TensorFlow实现模型评估

我们需要评估模型预测值来评估训练的好坏。

模型评估是非常重要的，随后的每个模型都有模型评估方式。使用TensorFlow时，需要把模型评估加入到计算图中，然后在模型训练完后调用模型评估。

在训练模型过程中，模型评估能洞察模型算法，给出提示信息来调试、提高或者改变整个模型。但是在模型训练中并不是总需要模型评估，我们将展示如何在回归算法和分类算法中使用它。

训练模型之后，需要定量评估模型的性能如何。在理想情况下，评估模型需要一个训练数据集和测试数据集，有时甚至需要一个验证数据集。

想评估一个模型时就得使用大批量数据点。如果完成批量训练，我们可以重用模型来预测批量数据点。但是如果要完成随机训练，就不得不创建单独的评估器来处理批量数据点。

分类算法模型基于数值型输入预测分类值，实际目标是1和0的序列。我们需要度量预测值与真实值之间的距离。分类算法模型的损失函数一般不容易解释模型好坏，所以通常情况是看下准确预测分类的结果的百分比。

不管算法模型预测的如何，我们都需要测试算法模型，这点相当重要。在训练数据和测试数据上都进行模型评估，以搞清楚模型是否过拟合。

# TensorFlowm模型评估 # # This code will implement two models. The first # is a simple regression model, we will show how to # call the loss function, MSE during training, and # output it after for test and training sets. # # The second model will be a simple classification # model. We will also show how to print percent # classified correctly during training and after # for both the test and training sets. import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from tensorflow.python.framework import ops ops.reset_default_graph() # 创建计算图 sess = tf.Session() # 回归例子: # We will create sample data as follows: # x-data: 100 random samples from a normal ~ N(1, 0.1) # target: 100 values of the value 10. # We will fit the model: # x-data * A = target # 理论上, A = 10. # 声明批量大小 batch_size = 25 # 创建数据集 x_vals = np.random.normal(1, 0.1, 100) y_vals = np.repeat(10., 100) x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32) # 八二分训练/测试数据 train/test = 80%/20% 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] # 创建变量 (one model parameter = A) A = tf.Variable(tf.random_normal(shape=[1,1])) # 增加操作到计算图 my_output = tf.matmul(x_data, A) # 增加L2损失函数到计算图 loss = tf.reduce_mean(tf.square(my_output - y_target)) # 创建优化器 my_opt = tf.train.GradientDescentOptimizer(0.02) train_step = my_opt.minimize(loss) # 初始化变量 init = tf.global_variables_initializer() sess.run(init) # 迭代运行 # 如果在损失函数中使用的模型输出结果经过转换操作，例如，sigmoid_cross_entropy_with_logits（）函数， # 为了精确计算预测结果，别忘了在模型评估中也要进行转换操作。 for i in range(100): rand_index = np.random.choice(len(x_vals_train), size=batch_size) rand_x = np.transpose([x_vals_train[rand_index]]) rand_y = np.transpose([y_vals_train[rand_index]]) sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y}) if (i+1)%25==0: print(Step # + str(i+1) + A = + str(sess.run(A))) print(Loss = + str(sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y}))) # 评估准确率(loss) mse_test = sess.run(loss, feed_dict={x_data: np.transpose([x_vals_test]), y_target: np.transpose([y_vals_test])}) mse_train = sess.run(loss, feed_dict={x_data: np.transpose([x_vals_train]), y_target: np.transpose([y_vals_train])}) print(MSE on test: + str(np.round(mse_test, 2))) print(MSE on train: + str(np.round(mse_train, 2))) # 分类算法案例 # We will create sample data as follows: # x-data: sample 50 random values from a normal = N(-1, 1) # + sample 50 random values from a normal = N(1, 1) # target: 50 values of 0 + 50 values of 1. # These are essentially 100 values of the corresponding output index # We will fit the binary classification model: # If sigmoid(x+A) < 0.5 -> 0 else 1 # Theoretically, A should be -(mean1 + mean2)/2 # 重置计算图 ops.reset_default_graph() # 加载计算图 sess = tf.Session() # 声明批量大小 batch_size = 25 # 创建数据集catenate((np.random.normal(-1, 1, 50), np.random.normal(2, 1, 50))) 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) # 分割数据集 train/test = 80%/20% 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] # 创建变量 (one model parameter = A) A = tf.Variable(tf.random_normal(mean=10, shape=[1])) # Add operation to graph # Want to create the operstion sigmoid(x + A) # Note, the sigmoid() part is in the loss function my_output = tf.add(x_data, A) # 增加分类损失函数 (cross entropy) xentropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output, labels=y_target)) # Create Optimizer my_opt = tf.train.GradientDescentOptimizer(0.05) train_step = my_opt.minimize(xentropy) # Initialize variables init = tf.global_variables_initializer() sess.run(init) # 运行迭代 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}))) # 评估预测 # 用squeeze（）函数封装预测操作，使得预测值和目标值有相同的维度。 y_prediction = tf.squeeze(tf.round(tf.nn.sigmoid(tf.add(x_data, A)))) # 用equal（）函数检测是否相等, # 把得到的true或false的boolean型张量转化成float32型， # 再对其取平均值，得到一个准确度值。 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)) # 绘制分类结果 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=white) 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()

输出：

Step #25 A = [[ 5.79096079]] Loss = 16.8725 Step #50 A = [[ 8.36085415]] Loss = 3.60671 Step #75 A = [[ 9.26366138]] Loss = 1.05438 Step #100 A = [[ 9.58914948]] Loss = 1.39841 MSE on test:1.04 MSE on train:1.13 Step #200 A = [ 5.83126402] Loss = 1.9799 Step #400 A = [ 1.64923656] Loss = 0.678205 Step #600 A = [ 0.12520729] Loss = 0.218827 Step #800 A = [-0.21780498] Loss = 0.223919 Step #1000 A = [-0.31613481] Loss = 0.234474 Step #1200 A = [-0.33259964] Loss = 0.237227 Step #1400 A = [-0.28847221] Loss = 0.345202 Step #1600 A = [-0.30949864] Loss = 0.312794 Step #1800 A = [-0.33211425] Loss = 0.277342 Accuracy on train set: 0.9625 Accuracy on test set: 1.0

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