def execute_backward_pass(self, d_next_layer):
"""
Calculates gradient of the cost with respect to the output of this layer and updates the weights.
Arguments:
- d_next_layer: gradient of the cost with respect to the output of the next layer.
Returns:
- the gradient of the cost with respect to the output of this layer.
"""
d_next_layer = relu_derivative(self.output)
doutput = np.zeros(self.inputs.shape)
dfilters = np.zeros(self.filters.shape)
filter_index = 0
for input_index in range(self.inputs.shape[0]):
for y in range(self.filter_size[1]):
for x in range(self.filter_size[0]):
doutput[input_index, y:y + self.filter_size[1],
x:x + self.filter_size[0]] += self.filters[
filter_index] * d_next_layer[filter_index, y,
x]
dfilters[filter_index, :, :] += self.output[
input_index, y:y + self.filter_size[1], x:x +
self.filter_size[0]] * d_next_layer[filter_index, y, x]
if filter_index < self.n_filters - 1:
filter_index += 1
else:
filter_index = 0
self.filters += self.learning_rate * dfilters
return doutput
def backpropagate(self, x, y):
gradients_b = [np.zeros(b.shape) for b in self.biases]
gradients_w = [np.zeros(w.shape) for w in self.weights]
#feedforward
a = x
a_list = [x]
z_list = []
for b, w in zip(self.biases[0:-1], self.weights[0:-1]):
z = np.dot(w, a) + b
a = utils.relu(z)
a_list.append(a)
z_list.append(z)
z = np.dot(self.weights[-1], a_list[-1]) + self.biases[-1]
a = utils.softmax(z)
a_list.append(a)
z_list.append(z)
# backward
# for softmax-cross-entropy layer: delta in last layer = result - ground truth
delta = a_list[-1] - y
# update b and w for the last layer L
gradients_b[-1] = delta
gradients_w[-1] = np.dot(delta, a_list[-2].transpose())
# update b and w for the rest of layers L-1, L-2, ...
for l in range(2, self.num_layers):
z = z_list[-l] # lth last layer of z
r_derivative = utils.relu_derivative(z)
# update delta based on delta(l) = transpose of w(l+1) * delta(l+1)
delta = np.dot(self.weights[-l + 1].transpose(),
delta) * r_derivative
gradients_b[-l] = delta
gradients_w[-l] = np.dot(delta, a_list[-l - 1].transpose())
return (gradients_b, gradients_w)
def back_step(self, xc, grad, state, h_prev, cell_prev):
# softmax
self.params.wk_diff = np.dot(state.h, grad.T)
# self.params.wk_diff = np.dot(grad,self.params.wr)
self.params.bk_diff = grad
# Relu
dr_before = util.relu_derivative(np.dot(self.params.wk, grad))
self.params.wr_diff += np.dot(
dr_before.T,
np.dot(state.h, self.params.wr) + self.params.br)
self.params.br_diff += dr_before
# h next state
state.diff_h_values = np.dot(dr_before, self.params.wr)
state.diff_h_values += state.h
# output
do = np.multiply(state.diff_h_values,
util.tanh_normal(state.cell_values))
db_o = np.multiply(
do, np.multiply(state.output_values, (1 - state.output_values)))
self.params.wo_diff += np.dot(db_o, xc.T)
self.params.bo_diff += db_o
# cell
dc = np.multiply(
state.diff_h_values,
np.multiply(state.output_values,
(1 - util.tanh_normal(state.cell_values)**2)))
dc += state.cell_values
dc_temp = np.multiply(dc, state.input_values)
db_c = np.multiply(dc_temp, (1 - state.cell_temp_values**2))
self.params.wg_diff += np.dot(db_c, xc.T)
self.params.bg_diff += db_c
# input
di = np.multiply(dc, state.cell_temp_values)
db_i = np.multiply(
di, np.multiply(state.input_values, (1 - state.input_values)))
self.params.wi_diff += np.dot(db_i, xc.T)
self.params.bi_diff += db_i
# forget
df = np.multiply(dc, cell_prev)
db_f = np.multiply(
df, np.multiply(state.forget_values, (1 - state.forget_values)))
self.params.wf_diff += np.dot(db_f, xc.T)
self.params.bf_diff += db_f
# xc
dxc = (np.dot(self.params.wf.T, db_f) +
np.dot(self.params.wi.T, db_i) +
np.dot(self.params.wg.T, db_c) + np.dot(self.params.wo.T, db_o))
def BackPropagationLearner(dataset,
net,
learning_rate,
epochs,
activation=sigmoid):
"""[Figure 18.23] The back-propagation algorithm for multilayer networks"""
# Initialise weights
for layer in net:
for node in layer:
node.weights = random_weights(min_value=-0.5,
max_value=0.5,
num_weights=len(node.weights))
examples = dataset.examples
'''
As of now dataset.target gives an int instead of list,
Changing dataset class will have effect on all the learners.
