This is a record. This is the artificial intelligence class taught by Professor Ma Jiquan of Heilongjiang University. This teacher heard that scientific research is very good, but what he handed in was a bit vague. It was mainly concepts and no actual code. It was disappointing, but There was no final exam, so it was pretty good.
---------------------------------------------------------------------------------------------------------------------------------
The main content of the experiment is to convert a 28*28 graph into a value of [0-1, 784], with the gray value in front.
Build a network
loss value
This is the code given by the teacher
import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, optimizers, datasets
import numpy as np
os.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.loop
for 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 loss
loss = tf.reduce_sum(tf.square(out - y)) / x.shape[0]
# Step3. optimize and update w1, w2, w3, b1, b2, b3
grads = tape.gradient(loss, model.trainable_variables)
# w' = w - lr * grad
optimizer.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()After completing it yourself
import os
from tkinter import *
import tensorflow as tf
from tensorflow.python import keras
from tensorflow.keras import *
import numpy as np
# import matplotlib.pyplot as plt
from PIL import Image as img
os.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 digit
x = 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784-256
layers.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.loop
for 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 loss
loss = tf.reduce_sum(tf.square(out - y)) / x.shape[0]
# Step3. optimize and update w1, w2, w3, b1, b2, b3
grads = tape.gradient(loss, model.trainable_variables)
# w' = w - lr * grad
optimizer.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) / 255
im = 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()
The accuracy of this recognition is not high. The simple ones are OK, but 5 and 8 have always been difficult to recognize.