def worker_GS(input_worker):
"""Explanations"""
#Global variables:
global numInput, numOutput, numHidden1, numHidden2
global dim_hidden2_output, dim_input_hidden1, dim_hidden1_hidden2
global env
global sigma
global VBN_dict
#Local:
seed = int(input_worker[0])
epsilon = input_worker[1]
p = input_worker[2]
env.seed(seed)
#np.random.seed(seed)
#Neural Networks:
NN = NeuralNetwork(numInput, numHidden1, numHidden2, numOutput, VBN_dict)
NN.use_VBN = False
NN.W1 = toeplitz(p[0][numInput:], p[0][:numInput])
NN.W2 = toeplitz(p[1][numHidden1:], p[1][:numHidden1])
NN.W3 = toeplitz(p[2][numHidden2:], p[2][:numHidden2])
#distortions
epsilon_W1 = epsilon[0:numHidden1 + numInput]
epsilon_W2 = epsilon[numHidden1 + numInput:numHidden1 + numInput +
numHidden2 + numHidden1]
epsilon_W3 = epsilon[numHidden1 + numInput + numHidden2 +
numHidden1:numHidden1 + numInput + numHidden2 +
numHidden1 + numOutput + numHidden2]
#parameters update
NN.W1 = NN.W1 + sigma * toeplitz(epsilon_W1[numInput:],
epsilon_W1[:numInput])
NN.W2 = NN.W2 + sigma * toeplitz(epsilon_W2[numHidden1:],
epsilon_W2[:numHidden1])
NN.W3 = NN.W3 + sigma * toeplitz(epsilon_W3[numHidden2:],
epsilon_W3[:numHidden2])
initial_observation = env.reset()
reward_worker = episodeRoute(NN, env, initial_observation, steps=1000)
return (reward_worker, epsilon_W1, epsilon_W2, epsilon_W3)
def worker_GS(input_worker):
"""Explanations"""
#Global variables:
global numInput, numOutput, numHidden1, numHidden2
global dim_hidden2_output, dim_input_hidden1, dim_hidden1_hidden2
global env
global sigma
global VBN_dict
#Local:
seed = int(input_worker[0])
epsilon = input_worker[1]
p = input_worker[2]
env.seed(seed)
#np.random.seed(seed)
#Neural Networks:
NN = NeuralNetwork(numInput, numHidden1, numHidden2, numOutput, VBN_dict)
NN.use_VBN = False
NN.W1 = p[0]
NN.W2 = p[1]
NN.W3 = p[2]
#distortions
epsilon_W3 = epsilon[0:dim_hidden2_output].reshape((numOutput, numHidden2))
epsilon_W2 = epsilon[dim_hidden2_output:dim_hidden2_output +
dim_hidden1_hidden2].reshape((numHidden2, numHidden1))
epsilon_W1 = epsilon[dim_hidden2_output +
dim_hidden1_hidden2:dim_hidden2_output +
dim_hidden1_hidden2 + dim_input_hidden1].reshape(
(numHidden1, numInput))
#parameters update
NN.W1 = NN.W1 + epsilon_W1 * sigma
NN.W2 = NN.W2 + epsilon_W2 * sigma
NN.W3 = NN.W3 + epsilon_W3 * sigma
initial_observation = env.reset()
reward_worker = episodeRoute(NN, env, initial_observation, steps=250)
return (reward_worker, epsilon_W1, epsilon_W2, epsilon_W3)
def worker(input_worker):
"""Explanations"""
#Global variables:
global numInput, numOutput, numHidden1, numHidden2
global dim_hidden2_output, dim_input_hidden1, dim_hidden1_hidden2
global sigma
global env
#Local:
seed = int(input_worker[0])
p = input_worker[1]
env.seed(seed)
np.random.seed(seed)
#Neural Networks:
NN = NeuralNetwork(numInput, numHidden1, numHidden2, numOutput, VBN_dict)
NN.W1 = toeplitz(p[0][numInput:], p[0][:numInput])
NN.W2 = toeplitz(p[1][numHidden1:], p[1][:numHidden1])
NN.W3 = toeplitz(p[2][numHidden2:], p[2][:numHidden2])
#distortions
epsilon = np.random.multivariate_normal(
np.zeros(dim_hidden2_output + dim_input_hidden1 + dim_hidden1_hidden2),
np.identity(dim_hidden2_output + dim_input_hidden1 +
dim_hidden1_hidden2))
epsilon_W3 = epsilon[0:dim_hidden2_output].reshape((numOutput, numHidden2))
epsilon_W2 = epsilon[dim_hidden2_output:dim_hidden2_output +
dim_hidden1_hidden2].reshape((numHidden2, numHidden1))
epsilon_W1 = epsilon[dim_hidden2_output +
dim_hidden1_hidden2:dim_hidden2_output +
dim_hidden1_hidden2 + dim_input_hidden1].reshape(
(numHidden1, numInput))
#parameters update
NN.W1 = NN.W1 + epsilon_W1 * sigma
NN.W2 = NN.W2 + epsilon_W2 * sigma
NN.W3 = NN.W3 + epsilon_W3 * sigma
#initial_observation=env.reset()
reward_worker = episodeRoute(NN, env, initial_observation, steps=250)
return (reward_worker, epsilon_W1, epsilon_W2, epsilon_W3)
def worker_train_VBN(input_worker_VBN):
"""Explanations"""
