Tensorflow基本操作小白快速构建线性回归和分类模型

@Author:Runsen

TF 目前发布2.5 版本,之前阅读1.X官方文档,最近查看2.X的文档。tensorflow是非常强的工具,生态庞大。

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tensorflow提供了Keras的分支,这里不再提供Keras相关顺序模型教程。

关于环境:ubuntu的 GPU,需要cuda和nvcc

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不会安装:查看

  • 完整的Ubuntu18.04深度学习GPU环境配置,英伟达显卡驱动安装、cuda9.0安装、cudnn的安装、anaconda安装

不安装,直接翻墙用colab

测试GPU

>>> from tensorflow.python.client import device_lib
>>> device_lib.list_local_devices()

这是意思是挂了一个显卡

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具体查看官方文档:https://www.tensorflow.org/install

解决服务器运行Jupyter Notebook方法

Define tensor constants.

import tensorflow as tf

# Create a Tensor.
hello = tf.constant("hello world")
hello

# Define tensor constants.
a = tf.constant(1)
b = tf.constant(6)
c = tf.constant(9)

# tensor变量的操作
# (+, *, ...)
add = tf.add(a, b)
sub = tf.subtract(a, b)
mul = tf.multiply(a, b)
div = tf.divide(a, b)

# 通过numpy返回数值  和torch一样
print("add =", add.numpy())
print("sub =", sub.numpy())
print("mul =", mul.numpy())
print("div =", div.numpy())

add = 7
sub = -5
mul = 6
div = 0.16666666666666666

mean = tf.reduce_mean([a, b, c])
sum_ = tf.reduce_sum([a, b, c])

# Access tensors value.
print("mean =", mean.numpy())
print("sum =", sum_ .numpy())

mean = 5
sum = 16

# Matrix multiplications.
matrix1 = tf.constant([[1., 2.], [3., 4.]])
matrix2 = tf.constant([[5., 6.], [7., 8.]])

product = tf.matmul(matrix1, matrix2)

product

<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[19., 22.],
       [43., 50.]], dtype=float32)>

# Tensor to Numpy.
np_product = product.numpy()
print(type(np_product), np_product)

(numpy.ndarray,
 array([[19., 22.],
        [43., 50.]], dtype=float32))
Linear Regression

下面使用tensorflow快速构建线性回归模型,这里不使用kears的顺序模型,而是采用torch的模型定义的写法。

import numpy as np
import tensorflow as tf

# Parameters:
learning_rate = 0.01
training_steps = 1000
display_step = 50

# Training Data.
X = np.array([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
Y = np.array([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])

random = np.random

# 权重和偏差,随机初始化。
W = tf.Variable(random.randn(), name="weight")
b = tf.Variable(random.randn(), name="bias")

# Linear regression (Wx + b).
def linear_regression(x):
    return W * x + b

# Mean square error.
def mean_square(y_pred, y_true):
    return tf.reduce_mean(tf.square(y_pred - y_true))

# 随机梯度下降优化器。
optimizer = tf.optimizers.SGD(learning_rate)

# 优化过程。
def run_optimization():
    # 将计算包在GradientTape中,以便自动区分。
    with tf.GradientTape() as g:
        pred = linear_regression(X)
        loss = mean_square(pred, Y)

    # 计算梯度。
    gradients = g.gradient(loss, [W, b])
    
    # 按照梯度更新W和b。
    optimizer.apply_gradients(zip(gradients, [W, b]))

#按给定的步数进行训练。
for step in range(1, training_steps + 1):
    # 运行优化以更新W和b值。
    run_optimization()
    
    if step % display_step == 0:
        pred = linear_regression(X)
        loss = mean_square(pred, Y)
        print("Step: %i, loss: %f, W: %f, b: %f" % (step, loss, W.numpy(), b.numpy()))

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import matplotlib.pyplot as plt
plt.plot(X, Y, 'ro', label='Original data')
plt.plot(X, np.array(W * X + b), label='Fitted line')
plt.legend()
plt.show()

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分类模型

本例使用MNIST手写数字。数据集包含60000个训练示例和10000个测试示例。这些数字已经过大小标准化,并在一个固定大小的图像(28x28像素)中居中,值从0到255。

在本例中,每个图像将转换为float32,标准化为[0,1],并展平为784个特征(28×28)的一维数组。

import numpy as np
import tensorflow as tf

#  MNIST data
num_classes = 10      # 0->9 digits
num_features = 784    # 28 * 28

# Parameters 
lr = 0.01
batch_size = 256
display_step = 100
training_steps = 1000

# Prepare MNIST data
from tensorflow.keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Convert to Float32
x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)

# Flatten images into 1-D vector of 784 dimensions (28 * 28)
x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])

# [0, 255] to [0, 1]
x_train, x_test = x_train / 255, x_test / 255

# 打乱顺序: tf.data API to shuffle and batch data
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = train_dataset.repeat().shuffle(5000).batch(batch_size=batch_size).prefetch(1)


# Weight of shape [784, 10] ~= [number_features, number_classes]
W = tf.Variable(tf.ones([num_features, num_classes]), name='weight')

# Bias of shape [10] ~= [number_classes]
b = tf.Variable(tf.zeros([num_classes]), name='bias')

# Logistic regression: W*x + b
def logistic_regression(x):
    # 应用softmax函数将logit标准化为概率分布
    out = tf.nn.softmax(tf.matmul(x, W) + b)
    
    return out

# 交叉熵损失函数
def cross_entropy(y_pred, y_true):
    # 将标签编码为一个one_hot向量
    y_true = tf.one_hot(y_true, depth=num_classes)
    
    # 剪裁预测值避免错误
    y_pred = tf.clip_by_value(y_pred, 1e-9, 1)
    
    # 计算交叉熵
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred), 1))
    
    return cross_entropy

# Accuracy
def accuracy(y_pred, y_true):
    correct = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))
    return tf.reduce_mean(tf.cast(correct, tf.float32))

# 随机梯度下降优化器
optimizer = tf.optimizers.SGD(lr)

# Optimization
def run_optimization(x, y):
    with tf.GradientTape() as g:
        pred = logistic_regression(x)
        loss = cross_entropy(y_pred=pred, y_true=y)
    gradients = g.gradient(loss, [W, b])
    
    optimizer.apply_gradients(zip(gradients, [W, b]))

# Training
for step, (batch_x, batch_y) in enumerate(train_dataset.take(training_steps), 1):
    # Run the optimization to update W and b
    run_optimization(x=batch_x, y=batch_y)
    
    if step % display_step == 0:
        pred = logistic_regression(batch_x)
        loss = cross_entropy(y_pred=pred, y_true=batch_y)
        acc = accuracy(y_pred=pred, y_true=batch_y)
        print("Step: %i, loss: %f, accuracy: %f" % (step, loss, acc))

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pred = logistic_regression(x_test)
print(f"Test Accuracy: {accuracy(pred, y_test)}")

Test Accuracy: 0.892300009727478

import matplotlib.pyplot as plt
n_images = 5
test_images = x_test[:n_images]
predictions = logistic_regression(test_images)
# 预测前5张
for i in range(n_images):
    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')
    plt.show()
    print("Model prediction: %i" % np.argmax(predictions.numpy()[i]))

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Model prediction: 7

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Model prediction: 2

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Model prediction: 1

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Model prediction: 0

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Model prediction: 4

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