Hugging Face's logo

iaecpsu-1
/
tdce-basic

Model card Files
xet
 Community
tdce-basic / model /employee_fc_layer.py
Tin Theethawat Savastham
♻️ Split Function for Implement and Model Directory
4ec8622
Raw History Blame Contribute Delete
4.45 kB
from layer import Layer
import numpy as np
from tsensor import explain as exp
import activation as act
import weight_activation as wa
import importlib
importlib.reload(act)
importlib.reload(wa)
class EmployeeFCLayer(Layer):
def __init__(self, input_size, output_size, hour_day):
self.weights = np.full(
(input_size, output_size), 1.0
) # np.random.rand(input_size, output_size) - 0.5
self.bias = np.full(
(1, output_size), 0.0
) # np.random.rand(1, output_size) - 0.5
print(f"weight shape {self.weights.shape}")
self.time_usage = None
self.hour_day = hour_day
self.cost = None
self.input = None
def annotate(self, cost_rate, time_usage, day_amount):
with exp() as c:
# fmt: off
output = 1/75 * 1/self.hour_day * time_usage *1/day_amount * cost_rate @ self.weights + self.bias
# fmt: on
# Predict the result during use
def predict(self, cost_data, time_usage, day_amount):
cost_time = np.multiply(cost_data, time_usage)
day_amount = np.divide(1, day_amount)
output = (1 / 75) * (1 / self.hour_day) * np.dot(
cost_time, self.weights
) + self.bias
# print(f"Weight For Daily Employee: {self.weights}")
self.input = output
return act.leaky_relu(output)
# Predict During Train
def forward_propagation(self, cost_data, time_usage, day_amount):
self.cost = cost_data
self.time_usage = time_usage
self.day_amount = day_amount
if (
np.all(cost_data == 0)
and np.all(time_usage == 0)
and np.all(day_amount == 0)
):
self.output = np.zeros((1, 1))
return self.output
cost_time = np.multiply(self.cost, self.time_usage)
cost_time = np.divide(cost_time, day_amount)
self.output = (
(1 / 75) * (1 / self.hour_day) * np.dot(cost_time, self.weights)
) # + self.bias
return act.leaky_relu(self.output)
def predict(self, cost_data, time_usage, day_amount):
cost_time = np.multiply(cost_data, time_usage)
cost_time = np.divide(cost_time, day_amount)
output = (
(1 / 75) * (1 / self.hour_day) * np.dot(cost_time, self.weights)
) # + self.bias
# print(f"Weight For Daily Employee: {self.weights}")
self.input = output
return act.leaky_relu(output)
# output error is dE/dY
def backward_propagation(self, output_error, learning_rate):
# print(f"DE: Weight {self.weights}")
input_error = np.dot(output_error, self.weights.T)
# print(f"DE: Weight.T {self.weights.T}")
# print(f"DE: Input Error {input_error}")
# print(f"DE: Output Error {output_error}")
# print(f"DE: Time Usage {self.time_usage}")
input = self.time_usage * (1 / 75) * (1 / self.hour_day)
# print(f"DE: input Before Multiply {input}")
# print(f"DE: Cost {self.cost} and Cost Transpose {self.cost.T}")
input = np.multiply(input, self.cost)
input = np.multiply(input, 1 / self.day_amount)
# print(f"DE: input After Multiply {gradient}")
# print(f"DE: Output Error {output_error}")
weight_error = np.dot(output_error, input)
activation_input = self.input
activation_prime = act.leaky_relu_prime(activation_input)
weight_error = np.multiply(weight_error, activation_prime)
row, col = self.weights.shape
# dE/dB = dE/dY
bias_error = output_error * activation_prime
# bias_error = output_error
# print(f"Daily Employee Acutal Input{activation_input}")
# print(f"Daily Employee Activation Prime {activation_prime}")
# print(f"Daily Employee Output Error {output_error}")
weight_error = weight_error.reshape(row, col)
# Update Parameter
# print(f"DE: Weight Error {weight_error}")
# print(f"DE: New Weight {self.weights}")
self.weights -= learning_rate * weight_error
self.weights = wa.un_zero_weight(self.weights)
# print('DE: New Bias', self.bias)
self.bias -= learning_rate * bias_error
# print("Update weight to ", self.weights)
return input_error # dE/dX
def get_weight(self):
return self.weights
def get_bias(self):
return self.bias