TensorFlowのプログラムを読む2　非線形回帰

1. モデル
予測値Y’ 真の予測値Y
損失関数
損失率
オプティマイザー
①モデルは何か？
②損失関数は何を使っているか？
③オプティマイザーは何を使っているか？

2. 15num_coeffs = 6
16trY_coeffs = [1, 2, 3, 4, 5, 6]
17trY = 0
18for i in range(num_coeffs):
19 #print(i)
20 trY += trY_coeffs[i] * np.power(trX, i)
21 #print(trX)
22 #print(trY)
23
24trY += np.random.randn(*trX.shape) * 1.5
25
26#print(trX)
27#print(trY)
28plt.scatter(trX, trY)
29plt.show()
F(x) = Wn*X^n + … + W1*x + W0
https://github.com/RuoAndo/seminar/blob/master/jt/7-nonlinear.py

3. 41w = tf.Variable([0.] * num_coeffs, name="parameters")
42y_model = model(X, w)
43
44cost = tf.reduce_sum(tf.square(Y-y_model))
45train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
46
47sess = tf.Session()
48init = tf.global_variables_initializer()
49sess.run(init)
50
51for epoch in range(training_epochs):
52 for (x, y) in zip(trX, trY):
53 sess.run(train_op, feed_dict={X: x, Y: y})
54
55w_val = sess.run(w)
56print(w_val)
[1.0518903 1.7557341 4.2570124 5.756541 3.2840345 5.1628995]
session.runの引数
train_opを探す。

4. 41w = tf.Variable([0.] * num_coeffs, name="parameters")
42y_model = model(X, w)
43
44cost = tf.reduce_sum(tf.square(Y-y_model))
45train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
46
47sess = tf.Session()
48init = tf.global_variables_initializer()
49sess.run(init)
50
51for epoch in range(training_epochs):
52 for (x, y) in zip(trX, trY):
53 sess.run(train_op, feed_dict={X: x, Y: y})
54
55w_val = sess.run(w)
56print(w_val)
[1.0518903 1.7557341 4.2570124 5.756541 3.2840345 5.1628995]
損失関数は
何か？

5. 41w = tf.Variable([0.] * num_coeffs, name="parameters")
42y_model = model(X, w)
43
44cost = tf.reduce_sum(tf.square(Y-y_model))
45train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
46
47sess = tf.Session()
48init = tf.global_variables_initializer()
49sess.run(init)
50
51for epoch in range(training_epochs):
52 for (x, y) in zip(trX, trY):
53 sess.run(train_op, feed_dict={X: x, Y: y})
54
55w_val = sess.run(w)
56print(w_val)
[1.0518903 1.7557341 4.2570124 5.756541 3.2840345 5.1628995]
モデルを探す。

6. 41w = tf.Variable([0.] * num_coeffs, name="parameters")
42y_model = model(X, w)
43
44cost = tf.reduce_sum(tf.square(Y-y_model))
45train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
46
47sess = tf.Session()
48init = tf.global_variables_initializer()
49sess.run(init)
50
51for epoch in range(training_epochs):
52 for (x, y) in zip(trX, trY):
53 sess.run(train_op, feed_dict={X: x, Y: y})
54
55w_val = sess.run(w)
56print(w_val)
[1.0518903 1.7557341 4.2570124 5.756541 3.2840345 5.1628995]
34def model(X, w):
35 terms = []
36 for i in range(num_coeffs):
37 term = tf.multiply(w[i], tf.pow(X, i))
38 terms.append(term)
39 return tf.add_n(terms)
モデル

7. 34def model(X, w):
35 terms = []
36 for i in range(num_coeffs):
37 term = tf.multiply(w[i], tf.pow(X, i))
38 terms.append(term)
39 return tf.add_n(terms)
モデル
15num_coeffs = 6
16trY_coeffs = [1, 2, 3, 4, 5, 6]
17trY = 0
18for i in range(num_coeffs):
19 #print(i)
20 trY += trY_coeffs[i] * np.power(trX, i)
生成器

8. 60plt.scatter(trX, trY)
61trY2 = 0
62for i in range(num_coeffs):
63 trY2 += w_val[i] * np.power(trX, i)
64plt.plot(trX, trY2, 'r')
65plt.show()
15num_coeffs = 6
16trY_coeffs = [1, 2, 3, 4, 5, 6]
17trY = 0
18for i in range(num_coeffs):
19 #print(i)
20 trY += trY_coeffs[i] * np.power(trX, i)
生成器