飞桨AI Studio作业答案，最全汇总！

飞桨AI Studio作为一款全面的AI开发平台，为AI爱好者们提供了丰富的学习资源和实践体验。然而，在学习的过程中，会遇到“无从下手”的困惑，其中最常见的问题便是作业难以完成。那么，面对这样的问题，如何做到拿到满分的作业答案呢？

以下是一些飞桨AI Studio作业的答案，希望能为你提供点帮助。

一、Python作业答案

1. 打印100以内的奇数（每行打印5个）

“`

for i in range(1, 101, 2):

print(i, end=’ ‘)

if i % 10 == 9:

print()

“`

2. 计算字符串中字母、数字、空格和其他字符的个数

“`

s = input(‘Enter a string: ‘)

letters, digits, spaces, others = 0, 0, 0, 0

for c in s:

if c.isalpha():

letters += 1

elif c.isdigit():

digits += 1

elif c.isspace():

spaces += 1

else:

others += 1

print(‘Letters:’, letters)

print(‘Digits:’, digits)

print(‘Spaces:’, spaces)

print(‘Others:’, others)

“`

二、机器学习作业答案

1. 利用飞桨完成手写数字识别（MNIST数据集）

“`

import paddle

import paddle.fluid as fluid

from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear

from paddle.fluid.dygraph.base import to_variable

from paddle.dataset.mnist import train, test

import numpy as np

class MNIST(fluid.dygraph.Layer):

def __init__(self):

super(MNIST, self).__init__()

self.conv1 = Conv2D(num_channels=1, num_filters=6, filter_size=5, stride=1, padding=2, act=’relu’)

self.pool1 = Pool2D(pool_size=2, pool_stride=2, pool_type=’max’)

self.conv2 = Conv2D(num_channels=6, num_filters=16, filter_size=5, stride=1, padding=0, act=’relu’)

self.pool2 = Pool2D(pool_size=2, pool_stride=2, pool_type=’max’)

self.fc1 = Linear(input_DIM=16*5*5, output_dim=120, act=’relu’)

self.fc2 = Linear(input_dim=120, output_dim=84, act=’relu’)

self.fc3 = Linear(input_dim=84, output_dim=10, act=’softmax’)

def forward(self, inputs):

x = self.conv1(inputs)

x = self.pool1(x)

x = self.conv2(x)

x = self.pool2(x)

x = fluid.layers.reshape(x, [x.shape[0], -1])

x = self.fc1(x)

x = self.fc2(x)

x = self.fc3(x)

return x

# 开启动态图模式

fluid.enable_dygraph()

# 加载训练数据和测试数据

X_train, y_train = train(), train(labels=True)

X_test, y_test = test(), test(labels=True)

# 定义模型、优化器和损失

model = MNIST()

optimizer = fluid.optimizer.Adam(learning_rate=0.001, parameter_list=model.parameters())

criterion = fluid.dygraph.CrossEntropyLoss()

# 开始训练

model.train()

for epoch in range(20):

for i, (x, y) in enumerate(zip(X_train, y_train)):

x = to_variable(x.reshape([1, 1, 28, 28]).astype(np.float32))

y = to_variable(y.reshape([1]).astype(np.int64))

logits = model(x)

loss = criterion(logits, y)

loss.backward()

optimizer.minimize(loss)

model.clear_gradients()

if (i + 1) % 1000 == 0:

print(‘Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}’.format(epoch+1, 20, i+1, len(X_train), loss.numpy()[0]))

# 开始测试

model.eval()

with fluid.dygraph.no_grad():

for i, (x, y) in enumerate(zip(X_test, y_test)):

x = to_variable(x.reshape([1, 1, 28, 28]).astype(np.float32))

y = np.array(y).reshape([1]).astype(np.int64)

logits = model(x)

pred = np.argmax(logits.numpy())

if pred == y:

acc = 1

else:

acc = 0

if (i + 1) % 1000 == 0:

print(‘Step [{}/{}], Accuracy: {:.2f}%’.format(i+1, len(X_test), acc * 100))

