二分类问题的模型评估

创建数据之后，将它们随机分割成训练数据集和测试数据集，分别训练和测试一个二分类模型，最后可视化。

代码如下：

from tensorflow.python.framework import ops
ops.reset_default_graph()
sess = tf.Session()
batch_size = 25
x_vals = np.concatenate((np.random.normal(-1,1,50),np.random.normal(2,1,50)))  #拼接数组
y_vals = np.concatenate((np.repeat(0.,50),np.repeat(1.,50)))
x_data = tf.placeholder(shape=[1,None],dtype=tf.float32)
y_target = tf.placeholder(shape=[1,None],dtype=tf.float32)
train_indices = np.random.choice(len(x_vals),round(len(x_vals)*0.8), replace=False)
test_indices = np.array(list(set(range(len(x_vals)))-set(train_indices)))
x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]
A = tf.Variable(tf.random_normal(mean=10, shape=[1]))
my_output = tf.add(x_data,A)
init = tf.global_variables_initializer()
sess.run(init)
xentropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_target,logits=my_output))
my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(xentropy)
for i in range(1800):
    rand_index = np.random.choice(len(x_vals_train), size=batch_size)
    rand_x = [x_vals_train[rand_index]]
    rand_y = [y_vals_train[rand_index]]
    sess.run(train_step, feed_dict={x_data:rand_x,y_target:rand_y})
    if(i+1)%200 == 0:
        print('Step #'+str(i+1)+' A = '+str(sess.run(A)))
        print('Loss = '+str(sess.run(xentropy,feed_dict={x_data:rand_x,y_target:rand_y})))
y_prediction = tf.squeeze(tf.round(tf.nn.sigmoid(tf.add(x_data, A))))
correct_prediction = tf.equal(y_prediction, y_target)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
acc_value_test = sess.run(accuracy, feed_dict={x_data: [x_vals_test],y_target:[y_vals_test]})
acc_value_train = sess.run(accuracy, feed_dict={x_data: [x_vals_train],y_target:[y_vals_train]})
print('Accuracy on train set: '+str(acc_value_train))
print('Accuracy on test set: '+str(acc_value_test))
A_result = sess.run(A)
bins = np.linspace(-5,5,50)
plt.hist(x_vals[0:50],bins,alpha=0.5,label='N(-1,1)',color='blue')
plt.hist(x_vals[50:100],bins[0:50],alpha=0.5,label='N(2,1)',color='red')
plt.plot((A_result,A_result),(0,8),'k--',linewidth=3,label='A = '+str(np.round(A_result,2)))
plt.legend(loc='upper right')
plt.title('Binary Classifier, Accuracy = '+str(np.round(acc_value_test,2)))
plt.show()

可视化后的结果：