File size: 3,724 Bytes
d4c7aae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | import network as nw
import importlib
import numpy as np
importlib.reload(nw)
class MaterialNetwork(nw.Network):
def __init__(self):
super().__init__()
def fit(self, cost_train, y_train, epochs, learning_rate, amount_train):
result = []
samples = len(cost_train)
# Train epoch times
for i in range(epochs):
err = 0
# train for all samples
for j in range(samples):
cost_input = cost_train[j]
amount_input = amount_train[j]
output = None
# predict data at all layer
for layer in self.layers:
output = layer.forward_propagation(cost_input, amount_input)
# output as input of next layer
cost_input = output
# Find loss for display
err += self.loss(y_train[j], output)
# Find Error of output dE/dY using derivation of MSE
error = self.loss_prime(y_train[j], output)
# Update Weight for next data
# By find gradient of weight in each layer and update
weight = []
for layer in reversed(self.layers):
error = layer.backward_propagation(error, learning_rate)
weight.append(layer.get_weight())
# Find Average Error per sample
err /= samples
print("Epoch %d/%d calculate with error = %f" % (i + 1, epochs, err))
result.append({"epoch": i + 1, "error": err})
self.weight_list = np.append(self.weight_list, [weight])
return result
def fit_on_sample(self, cost_input, y_train, learning_rate, amount_input):
err = 0
output = None
# predict data at all layer
for layer in self.layers:
output = layer.forward_propagation(cost_input, amount_input)
# output as input of next layer
cost_input = output
# Find loss for display
err += self.loss(y_train, output)
# Find Error of output dE/dY using derivation of MSE
error = self.loss_prime(y_train, output)
# Update Weight for next data
# By find gradient of weight in each layer and update
weight = []
for layer in reversed(self.layers):
error = layer.backward_propagation(error, learning_rate)
weight.append(layer.get_weight())
# print(f'Epoch calculate with error = {err}')
self.weight_list = np.append(self.weight_list, [weight])
return (output, err)
def get_weights(self):
result = []
for layer in self.layers:
result.append(layer.get_weight())
return result
def predict_sample(self, cost_input, amount_input):
output = None
# predict data at all layer
for layer in self.layers:
output = layer.forward_propagation(cost_input, amount_input)
# output as input of next layer
first_input = output
weight = layer.get_weight()
self.weight_list = np.append(self.weight_list, [weight])
return output
def predict(self, cost_input, amount_input):
output = None
# predict data at all layer
for layer in self.layers:
output = layer.predict(cost_input, amount_input)
# output as input of next layer
first_input = output
return output
def check_type(self, input_name):
if input_name == "material":
return True
print(f"You go to wrong class this is Material not {input_name}")
return False
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