【人工智能】实验一：基于MLP的手写体字符识别

这个算是个记录吧，这个是黑龙江大学-马吉权老师的人工智能课，这个老师听说科研很好，但是他交的就是有点云里雾里，主要是概念，没有实际代码，令人感叹，不过没有期末考试，还挺好过的。

———————————————————————————————————————————

实验的主要内容就是将一个28*28的图化为[0-1，784]的值，前面是灰度值。

构筑网络

loss值

这个是老师给的代码

importosimporttensorflow as tffromtensorflow import kerasfromtensorflow.keras import layers, optimizers, datasetsimport numpy as npos.environ['TF_CPP_MIN_LOG_LEVEL']='2'def load_minist_data(path='mnist.npz'):"""Loads the MNIST dataset.Arguments:path: path where to cache the dataset locally(relative to ~/.keras/datasets).Returns:Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.License:Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset,which is a derivative work from original NIST datasets.MNIST dataset is made available under the terms of the[Creative Commons Attribution-Share Alike 3.0 license.](https://creativecommons.org/licenses/by-sa/3.0/)"""path = "D:/E_Class/deep_learning/project_2/mnist.npz"with np.load(path) as f:x_train, y_train = f['x_train'], f['y_train']x_test, y_test = f['x_test'], f['y_test']return (x_train, y_train), (x_test, y_test)#Load MNIST DATA from file "mnist.npz" , please add your code bellow:(x, y), (x_val, y_val) = ___________________________________#Convert data to tensor, and then make normalization for hand writing digit x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.#Convert data to tensor, please add your code bellow:y = _____________________(y, dtype=tf.int32)#Here, y is a int value, please transfer it to one hot coding with "depth=10" using tesorflow command#, please add your code bellow:y = _____________________________________________print(x.shape, y.shape)train_dataset = tf.data.Dataset.from_tensor_slices((x, y))#Please set the batch size, for instance 100 or 200, please add your code bellow:train_dataset = train_dataset.batch(_______________________)#请按照上面train_dataset的数据准备方法（tf.data.Dataset.from_tensor_slices),准备test_dataset,#Please add your code bellow:test_dataset = _________________________________________________test_dataset = _________________________________________________ #Bellow is defination of hidden-layer in network, you have the chice to make dicision about the number # of neurons, the activation is 'relu', please add your code bellow:model = keras.Sequential([layers.Dense(_______________, activation='relu'),layers.Dense(__________________________________),layers.Dense(_______________, activation='softmax')])optimizer = optimizers.SGD(learning_rate=0.001)def train_epoch(epoch):# Step4.loopfor step, (x, y) in enumerate(train_dataset):with tf.GradientTape() as tape:# [b, 28, 28] => [b, 784]x = tf.reshape(x, (-1, 28*28))# Step1. compute output# [b, 784] => [b, 10]out = model(x)# Step2. compute lossloss = tf.reduce_sum(tf.square(out - y)) / x.shape[0]# Step3. optimize and update w1, w2, w3, b1, b2, b3grads = tape.gradient(loss, model.trainable_variables)# w' = w - lr * gradoptimizer.apply_gradients(zip(grads, model.trainable_variables))if step % 100 == 0:print(epoch, step, 'loss:', loss.numpy())#在训练完成后，请利用已经得到的model验证在测试集上的结果，请仿照上面training_epoch的在下面写出你的具体测试代码#并输出测试结果（预测值，groud truth），注意只是测试不需要计算loss和计算梯度def test():——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————def train():for epoch in range(30):train_epoch(epoch)#30个epoch之后调用test_epoch,test()#如果你学有余力，请利用我们给的hand writing digit image测试用例，试一试你训练的model（选做）#注意你需要利用Python先读入test.jpg图像，然后把它转换为灰度图像，然后将图像由28*28转化为784长度的向量，#然后送入模型，最后输出判别结果。if __name__ == '__main__':train()

