机器学习框架的一般套路

谈起机器学习，想必大家都或多或少地对其中多种复杂的框架有些吃不消吧，下面我们就来谈一下机器学习框架的一般套路：

1）获取数据集合；

# Load the data
# iris.data = [(Sepal Length, Sepal Width, Petal Length, Petal Width)]
iris = datasets.load_iris()
x_vals = np.array([[x[0], x[3]] for x in iris.data])
y_vals = np.array([1 if y==0 else -1 for y in iris.target])

# Split data into train/test sets
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]

2）声明数据和标签的类型，具体的是声明数据和标签的占位符操作，等到运行的时候我们再用feed_dict向其中填充真实数据；

# Declare batch size
batch_size = 100

# Initialize placeholders
x_data = tf.placeholder(shape=[None, 2], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

3）声明需要计算的参数类型，具体是采用随机生成的方式，主要是生命其中的shape参数；

# Create variables for linear regression
A = tf.Variable(tf.random_normal(shape=[2,1]))
b = tf.Variable(tf.random_normal(shape=[1,1]))

4）搭建模型同时进行loss函数的声明；

# Declare model operations
model_output = tf.subtract(tf.matmul(x_data, A), b)

# Declare vector L2 'norm' function squared
l2_norm = tf.reduce_sum(tf.square(A))

# Declare loss function
# = max(0, 1-pred*actual) + alpha * L2_norm(A)^2
# L2 regularization parameter, alpha
alpha = tf.constant([0.01])
# Margin term in loss
classification_term = tf.reduce_mean(tf.maximum(0., tf.subtract(1., tf.multiply(model_output, y_target))))
# Put terms together
loss = tf.add(classification_term, tf.multiply(alpha, l2_norm))

5）然后初始化所有变量，同时进行优化器的声明以及对loss函数的优化；

# Declare optimizer
my_opt = tf.train.GradientDescentOptimizer(0.01)
train_step = my_opt.minimize(loss)

# Initialize variables
#init = tf.initialize_all_variables()
init=tf.global_variables_initializer()
sess.run(init)

6）声明预测模型，并进行accuracy准确率的计算

# Declare prediction function
prediction = tf.sign(model_output)
accuracy = tf.reduce_mean(tf.cast(tf.equal(prediction, y_target), tf.float32))

6）多次迭代，填充数据

# Training loop
loss_vec = []
train_accuracy = []
test_accuracy = []
for i in range(500):
rand_index = np.random.choice(len(x_vals_train), size=batch_size)
rand_x = 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})

temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
loss_vec.append(temp_loss)

train_acc_temp = sess.run(accuracy, feed_dict={x_data: x_vals_train, y_target: np.transpose([y_vals_train])})
train_accuracy.append(train_acc_temp)

test_acc_temp = sess.run(accuracy, feed_dict={x_data: x_vals_test, y_target: np.transpose([y_vals_test])})
test_accuracy.append(test_acc_temp)

if (i+1)%100==0:
print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ' b = ' + str(sess.run(b)))
print('Loss = ' + str(temp_loss))

# Extract coefficients
[[a1], [a2]] = sess.run(A)
[[b]] = sess.run(b)
slope = -a2/a1
y_intercept = b/a1

# Extract x1 and x2 vals
x1_vals = [d[1] for d in x_vals]

# Get best fit line
best_fit = []
for i in x1_vals:
best_fit.append(slope*i+y_intercept)

# Separate I. setosa
setosa_x = [d[1] for i,d in enumerate(x_vals) if y_vals[i]==1]
setosa_y = [d[0] for i,d in enumerate(x_vals) if y_vals[i]==1]
not_setosa_x = [d[1] for i,d in enumerate(x_vals) if y_vals[i]==-1]
not_setosa_y = [d[0] for i,d in enumerate(x_vals) if y_vals[i]==-1]

# Plot data and line
plt.plot(setosa_x, setosa_y, 'o', label='I. setosa')
plt.plot(not_setosa_x, not_setosa_y, 'x', label='Non-setosa')
plt.plot(x1_vals, best_fit, 'r-', label='Linear Separator', linewidth=3)
plt.ylim([0, 10])
plt.legend(loc='lower right')
plt.title('Sepal Length vs Pedal Width')
plt.xlabel('Pedal Width')
plt.ylabel('Sepal Length')
plt.show()

# Plot train/test accuracies
plt.plot(train_accuracy, 'k-', label='Training Accuracy')
plt.plot(test_accuracy, 'r--', label='Test Accuracy')
plt.title('Train and Test Set Accuracies')
plt.xlabel('Generation')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()

# Plot loss over time
plt.plot(loss_vec, 'k-')
plt.title('Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Loss')
plt.show()

keras框架的搭建和tensorflow的基本思路差不多，只是进行了一些更高层的封装，具体大家可以多看看keras教程对比记忆，代码摘抄自tensorflow_code,里面有详细的各种tensorflow的代码，使用方法，推荐大家查看源码，具体我将起上传到博客上面，大家如果想看的话随时可以下载，希望大家都能在机器学习领域中探索到自己想要的东西，加油，我们一直在路上！