import tensorflow as tf
import tensormodel as nn
# XOR with Relu (delta)
x_data = [[0, 0], [0, 1], [1, 0], [1, 1]]
y_data = [[0], [1], [1], [0]]
X = nn.DefPlaceHolder(2, 'Input')
Y = nn.DefPlaceHolder(1, 'Target')
hidden = nn.NnFullConnectedLayer(X, 2, 2, nn.relu_tf, bias='Variable')
output = nn.NnFullConnectedLayer(hidden.activation,
2,
1,
nn.relu_tf,
bias='Variable')
learn = nn.GradientDescentOptimizer(Y, output)
run = nn.RunLearnModel("XOR with Relu")
run.Learn(learn, feed_dict={X: x_data, Y: y_data})
run.Test_1(Y, output, feed_dict={X: x_data, Y: y_data})
import tensorflow as tf
import numpy as np
import tensormodel as nn
# implement AND NN machine using tensorflow
# define input data
x_data = [[0, 0], [0, 1], [1, 0], [1, 1]]
# define output data
y_data = [[0], [0], [0], [1]]
X = tf.placeholder(tf.float32, [None, 2], name="X")
Y = tf.placeholder(tf.float32, [None, 1], name="Y")
output = nn.NnFullConnectedLayer(X, 2, 1, nn.sigmoid_tf, bias='Variable')
learn = nn.GradientDescentOptimizer(Y, output)
run = nn.RunLearnModel("AND")
run.Learn(learn, feed_dict={X: x_data, Y: y_data})
run.Test_1(Y, output, feed_dict={X: x_data, Y: y_data})
import tensorflow as tf
import tensormodel as nn
# implement XOR NN machine with delta rule
## NN sometimes can not find answer.
## When changes of error is not converge, NN is not learned well.
x_data = [[0, 0], [0, 1], [1, 0], [1, 1]]
y_data = [[0], [1], [1], [0]]
X = nn.DefPlaceHolder(2, 'Input')
Y = nn.DefPlaceHolder(1, 'Target')
hidden = nn.NnFullConnectedLayer(X, 2, 2, nn.sigmoid_tf, bias='Variable')
output = nn.NnFullConnectedLayer(hidden.activation,
2,
1,
nn.sigmoid_tf,
bias='Variable')
learn = nn.GradientDescentOptimizer(Y, output)
run = nn.RunLearnModel("XOR")
run.Learn(learn, feed_dict={X: x_data, Y: y_data})
run.Test_1(Y, output, feed_dict={X: x_data, Y: y_data})
import tensorflow as tf
import numpy as np
import tensormodel as nn
# XOR 논리 게이트 구현.
## Relu 함수 + softmax entropy
## Relu 함수의 오차는 빠르게 줄기 때문에, 오차만 놓고 볼 때 일정 회수 (100) 이상의 반복 학습은 의미가 없다.
## OR, AND 구현에서도 확인할 수 있지만,
## Relu 함수를 적용한 논리 게이트의 학습 결과는 종종 오류가 발생하기 때문에
## 논리 게이트의 학습에 잘 맞지 않는다.
x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y_data = np.array([[1, 0], [0, 1], [0, 1], [1, 0]])
X = nn.DefPlaceHolder(2, 'Input')
Y = nn.DefPlaceHolder(2, 'Target')
hidden = nn.NnFullConnectedLayer(X, 2, 2, nn.relu_tf, bias='Variable')
output = nn.NnFullConnectedLayer(hidden.activation,
2,
2,
nn.relu_tf,
bias='Variable')
learn = nn.SoftmaxCrossEntropy(Y, output)
run = nn.RunLearnModel("XOR with Relu (softmax)")
run.Learn(learn, feed_dict={X: x_data, Y: y_data})
run.Test_2(Y, output, feed_dict={X: x_data, Y: y_data})