#Global variables:
global numInput, numOutput, numHidden1, numHidden2
global dim_hidden2_output, dim_input_hidden1, dim_hidden1_hidden2
global env
#Local:
seed = int(input_worker_VBN[0])
p = input_worker_VBN[1]
env.seed(seed)
#np.random.seed(seed)
VBN_dict = {}
#VBN_dict['mu_i']=np.zeros((numInput,1))
#VBN_dict['var_i']=np.ones((numInput,1))
VBN_dict['mu_h1'] = np.zeros((numHidden1, 1))
VBN_dict['var_h1'] = np.ones((numHidden1, 1))
VBN_dict['mu_h2'] = np.zeros((numHidden2, 1))
VBN_dict['var_h2'] = np.zeros((numHidden2, 1))
#Neural Networks:
NN = NeuralNetwork(numInput, numHidden1, numHidden2, numOutput, VBN_dict)
NN.W1 = toeplitz(p[0][numInput:], p[0][:numInput])
NN.W2 = toeplitz(p[1][numHidden1:], p[1][:numHidden1])
NN.W3 = toeplitz(p[2][numHidden2:], p[2][:numHidden2])
#SHOULD IT BE PLACED IN THE LOOP ? CANT THINK RIGHT NOW
sum_zh1 = [0.] * numHidden1
sum_zh2 = [0.] * numHidden2
#sum_zi=[0.] * numInput
sum_zh1_sq = [0.] * numHidden1
sum_zh2_sq = [0.] * numHidden2
#sum_zi_sq=[0.] * numInput
steps = 1000
Ai = env.reset()
num_step = steps
NN.use_VBN = False #we don't want to use feedforward options with VBN to compute the statistics
for j in range(steps):
Ao = NN.feedForward(Ai)
sum_zh1 = [sum(x) for x in zip(sum_zh1, NN.Z1)]
sum_zh2 = [sum(x) for x in zip(sum_zh2, NN.Z2)]
#sum_zi=[sum(x) for x in zip(sum_zi, NN.Ai)]
sum_zh1_sq = [sum(x) for x in zip(sum_zh1_sq, square(NN.Z1))]
sum_zh2_sq = [sum(x) for x in zip(sum_zh2_sq, square(NN.Z2))]
#sum_zi_sq=[sum(x) for x in zip(sum_zi_sq, square(NN.Ai))]
action = np.argmax(Ao)
Ai, reward, done, info = env.step(action)
if done:
break
num_step = j
#return(sum_zi,sum_zh1,sum_zh2,sum_zi_sq,sum_zh1_sq,sum_zh2_sq,num_step)
return (sum_zh1, sum_zh2, sum_zh1_sq, sum_zh2_sq, num_step)
def worker_GS(input_worker):
"""Explanations"""
#Global variables:
global numInput, numOutput, numHidden1, numHidden2
global dim_hidden2_output, dim_input_hidden1, dim_hidden1_hidden2
global env
global sigma
global VBN_dict
#Local:
seed = int(input_worker[0])
epsilon = input_worker[1]
p = input_worker[2]
env.seed(seed)
#np.random.seed(seed)
#Neural Networks:
NN = NeuralNetwork(numInput, numHidden1, numHidden2, numOutput, VBN_dict)
NN.W1 = toeplitz(p[0][numInput:], p[0][:numInput])
NN.W2 = toeplitz(p[1][numHidden1:], p[1][:numHidden1])
NN.W3 = toeplitz(p[2][numHidden2:], p[2][:numHidden2])
NN.use_VBN = True
NN.gamma1 = p[3]
NN.beta1 = p[4]
NN.gamma2 = p[5]
NN.beta2 = p[6]
#distortions
epsilon_W1 = epsilon[0:numHidden1 + numInput]
epsilon_W2 = epsilon[numHidden1 + numInput:numHidden1 + numInput +
numHidden2 + numHidden1]
epsilon_W3 = epsilon[numHidden1 + numInput + numHidden2 +
numHidden1:numHidden1 + numInput + numHidden2 +
numHidden1 + numOutput + numHidden2]
dim_temp = numHidden1 + numInput + numHidden2 + numHidden1 + numOutput + numHidden2
epsilon_beta1 = epsilon[dim_temp:dim_temp + numHidden1].reshape(
(numHidden1, 1))
epsilon_gamma1 = epsilon[dim_temp + numHidden1:dim_temp +
2 * numHidden1].reshape((numHidden1, 1))
epsilon_beta2 = epsilon[dim_temp + 2 * numHidden1:dim_temp +
2 * numHidden1 + numHidden2].reshape(
(numHidden2, 1))
epsilon_gamma2 = epsilon[dim_temp + 2 * numHidden1 + numHidden2:dim_temp +
2 * numHidden1 + 2 * numHidden2].reshape(
(numHidden2, 1))
#parameters update
NN.W1 = NN.W1 + sigma * toeplitz(epsilon_W1[numInput:],
epsilon_W1[:numInput])
NN.W2 = NN.W2 + sigma * toeplitz(epsilon_W2[numHidden1:],
epsilon_W2[:numHidden1])
NN.W3 = NN.W3 + sigma * toeplitz(epsilon_W3[numHidden2:],
epsilon_W3[:numHidden2])
NN.beta1 = NN.beta1 + epsilon_beta1 * sigma
NN.gamma1 = NN.gamma1 + epsilon_gamma1 * sigma
NN.beta2 = NN.beta2 + epsilon_beta2 * sigma
NN.gamma2 = NN.gamma2 + epsilon_gamma2 * sigma
initial_observation = env.reset()
reward_worker = episodeRoute(NN, env, initial_observation, steps=1000)
return (reward_worker, epsilon_W1, epsilon_W2, epsilon_W3, epsilon_gamma1,
epsilon_beta1, epsilon_gamma2, epsilon_beta2)