使用tensorflow的线性回归的例子（五）

我们使用Iris数据，Sepal length为y值而Petal width为x值。

import matplotlib.pyplot as plt

import numpy as np

import tensorflow as tf

from sklearn import datasets

from tensorflow.python.framework import ops

ops.reset_default_graph()

# Load the data

# iris.data = [(Sepal Length, Sepal Width, Petal Length, Petal Width)]

iris = datasets.load_iris()

x_vals = np.array([x[3] for x in iris.data])

y_vals = np.array([y[0] for y in iris.data])

def model(x,w,b):

    # Declare model operations

    model_output = tf.add(tf.matmul(x, w), b)

    return model_output

def loss(x, y,w,b):

    # Declare loss function (L2 loss)

    loss = tf.reduce_mean(tf.square(y - model(x,w,b)))

    return loss

def grad(x,y,w,b):

    with tf.GradientTape() as tape:

        loss_ = loss(x,y,w,b)

return tape.gradient(loss_,[w,b])

# Declare batch size

batch_size = 25

# Create variables for linear regression

w = tf.Variable(tf.random.normal(shape=[1,1]),tf.float32)

b = tf.Variable(tf.random.normal(shape=[1,1]),tf.float32)

optimizer = tf.optimizers.Adam()

# Training loop

loss_vec = []

for i in range(5000):

    rand_index = np.random.choice(len(x_vals), size=batch_size)

    rand_x = np.transpose([x_vals[rand_index]])

    rand_y = np.transpose([y_vals[rand_index]])

    x=tf.cast(rand_x,tf.float32)

    y=tf.cast(rand_y,tf.float32)

    grads=grad(x,y,w,b)

    optimizer.apply_gradients(zip(grads,[w,b]))

    #sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})

    temp_loss = loss(x, y,w,b).numpy()

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

    loss_vec.append(temp_loss)

    if (i+1)%25==0:

        print('Step #' + str(i+1) + ' A = ' + str(w.numpy()) + ' b = ' + str(b.numpy()))

        print('Loss = ' + str(temp_loss))

# Get the optimal coefficients

[slope] = w.numpy()

[y_intercept] = b.numpy()

# Get best fit line

best_fit = []

for i in x_vals:

  best_fit.append(slope*i+y_intercept)

# Plot the result

plt.plot(x_vals, y_vals, 'o', label='Data Points')

plt.plot(x_vals, best_fit, 'r-', label='Best fit line', linewidth=3)

plt.legend(loc='upper left')

plt.title('Sepal Length vs Petal Width')

plt.xlabel('Petal Width')

plt.ylabel('Sepal Length')

plt.show()

# Plot loss over time

plt.plot(loss_vec, 'k-')

plt.title('L2 Loss per Generation')

plt.xlabel('Generation')

plt.ylabel('L2 Loss')

plt.show()