Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation (No. ICS-8506). CALIFORNIA UNIV SAN DIEGO LA JOLLA INST FOR COGNITIVE SCIENCE.
In [1]:
person_1_input = [[1.0 if target == person else 0.0 for target in range(24) ] for person in range(24)]
person_2_output = person_1_input[:] # Data copy - Person 1 is the same data as person 2.
relationship_input = [[1.0 if target == relationship else 0.0 for target in range(12) ] for relationship in range(12)]
In [2]:
# (colin has-father james)
# (colin has-mother victoria)
# (james has-wife victoria)
# (charlotte has-brother colin)
# (victoria has-brother arthur)
# (charlotte has-uncle arthur)
# 아래의 리스트는 가족관계도에 있는 관계를 위의 예시와 같은 방법으로 나타낸 것입니다.
# [input_person, relationship, output_person]
triple_relationship = [[0, 3, 1], [0, 4, 3], [0, 5, 4],
[1, 2, 0], [1, 4, 3], [1, 5, 4],
[2, 2, 3],
[3, 3, 2], [3, 0, 0], [3, 1, 1], [3, 9, 4], [3, 10, 10], [3, 11, 11],
[4, 2, 5], [4, 0, 0], [4, 1, 1], [4, 5, 3], [4, 4, 10], [4, 5, 11],
[5, 3, 4], [5, 0, 6], [5, 1, 7], [5, 9, 9], [5, 4, 10], [5, 5, 11],
[6, 3, 7], [6, 4, 5], [6, 5, 8],
[7, 2, 6], [7, 4, 5], [7, 5, 8],
[8, 2, 9], [8, 0, 6], [8, 1, 7], [8, 8, 5], [8, 10, 10], [8, 11, 11],
[9, 3, 8],
[10, 0, 5], [10, 1, 4], [10, 9, 11], [10, 6, 3], [10, 7, 8],
[11, 0, 5], [11, 1, 4], [11, 8, 10], [11, 6, 3], [11, 7, 8],
[12, 3, 13], [12, 4, 15], [12, 5, 16],
[13, 2, 12], [13, 4, 15], [13, 5, 16],
[14, 2, 15],
[15, 3, 14], [15, 0, 12], [15, 1, 13], [15, 9, 16], [15, 10, 22], [15, 11, 23],
[16, 2, 17], [16, 0, 12], [16, 1, 15], [16, 5, 15], [16, 4, 22], [16, 5, 23],
[17, 3, 16], [17, 0, 18], [17, 1, 19], [17, 9, 21], [17, 4, 22], [17, 5, 23],
[18, 3, 19], [18, 4, 17], [18, 5, 20],
[19, 2, 18], [19, 4, 17], [19, 5, 20],
[20, 2, 21], [20, 0, 18], [20, 1, 19], [20, 8, 17], [20, 10, 22], [8, 11, 23],
[21, 3, 20],
[22, 0, 17], [22, 1, 16], [22, 9, 23], [22, 6, 15], [22, 7, 20],
[23, 0, 17], [23, 1, 16], [23, 8, 22], [23, 6, 15], [23, 7, 20]]
In [3]:
import tensorflow as tf
import numpy as np
In [4]:
x1_data = np.array([person_1_input[data[0]] for data in triple_relationship],dtype=np.float32)
x2_data = np.array([relationship_input[data[1]] for data in triple_relationship],dtype=np.float32)
y_data = np.array([person_2_output[data[2]] for data in triple_relationship],dtype=np.float32)
In [5]:
X1 = tf.placeholder(tf.float32, [None, 24])
X2 = tf.placeholder(tf.float32, [None, 12])
Y = tf.placeholder(tf.float32, [None, 24])
In [81]:
# Weights and bias
W11 = tf.Variable(tf.zeros([24, 6]))
W12 = tf.Variable(tf.zeros([12, 6]))
W21 = tf.Variable(tf.zeros([6, 12]))
W22 = tf.Variable(tf.zeros([6, 12]))
W3 = tf.Variable(tf.zeros([12, 24]))
b11 = tf.Variable(tf.zeros([6]))
b12 = tf.Variable(tf.zeros([6]))
b2 = tf.Variable(tf.zeros([12]))
b3 = tf.Variable(tf.zeros([24]))
# Hypothesis
L11 = tf.sigmoid(tf.matmul(X1, W11) + b11) # 24 by 6 mat
L12 = tf.sigmoid(tf.matmul(X2, W12) + b12) # 12 by 6 mat
# L2 = tf.sigmoid(tf.matmul(L11, W21) + tf.matmul(L12, W22) + b2) # Dimensions must be equal, but are 24 and 12 for 'add_22' (op: 'Add') with input shapes: [24,12], [12,12].
L2 = tf.sigmoid(tf.matmul(L11, W21) + tf.matmul(L12, W22) + b2)
hypothesis = tf.nn.softmax(tf.matmul(L2, W3) + b3)
# Minimize cost.
a = tf.Variable(0.01)
# cost = tf.reduce_mean(hypothesis, Y)
cost = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(hypothesis), reduction_indices=1))
train_step = tf.train.AdamOptimizer(a).minimize(cost)
# Initializa all variables.
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
# Loop
for i in range(1000):
sess.run(train_step, feed_dict={
X1: x1_data,
X2: x2_data,
Y:y_data}
)
if i % 100 == 0:
print(
i,
sess.run(cost, feed_dict={X1:x1_data, X2:x2_data, Y:y_data})
)
In [82]:
correct_prediction = tf.equal(tf.argmax(hypothesis,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={X1: x1_data, X2: x2_data, Y:y_data}))
print(sess.run(tf.argmax(hypothesis,1), feed_dict={X1: x1_data, X2: x2_data, Y:y_data}))
print(sess.run(tf.argmax(Y,1), feed_dict={X1: x1_data, X2: x2_data, Y:y_data}))
In [93]:
print()
data = sess.run(W11, feed_dict={X1: x1_data, X2: x2_data, Y:y_data})
data = data.transpose()
data.shape
Out[93]:
In [94]:
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
for index, values in enumerate(data):
plt.subplot(2, 4, index + 1)
values.shape = (2,12)
plt.axis('off')
plt.imshow(values, cmap=plt.cm.gray_r, interpolation='nearest')
plt.title('Case %i' % index)
In [ ]: