20210608 TensorFlow 实现数字图片分类

千磨万击还坚劲，任尔东西南北风。这篇文章主要讲述20210608 TensorFlow 实现数字图片分类相关的知识，希望能为你提供帮助。
0-1 导包

import warnings warnings.filterwarnings("ignore")import keras import numpy as np import tensorflow as tf import matplotlib.pyplot as plt

1-1 构造数据
调用接口去下载数据

mnist = keras.datasets.mnist# 导入 mnist (train_images, train_labels),(test_images,test_labels) = mnist.load_data()

60000条训练集，10000条测试集

print("train image shape:",train_images.shape,"train label",train_labels.shape) print("test image shape:",test_images.shape,"test label",test_labels.shape)

-->
train image shape: (60000, 28, 28) train label (60000,)
test image shape: (10000, 28, 28) test label (10000,)
60000条训练集分为训练的图片和标签，图片是手写的数字图片，长和宽都是 28，标签就是数字集，测试集是 10000 个图片，测试标签也是 10000

1-1-1 显示数据和标签

print("image data:",train_images[0]) print("label data:",train_labels[0])

# 数据太多太大，这里便不展示了
# label data: 5

1-1-2

defplot_img(img): plt.imshow(img.reshape(28,28),cmap="binary")# camp = "binary" 使用灰度图表示 plt.show() # 查看图片 plot_img(train_images[3]) plot_img(train_images[0])

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1-1-3 现在进行一个 0 1 的二分类

data_0 = [] data_1 = [] # zip 把里面的子数据拿出来了，img是(28,28)；label 标签是一个值 for img,label in zip(train_images, train_labels): if label == 0: # 首先需要把图片拍平，现在的训练是一种类似 DNN 神经网络的模式 # 所以无法传入 28*28 的图片进行训练，reshape(28*28)，将二阶变为一阶的 784 # 除以 255 的作用是，约束到 0 到 1 之间 img = img.reshape(28*28)/255 # 将图片和 label 放一起，变成 785 个数据，赋给 img img = np.append(img,label) data_0.append(img) if label == 1: img = img.reshape(28*28)/255 img = np.append(img,label) data_1.append(img) data_0 = np.array(data_0) data_1 = np.array(data_1) all_data = https://www.songbingjia.com/android/np.concatenate([data_0,data_1]) print(all_data.shape)

# --> (12665, 785)
# 前 784 是像素，最后的 1 是标签

np.random.shuffle(all_data)# np.random.shuffle 打乱顺序，避免前面全是0，后面全是 1 # 切分训练集和数据集，按理说 20% 测试集，80%训练集，这里只是简单的前面的作为训练集，后200个作为测试集 train_data = https://www.songbingjia.com/android/all_data[:-200] test_data = all_data[-200:]

2-1 数据分块

# 生成器 def gen_batch(data,batch_size): np.random.shuffle(data)# 打乱，增加随机性 for i in range(len(data) // batch_size): cursor = batch_size * i batch_data = https://www.songbingjia.com/android/data[cursor : cursor + batch_size] x = batch_data[:, 0:784]# 第一维度全取，第二维度取前 784 个像素，最后一个是标签 y = batch_data[:, 784] yield x,y.reshape(-1,1) remainder = len(data) % batch_size if remainder != 0: x, y = data[-remainder:, 0:784], data[-remainder:, 784] yield x, y.reshape(-1,1) for x_,y_ in gen_batch(train_data,128): print(x_.shape) print(y_.shape) print(/'-------\') break

# -->
# (128, 784)
# (128, 1)
# -------

3-1 超参数

learing_rate = 0.01 num_train_epochs = 50# 循环训练集总轮数 display_per_step = 100 batch_size = 128

4-1 计算图

graph = tf.Graph() with graph.as_default(): x = tf.placeholder(shape=[None,784], dtype=tf.float32, name=\'x\') y = tf.placeholder(shape=[None,1], dtype=tf.float32, name=\'y\') w =tf.Variable(tf.ones(shape=[784,1]), dtype=tf.float32) b =tf.Variable(0, dtype=tf.float32) logits = tf.matmul(x, w) + b y_pred = tf.sigmoid(logits) # 定义loss with tf.name_scope("loss"): loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits), name="calculate_loss") # 定义优化器 with tf.name_scope("SGD"): optimizer = tf.train.GradientDescentOptimizer(learing_rate) train_step = optimizer.minimize(loss) # 定义正确率 with tf.name_scope("calculation_accuracy"): res_pred = tf.cast(tf.greater_equal(y_pred, 0.5), dtype=tf.float32) acc = tf.reduce_mean(tf.cast(tf.equal(res_pred, y), dtype=tf.float32))

