def show_bias(self):
if len(self.weights) is 1:
mp = MyPlot()
mp.set_labels('Step', 'Bias')
mp.show_list(self.biases)
else:
print('Cannot show the bias! Call print_bias mehtod.')
def show_weight(self):
print('shape=', self.weights)
if len(self.weights[0]) is 1:
mp = MyPlot()
mp.set_labels('Step', 'Weight')
mp.show_list(self.weights)
else:
print('Cannot show the weight! Call print_weight method.')
#----- a neuron
w = tf.Variable(tf.random_normal([2, 1]))
b = tf.Variable(tf.random_normal([1]))
hypo = tf.matmul(x, w) + b
#-----
cost = tf.reduce_mean((hypo - y) * (hypo - y))
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
costs = []
for i in range(2001):
sess.run(train)
if i % 50 == 0:
print('hypo:', sess.run(hypo), '|', sess.run(w), sess.run(b),
sess.run(cost))
costs.append(sess.run(cost))
hypo2 = tf.matmul([[4., 4]], w) + b
print(sess.run(hypo2))
p = MyPlot()
p.show_list(costs)
def show_error(self):
mp = MyPlot()
mp.set_labels('Step', 'Error')
mp.show_list(self.costs)
train = tf.train.GradientDescentOptimizer(learning_rate=0.2).minimize(cost)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
errors = []
for step in range(10001):
sess.run(train) #계산그래프, 데이터 4개에 대하여 forward prop. 오류값을 구하고 충분히 작지 않으면
if step % 500 == 0:
#print(step, sess.run(cost))
errors.append(sess.run(cost))
#----- testing(classification)
predicted = tf.cast(hypo > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(predicted, y_data), dtype=tf.float32))
h = sess.run(hypo)
print("\nHypo: ", h)
p = sess.run(predicted)
print("Predicted: ", p)
a = sess.run(accuracy)
print("Accuracy(%): ", a * 100)
from myplot import MyPlot
p = MyPlot()
p.show_list(errors)
from myplot import MyPlot
x_data = [1]
y_data = [1]
#----- a neuron
w = tf.Variable(tf.random_normal([1]))
b = tf.Variable(tf.random_normal([1]))
hypo = w * x_data + b
#-----
cost = (hypo - y_data) ** 2
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
costs = []
for i in range(1001):
sess.run(train)
if i % 50 == 0:
print(sess.run(w), sess.run(b), sess.run(cost))
costs.append(sess.run(cost))
gildong = MyPlot()
gildong.show_list(costs)