Will be taken care of later.
'''
o_nodes = net[-1]
i_nodes = net[0]
o_units = len(o_nodes)
idx_t = dataset.target
idx_i = dataset.inputs
n_layers = len(net)
inputs, targets = init_examples(examples, idx_i, idx_t, o_units)
for epoch in range(epochs):
# Iterate over each example
for e in range(len(examples)):
i_val = inputs[e]
t_val = targets[e]
# Activate input layer
for v, n in zip(i_val, i_nodes):
n.value = v
# Forward pass
for layer in net[1:]:
for node in layer:
inc = [n.value for n in node.inputs]
in_val = dotproduct(inc, node.weights)
node.value = node.activation(in_val)
# Initialize delta
delta = [[] for _ in range(n_layers)]
# Compute outer layer delta
# Error for the MSE cost function
err = [t_val[i] - o_nodes[i].value for i in range(o_units)]
# The activation function used is relu or sigmoid function
if node.activation == sigmoid:
delta[-1] = [
sigmoid_derivative(o_nodes[i].value) * err[i]
for i in range(o_units)
]
elif node.activation == relu:
delta[-1] = [
relu_derivative(o_nodes[i].value) * err[i]
for i in range(o_units)
]
elif node.activation == tanh:
delta[-1] = [
tanh_derivative(o_nodes[i].value) * err[i]
for i in range(o_units)
]
elif node.activation == elu:
delta[-1] = [
elu_derivative(o_nodes[i].value) * err[i]
for i in range(o_units)
]
else:
delta[-1] = [
leaky_relu_derivative(o_nodes[i].value) * err[i]
for i in range(o_units)
]
# Backward pass
h_layers = n_layers - 2
for i in range(h_layers, 0, -1):
layer = net[i]
h_units = len(layer)
nx_layer = net[i + 1]
# weights from each ith layer node to each i + 1th layer node
w = [[node.weights[k] for node in nx_layer]
for k in range(h_units)]
if activation == sigmoid:
delta[i] = [
sigmoid_derivative(layer[j].value) *
dotproduct(w[j], delta[i + 1]) for j in range(h_units)
]
elif activation == relu:
delta[i] = [
relu_derivative(layer[j].value) *
dotproduct(w[j], delta[i + 1]) for j in range(h_units)
]
elif activation == tanh:
delta[i] = [
tanh_derivative(layer[j].value) *
dotproduct(w[j], delta[i + 1]) for j in range(h_units)
]
elif activation == elu:
delta[i] = [
elu_derivative(layer[j].value) *
dotproduct(w[j], delta[i + 1]) for j in range(h_units)
]
else:
delta[i] = [
leaky_relu_derivative(layer[j].value) *
dotproduct(w[j], delta[i + 1]) for j in range(h_units)
]
# Update weights
for i in range(1, n_layers):
layer = net[i]
inc = [node.value for node in net[i - 1]]
units = len(layer)
for j in range(units):
layer[j].weights = vector_add(
layer[j].weights,
scalar_vector_product(learning_rate * delta[i][j],
inc))
return net
def BackPropagationLearner(dataset, net, learning_rate, epochs, activation=sigmoid, momentum=False, beta=0.903):
"""[Figure 18.23] The back-propagation algorithm for multilayer networks"""
# Initialise weights
for layer in net:
for node in layer:
node.weights = random_weights(min_value=-0.5, max_value=0.5,
num_weights=len(node.weights))
examples = dataset.examples
'''
As of now dataset.target gives an int instead of list,
Changing dataset class will have effect on all the learners.