5-1 运行计算图

with tf.Session(graph=graph) as sess: init = tf.global_variables_initializer() sess.run(init) step = 0 for epoch in range(num_train_epochs): for x_, y_ in gen_batch(train_data, batch_size): step += 1 _, l, acc_ = sess.run([train_step, loss, acc], feed_dict={x: x_, y: y_}) if step % display_per_step == 0: print("step: {:> 4}, loss: {:.4}, acc: {:.4%}".format(step, l, acc_)) print(\'training over\') x_test,y_test = next(gen_batch(test_data,200))# 取出全部测试集数据 # 查看测试集的 loss 和正确率 loss_test, acc_test = sess.run([loss, acc],feed_dict={x: x_test, y: y_test}) print("test loss is {:.4}, acc is {:.4%}".format(loss_test, acc_test)) res_weights = sess.run([w, b]) # res_weights 存在的目的是进行模型的预测，给出一张图片，判断能否正确预测

-->
step:100, loss: 43.14, acc: 60.1562%
step:200, loss: 34.86, acc: 50.7812%
step:300, loss: 19.21, acc: 52.3438%
……
step: 4700, loss: 0.09711, acc: 96.8750%
step: 4800, loss: 0.1117, acc: 97.6562%
step: 4900, loss: 0.0001993, acc: 100.0000%
training over
test loss is 0.04518, acc is 99.0000%

6-1 模型预测

def plot_img(img): plt.imshow(img.reshape(28,28),cmap="binary") plt.show() test_img = test_data[7,:784] plot_img(test_img)

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6-2

graph = tf.Graph() with graph.as_default(): x = tf.placeholder(shape=[None,784], dtype=tf.float32, name=\'x\') y = tf.placeholder(shape=[None,1], dtype=tf.float32, name=\'y\') # w 和 b 是前面训练好的值 w =tf.Variable(res_weights[0], dtype=tf.float32) b =tf.Variable(res_weights[1], dtype=tf.float32) logits = tf.matmul(x, w) + b y_pred = tf.sigmoid(logits) # 定义loss with tf.name_scope("loss"): loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits), name="calculate_loss") # 定义优化器 with tf.name_scope("SGD"): optimizer = tf.train.GradientDescentOptimizer(learing_rate) train_step = optimizer.minimize(loss) # 定义正确率 with tf.name_scope("calculation_accuracy"): res_pred = tf.cast(tf.greater_equal(y_pred, 0.5), dtype=tf.float32) acc = tf.reduce_mean(tf.cast(tf.equal(res_pred, y), dtype=tf.float32))

with tf.Session(graph=graph) as sess: # 初始化权重，将前面训练好的权重，放到计算图里 init = tf.global_variables_initializer() sess.run(init) # test_data[7,:784].shape 是 (784,) # 但是x = tf.placeholder(shape=[None,784], dtype=tf.float32, name=\'x\') # 所以需要 reshape 成为 (1,-1)，也就是第一维是 1，第二维就是 784/1 # 所以当前这里图片的 shape 就是 (1,784) x_ = test_data[7,:784].reshape(1,-1) # 从这地方可以看出，预测输出的时候，并没有使用标签，所以传参时只传 x 就可以 res_p = sess.run(res_pred, feed_dict={x: x_}) print(res_p)

【20210608 TensorFlow 实现数字图片分类】# --> [[0.]]
预测出的结果是 0，所以简单的网络结构确实可以对测试集的手写数字图片进行预测，这就是一个数字的图片分类

部分代码解释：
1. 1-1-3 中的 zip()
https://blog.51cto.com/u_15149862/2847102
2. 1-1-3 中列表的使用，比如 train_data = https://www.songbingjia.com/android/all_data[:-200]
https://blog.51cto.com/u_15149862/2704954
3. 2-1 中的生成器
https://blog.51cto.com/u_15149862/2844458
4. TensorFlow 基本用法
https://blog.51cto.com/u_15149862/2825353