再来一个tensorflow的测试性能的代码

Posted 2021-12-16 aguncn

感觉这个比前一套，容易理解些~~

关于数据提前下载的问题：

https://www.jianshu.com/p/5116046733fe

如果使用keras的cifar10.load_data()函数，你会发现，代码会自动去下载 cifar-10-python.tar.gz 文件
实际上，通过查看cifar10.py和site-packages/keras/utils/data_utils.py的get_file函数，你会发现，代码将将下载后的文件存放在 ～./keras/datasets目录下，但是！！！！文件名却被改成了 cifar-10-batches-py.tar.gz

惊不惊喜，意不意外？所以如果要避免下载，已经有数据集了，应该:
cp cifar-10-python.tar.gz ～./keras/datasets/cifar-10-batches-py.tar.gz

完美解决问题！

作者：不爱吃饭的小孩怎么办
链接：https://www.jianshu.com/p/5116046733fe
来源：简书
简书著作权归作者所有，任何形式的转载都请联系作者获得授权并注明出处。

import timeit
import tensorflow as tf
import numpy as np
from tensorflow.keras.datasets.cifar10 import load_data


def model():
    x = tf.placeholder(tf.float32, shape=[None, 32, 32, 3])
    y = tf.placeholder(tf.float32, shape=[None, 10])
    rate = tf.placeholder(tf.float32)
    # convolutional layer 1
    conv_1 = tf.layers.conv2d(x, 32, [3, 3], padding=‘SAME‘, activation=tf.nn.relu)
    max_pool_1 = tf.layers.max_pooling2d(conv_1, [2, 2], strides=2, padding=‘SAME‘)
    drop_1 = tf.layers.dropout(max_pool_1, rate=rate)
    # convolutional layer 2
    conv_2 = tf.layers.conv2d(drop_1, 64, [3, 3], padding="SAME", activation=tf.nn.relu)
    max_pool_2 = tf.layers.max_pooling2d(conv_2, [2, 2], strides=2, padding="SAME")
    drop_2 = tf.layers.dropout(max_pool_2, rate=rate)
    # convolutional layers 3
    conv_3 = tf.layers.conv2d(drop_2, 128, [3, 3], padding="SAME", activation=tf.nn.relu)
    max_pool_3 = tf.layers.max_pooling2d(conv_3, [2, 2], strides=2, padding="SAME")
    drop_3 = tf.layers.dropout(max_pool_3, rate=rate)
    # fully connected layer 1
    flat = tf.reshape(drop_3, shape=[-1, 4 * 4 * 128])
    fc_1 = tf.layers.dense(flat, 80, activation=tf.nn.relu)
    drop_4 = tf.layers.dropout(fc_1 , rate=rate)
    # fully connected layer 2 or the output layers
    fc_2 = tf.layers.dense(drop_4, 10)
    output = tf.nn.relu(fc_2)
    # accuracy
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(output, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    # loss
    loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=y))
    # optimizer
    optimizer = tf.train.AdamOptimizer(1e-4, beta1=0.9, beta2=0.999, epsilon=1e-8).minimize(loss)
    return x, y, rate, accuracy, loss, optimizer


def one_hot_encoder(y):
    ret = np.zeros(len(y) * 10)
    ret = ret.reshape([-1, 10])
    for i in range(len(y)):
        ret[i][y[i]] = 1
    return (ret)


def train(x_train, y_train, sess, x, y, rate, optimizer, accuracy, loss):
    batch_size = 128
    y_train_cls = one_hot_encoder(y_train)
    start = end = 0
    for i in range(int(len(x_train) / batch_size)):
        if (i + 1) % 100 == 1:
            start = timeit.default_timer()
        batch_x = x_train[i * batch_size:(i + 1) * batch_size]
        batch_y = y_train_cls[i * batch_size:(i + 1) * batch_size]
        _, batch_loss, batch_accuracy = sess.run([optimizer, loss, accuracy], feed_dict=x:batch_x, y:batch_y, rate:0.4)
        if (i + 1) % 100 == 0:
            end = timeit.default_timer()
            print("Time:", end-start, "s the loss is ", batch_loss, " and the accuracy is ", batch_accuracy * 100, "%")


def test(x_test, y_test, sess, x, y, rate, accuracy, loss):
    batch_size = 64
    y_test_cls = one_hot_encoder(y_test)
    global_loss = 0
    global_accuracy = 0
    for t in range(int(len(x_test) / batch_size)):
        batch_x = x_test[t * batch_size : (t + 1) * batch_size]
        batch_y = y_test_cls[t * batch_size : (t + 1) * batch_size]
        batch_loss, batch_accuracy = sess.run([loss, accuracy], feed_dict=x:batch_x, y:batch_y, rate:1)
        global_loss += batch_loss
        global_accuracy += batch_accuracy
    global_loss = global_loss / (len(x_test) / batch_size)
    global_accuracy = global_accuracy / (len(x_test) / batch_size)
    print("In Test Time, loss is ", global_loss, ‘ and the accuracy is ‘, global_accuracy)


EPOCH = 100
(x_train, y_train), (x_test, y_test) = load_data()
print("There is ", len(x_train), " training images and ", len(x_test), " images")
x, y, rate, accuracy, loss, optimizer = model()
sess = tf.Session()
sess.run(tf.global_variables_initializer())

for i in range(EPOCH):
    print("Train on epoch ", i ," start")
    train(x_train, y_train, sess, x, y, rate, optimizer, accuracy, loss)
    test(x_train, y_train, sess, x, y, rate, accuracy, loss)