Will be taken care of later.
'''
o_nodes = net[-1]
i_nodes = net[0]
o_units = len(o_nodes)
idx_t = dataset.target
idx_i = dataset.inputs
n_layers = len(net)
inputs, targets = init_examples(examples, idx_i, idx_t, o_units)
for epoch in range(epochs):
# Iterate over each example
for e in range(len(examples)):
i_val = inputs[e]
t_val = targets[e]
# Activate input layer
for v, n in zip(i_val, i_nodes):
n.value = v
# Finding the values of the nodes through forward propogation
for layer in net[1:]:
for node in layer:
inc = [n.value for n in node.inputs]
in_val = dotproduct(inc, node.weights)
node.value = node.activation(in_val)
# Initialize delta which stores the values of the gradients for each activation units
delta = [[] for _ in range(n_layers)]
#initializing the velocity_gradient
if momentum == True:
v_dw = [[0 for i in range(len(_))] for _ in net]
# Compute outer layer delta
# Error for the MSE cost function
err = [t_val[i] - o_nodes[i].value for i in range(o_units)]
# The activation function used is relu or sigmoid function
# First backward fast
if node.activation == sigmoid:
delta[-1] = [sigmoid_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]
elif node.activation == relu:
delta[-1] = [relu_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]
elif node.activation == tanh:
delta[-1] = [tanh_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]
elif node.activation == elu:
delta[-1] = [elu_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]
else:
delta[-1] = [leaky_relu_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]
# Propogating backward and finding gradients of nodes for each hidden layer
h_layers = n_layers - 2
for i in range(h_layers, 0, -1):
layer = net[i]
h_units = len(layer)
nx_layer = net[i+1]
# weights from each ith layer node to each i + 1th layer node
w = [[node.weights[k] for node in nx_layer] for k in range(h_units)]
if activation == sigmoid:
delta[i] = [sigmoid_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]
elif activation == relu:
delta[i] = [relu_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]
elif activation == tanh:
delta[i] = [tanh_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]
elif activation == elu:
delta[i] = [elu_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]
else:
delta[i] = [leaky_relu_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]
#optimization with velocity gradient
t_ = epoch + 1
if momentum == True:
if epoch == 0:
for i in range(len(delta)):
for j in range(len(delta[i])):
v_dw[i][j] = ((1-beta)*delta[i][j])/(1-beta**(t_+1))
else:
for i in range(len(delta)):
for j in range(len(delta[i])):
v_dw[i][j] = (beta*v_dw[i][j]+(1-beta)*delta[i][j])/(1-beta**(t_+1))
# Update weights with normal gradient descent
if momentum == False:
for i in range(1, n_layers):
layer = net[i]
inc = [node.value for node in net[i-1]]
units = len(layer)
for j in range(units):
layer[j].weights = vector_add(layer[j].weights,
scalar_vector_product(
learning_rate * delta[i][j],
inc
))
# Update weights with velocity gradient optimizer in gradient descent
else:
for i in range(1, n_layers):
layer = net[i]
inc = [node.value for node in net[i-1]]
units = len(layer)
for j in range(units):
layer[j].weights = vector_add(layer[j].weights,
scalar_vector_product(
learning_rate * v_dw[i][j],
inc
))
return net