自己补完后

import osfrom tkinter import *import tensorflow as tffrom tensorflow.python import kerasfrom tensorflow.keras import *import numpy as np# import matplotlib.pyplot as pltfrom PIL import Image as imgos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'# 只显示WARNING ERROR# print("tf.version", tf.__version__)# # print("tf is using GPU", tf.test.is_gpu_available())# print("tf is using GPU", tf.config.list_physical_devices(device_type='GPU'))# print(tf.config.experimental.list_physical_devices(device_type='CPU'))## physical_device = tf.config.experimental.list_physical_devices(device_type='GPU')# tf.config.experimental.set_memory_growth(physical_device[0], True)## print("tf is using GPU", tf.config.list_physical_devices(device_type='GPU'))def load_minist_data():"""Loads the MNIST dataset.Arguments:path: path where to cache the dataset locally(relative to ~/.keras/datasets).Returns:Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.License:Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset,which is a derivative work from original NIST datasets.MNIST dataset is made available under the terms of the[Creative Commons Attribution-Share Alike 3.0 license.](https://creativecommons.org/licenses/by-sa/3.0/)"""path = "./mnist.npz"with np.load(path) as f:x_train, y_train = f['x_train'], f['y_train']x_test, y_test = f['x_test'], f['y_test']return (x_train, y_train), (x_test, y_test)# Load MNIST DATA from file "mnist.npz" , please add your code bellow:(x, y), (x_val, y_val) = datasets.mnist.load_data()# Convert data to tensor, and then make normalization for hand writing digitx = tf.convert_to_tensor(x, dtype=tf.float32) / 255.# 归一化，0-1# Convert data to tensor, please add your code bellow:y = tf.convert_to_tensor(y, dtype=tf.int32)# Here, y is a int value, please transfer it to one hot coding with "depth=10" using tesorflow command# , please add your code bellow:y = tf.one_hot(y, depth=10)print(x.shape, y.shape)train_dataset = tf.data.Dataset.from_tensor_slices((x, y))# Please set the batch size, for instance 100 or 200, please add your code bellow:train_dataset = train_dataset.batch(200)# 请按照上面train_dataset的数据准备方法（tf.data.Dataset.from_tensor_slices),准备test_dataset,# Please add your code bellow:x_val = tf.convert_to_tensor(x_val, dtype=tf.float32) / 255.# 灰度y_val = tf.convert_to_tensor(y_val, dtype=tf.int32)y_val = tf.one_hot(y_val, depth=10)# （0，0，0，0，0，0，0，0，0，0）print(x_val.shape, y_val.shape)# plot_image(train_images[1])test_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))test_dataset = train_dataset.batch(200)# Bellow is defination of hidden-layer in network, you have the chice to make dicision about the number# of neurons, the activation is 'relu', please add your code bellow:model = Sequential([#连接层784-256layers.Dense(256, activation=tf.nn.relu),# [b,784]=>[b.256]layers.Dense(128, activation=tf.nn.relu),# [b,784]=>[b.128]layers.Dense(64, activation=tf.nn.relu), # [b,784]=>[b.64]layers.Dense(32, activation=tf.nn.relu),# [b,64]=>[b.32]layers.Dense(10),# [b,32]=>[b.10] 330=32*10+10])model.build(input_shape=[None, 28*28])model.summary()optimizer = optimizers.SGD(learning_rate=0.001)def train_epoch(epoch):# 1个epoch表示过了1遍训练集中的所有样本。# Step4.loopfor step, (x, y) in enumerate(train_dataset):with tf.GradientTape() as tape:# [b, 28, 28] => [b, 784]x = tf.reshape(x, (-1, 28 * 28))# Step1. compute output# [b, 784] => [b, 10]out = model(x)# Step2. compute lossloss = tf.reduce_sum(tf.square(out - y)) / x.shape[0]# Step3. optimize and update w1, w2, w3, b1, b2, b3grads = tape.gradient(loss, model.trainable_variables)# w' = w - lr * gradoptimizer.apply_gradients(zip(grads, model.trainable_variables))if step % 100 == 0:print(epoch, step, 'loss:', loss.numpy())model.save("1.h5")# 在训练完成后，请利用已经得到的model验证在测试集上的结果，请仿照上面training_epoch的在下面写出你的具体测试代码# 并输出测试结果（预测值，groud truth），注意只是测试不需要计算loss和计算梯度def test():pass# test = tf.reshape(x_val, (10000, 28 * 28))# 这里是将一组图像矩阵x重建为新的矩阵，该新矩阵的维数为（10000，28，28，1）# out = model(test)# np.set_printoptions(threshold=1000)# 控制输出的值的个数为10001# print(np.argmax(out, axis=1))# image_raw_data_jpg = tf.io.gfile.GFile('1.png').read()# with tf.Session() as sess:# img_data_jpg = tf.image.decode_jpeg(image_raw_data_jpg)# 图像解码# img_data_jpg = tf.image.convert_image_dtype(img_data_jpg, dtype=tf.uint8)# 改变图像数据的类型def model_use():IM = img.open(r"2.png")IM = IM.convert("L")im = np.array(IM)im = tf.convert_to_tensor(im, dtype=tf.float32) / 255im = tf.reshape(im, (-1, 28 * 28))result = model(im)result = np.argmax(result)print("Result:", result)# print("Result:6")def train():for epoch in range(30):train_epoch(epoch)# 30个epoch之后调用test_epoch,# test()model_use()# 如果你学有余力，请利用我们给的hand writing digit image测试用例，试一试你训练的model（选做）# 注意你需要利用Python先读入test.jpg图像，然后把它转换为灰度图像，然后将图像由28*28转化为784长度的向量，# 然后送入模型，最后输出判别结果。if __name__ == '__main__':train()# use()

这个识别正确率不高，简单的几个还行，5和8一直都很难识别