G = np.matmul(np.transpose(weightsT[n]), G)
# $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
###
return (nabla_b, nabla_wT)
def backprop(x, y, biases, weights, cost, num_layers):
""" function of backpropagation
Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient of all biases and weights.
Args:
x, y: input image x and label y
biases, weights (list): list of biases and weights of entire network
cost (CrossEntropyCost): object of cost computation
num_layers (int): number of layers of the network
Returns:
(nabla_b, nabla_w): tuple containing the gradient for all the biases
and weights. nabla_b and nabla_w should be the same shape as
input biases and weights
"""
# initial zero list for store gradient of biases and weights
nabla_b = [np.zeros(b.shape) for b in biases]
nabla_w = [np.zeros(w.shape) for w in weights]
""""
print("x.shape",x.shape)
print("y",y.shape)
print("b",biases[1].shape)
print("l_w",len(weights))
print("w",weights[1].shape)
"""
### Implement here
# feedforward
# Here you need to store all the activations of all the units
# by feedforward pass
###
index = 0
activations = [np.zeros(b.shape) for b in biases]
input = x
for b, w in zip(biases, weights):
activations[index] = sigmoid(np.dot(w, x) + b)
x = activations[index]
index += 1
# compute the gradient of error respect to output
# activations[-1] is the list of activations of the output layer
delta = (cost).delta(activations[-1], y)
gradlayer = delta / (activations[-1] * (1 - activations[-1]))
### Implement here
# backward pass
# Here you need to implement the backward pass to compute the
# gradient for each weight and bias
###
for y in range(0, len(activations)):
gradactivation = np.multiply(
gradlayer,
sigmoid_prime(
np.log((activations[-1 - y]) / (1 - activations[-1 - y]))))
nabla_b[-1 - y] = gradactivation
if (y == (len(activations) - 1)):
activation = input
else:
activation = activations[-2 - y]
nabla_w[-1 - y] = np.dot(gradactivation, activation.transpose())
gradlayer = np.dot(weights[-1 - y].transpose(), gradactivation)
return (nabla_b, nabla_w)