一看就懂的Tensorflow实战（Logistic回归模型Eager API）

AI异构

发布于 2020-07-29 11:20:04

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发布于 2020-07-29 11:20:04

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Tensorflow Eager API Logistic回归

from __future__ import absolute_import, division, print_function

import tensorflow as tf
import tensorflow.contrib.eager as tfe

设置 Eager API

# Set Eager API
tfe.enable_eager_execution()

导入数据

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./data/", one_hot=False)

Extracting ./data/train-images-idx3-ubyte.gz
Extracting ./data/train-labels-idx1-ubyte.gz
Extracting ./data/t10k-images-idx3-ubyte.gz
Extracting ./data/t10k-labels-idx1-ubyte.gz

设置变量

# Parameters
learning_rate = 0.1
batch_size = 128
num_steps = 1000
display_step = 100

调用 Dataset API 读取数据[3]

Dataset API是TensorFlow 1.3版本中引入的一个新的模块，主要服务于数据读取，构建输入数据的pipeline。

如果想要用到Eager模式，就必须要使用Dataset API来读取数据。

之前有用 placeholder 读取数据，tf.data.Dataset.from_tensor_slices 是另一种方式，其主要作用是切分传入 Tensor 的第一个维度，生成相应的 dataset。以下面的例子为例，是对 mnist.train.images 按batch_size 进行切分。

在Eager模式中，创建Iterator的方式是通过 tfe.Iterator(dataset) 的形式直接创建Iterator并迭代。迭代时可以直接取出值，不需要使用sess.run()。

# Iterator for the dataset
dataset = tf.data.Dataset.from_tensor_slices(
    (mnist.train.images, mnist.train.labels)).batch(batch_size)
dataset_iter = tfe.Iterator(dataset)

定义模型（公式+损失函数+准确率计算）

# Variables
W = tfe.Variable(tf.zeros([784, 10]), name='weights')
b = tfe.Variable(tf.zeros([10]), name='bias')

# Logistic regression (Wx + b)
def logistic_regression(inputs):
    return tf.matmul(inputs, W) + b

# Cross-Entropy loss function
def loss_fn(inference_fn, inputs, labels):
    # Using sparse_softmax cross entropy
    return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=inference_fn(inputs), labels=labels))

# Calculate accuracy
def accuracy_fn(inference_fn, inputs, labels):
    prediction = tf.nn.softmax(inference_fn(inputs))
    correct_pred = tf.equal(tf.argmax(prediction, 1), labels)
    return tf.reduce_mean(tf.cast(correct_pred, tf.float32))

补充: tf.nn.softmax_cross_entropy_with_logits(logits, labels, name=None):[4]

第一个参数logits：就是神经网络最后一层的输出，如果有batch的话，它的大小就是[batchsize，num_classes]，单样本的话，大小就是num_classes
第二个参数labels：实际的标签，大小同上

执行下面两步操作：

返回值是一个向量,对向量求 tf.reduce_mean，得到loss。

设置优化器（SGD）

# SGD Optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)

# Compute gradients
grad = tfe.implicit_gradients(loss_fn)

训练

# Training
average_loss = 0.
average_acc = 0.
for step in range(num_steps):

    # Iterate through the dataset
    try:
        d = dataset_iter.next()
    except StopIteration:  # try...except，except用于处理异常
        # Refill queue
        dataset_iter = tfe.Iterator(dataset)
        d = dataset_iter.next()

    # Images
    x_batch = d[0]
    # Labels
    y_batch = tf.cast(d[1], dtype=tf.int64)

    # Compute the batch loss
    batch_loss = loss_fn(logistic_regression, x_batch, y_batch)
    average_loss += batch_loss
    # Compute the batch accuracy
    batch_accuracy = accuracy_fn(logistic_regression, x_batch, y_batch)
    average_acc += batch_accuracy

    if step == 0:
        # Display the initial cost, before optimizing
        print("Initial loss= {:.9f}".format(average_loss))

    # Update the variables following gradients info
    optimizer.apply_gradients(grad(logistic_regression, x_batch, y_batch))

    # Display info
    if (step + 1) % display_step == 0 or step == 0:
        if step > 0:
            average_loss /= display_step
            average_acc /= display_step
        print("Step:", '%04d' % (step + 1), " loss=",
              "{:.9f}".format(average_loss), " accuracy=",
              "{:.4f}".format(average_acc))
        average_loss = 0.
        average_acc = 0.

Initial loss= 2.302585363
Step: 0001  loss= 2.302585363  accuracy= 0.1172
Step: 0100  loss= 0.952338576  accuracy= 0.7955
Step: 0200  loss= 0.535867393  accuracy= 0.8712
Step: 0300  loss= 0.485415280  accuracy= 0.8757
Step: 0400  loss= 0.433947176  accuracy= 0.8843
Step: 0500  loss= 0.381990731  accuracy= 0.8971
Step: 0600  loss= 0.394154936  accuracy= 0.8947
Step: 0700  loss= 0.391497582  accuracy= 0.8905
Step: 0800  loss= 0.386373132  accuracy= 0.8945
Step: 0900  loss= 0.332039326  accuracy= 0.9096
Step: 1000  loss= 0.358993709  accuracy= 0.9002

测试

# Evaluate model on the test image set
testX = mnist.test.images
testY = mnist.test.labels

test_acc = accuracy_fn(logistic_regression, testX, testY)
print("Testset Accuracy: {:.4f}".format(test_acc))