TF之LiR:利用TF自定义一个线性分类器LiR对乳腺癌肿瘤数据集进行二分类预测(良/恶性)
目录
- X_train = np.float32(train[['Clump Thickness', 'Cell Size']].T)
- y_train = np.float32(train['Type'].T)
- X_test = np.float32(test[['Clump Thickness', 'Cell Size']].T)
- y_test = np.float32(test['Type'].T)
-
- b = tf.Variable(tf.zeros([1]))
- W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
- y = tf.matmul(W, X_train) + b
-
- loss = tf.reduce_mean(tf.square(y - y_train))
- optimizer = tf.train.GradientDescentOptimizer(0.01)
- train = optimizer.minimize(loss)
-
-
- sess = tf.Session()
- sess.run(init)
-
- for step in range(0, 1000):
- sess.run(train)
- if step % 200 == 0:
- print(step, sess.run(W), sess.run(b))
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