“`

2. 利用飞桨完成图像风格转换

“`

import paddle

import paddle.fluid as fluid

from PIL import Image

import numpy as np

def load_image(path):

img = Image.open(path)

img = img.resize((256, 256), Image.ANTIALIAS)

img = np.array(img).transpose((2, 0, 1)).astype(‘float32’)

img = img / 255.0

img = img * 2.0 – 1.0

return img.reshape([1, 3, 256, 256])

class Transformer(fluid.dygraph.Layer):

def __init__(self):

super(Transformer, self).__init__()

self.conv1 = fluid.dygraph.Conv2D(num_channels=3, num_filters=32, filter_size=9, stride=1, padding=4)

self.in1 = fluid.dygraph.BatchNorm(32)

self.conv2 = fluid.dygraph.Conv2D(num_channels=32, num_filters=64, filter_size=3, stride=2, padding=1)

self.in2 = fluid.dygraph.BatchNorm(64)

self.conv3 = fluid.dygraph.Conv2D(num_channels=64, num_filters=128, filter_size=3, stride=2, padding=1)

self.in3 = fluid.dygraph.BatchNorm(128)

self.res1 = fluid.dygraph.Conv2D(num_channels=128, num_filters=128, filter_size=3, stride=1, padding=1, act=’relu’)

self.res2 = fluid.dygraph.Conv2D(num_channels=128, num_filters=128, filter_size=3, stride=1, padding=1, act=’relu’)

self.res3 = fluid.dygraph.Conv2D(num_channels=128, num_filters=128, filter_size=3, stride=1, padding=1, act=’relu’)

self.res4 = fluid.dygraph.Conv2D(num_channels=128, num_filters=128, filter_size=3, stride=1, padding=1, act=’relu’)

self.res5 = fluid.dygraph.Conv2D(num_channels=128, num_filters=128, filter_size=3, stride=1, padding=1, act=’relu’)

self.deconv1 = fluid.dygraph.Conv2DTranspose(num_channels=128, num_filters=64, filter_size=3, stride=2, padding=1, output_padding=1)

self.in4 = fluid.dygraph.BatchNorm(64)

self.deconv2 = fluid.dygraph.Conv2DTranspose(num_channels=64, num_filters=32, filter_size=3, stride=2, padding=1, output_padding=1)

self.in5 = fluid.dygraph.BatchNorm(32)

self.deconv3 = fluid.dygraph.Conv2D(num_channels=32, num_filters=3, filter_size=9, stride=1, padding=4, act=’tanh’)

def forward(self, inputs):

x = self.conv1(inputs)

x = self.in1(x)

x = fluid.layers.relu(x)

x = self.conv2(x)

x = self.in2(x)

x = fluid.layers.relu(x)

x = self.conv3(x)

x = self.in3(x)

x = fluid.layers.relu(x)

x = self.res1(x)

x = self.res2(x)

x = self.res3(x)

x = self.res4(x)

x = self.res5(x)

x = self.deconv1(x)

x = self.in4(x)

x = fluid.layers.relu(x)

x = self.deconv2(x)

x = self.in5(x)

x = fluid.layers.relu(x)

x = self.deconv3(x)

return x

# 开启动态图模式

fluid.enable_dygraph()

# 加载预训练模型和待转换的图像

model = Transformer()

params, _ = fluid.dygraph.load_dygraph(‘models/transformer.pdparams’)

model.load_dict(params)

content = load_image(‘images/content.jpg’)

style = load_image(‘images/style.jpg’)

# 开始转换

model.eval()

with fluid.dygraph.no_grad():

content_var = fluid.dygraph.to_variable(content)

style_var = fluid.dygraph.to_variable(style)

output_var = model(content_var, style_var)

# 保存转换后的图像

output = output_var.numpy()[0].transpose((1, 2, 0))

output = (output + 1.0) / 2.0

output = (output * 255.0).CLIP(0, 255).astype(‘uint8’)

output_img = Image.fromarray(output)

output_img.save(‘images/output.jpg’)

“`

三、自然语言处理作业答案

1. 对文本进行分词和词性标注

“`

import paddle

import paddle.fluid as fluid

import paddle.fluid.dygraph as dygraph

import jieba.posseg as pseg

text = ‘今天天气不错，适合出去旅游。’

words = pseg.cut(text)

for word, flag in words:

print(‘{}\t{}’.format(word, flag))

“`

2. 利用飞桨完成情感分析任务

“`

import paddle

import paddle.fluid as fluid

import jieba

import numpy as np

class SentimentAnalysis(fluid.dygraph.Layer):

def __init__(self, vocab_size, embed_size, num_filters, filter_sizes, num_labels):

super(SentimentAnalysis, self).__init__()

self.embedding = fluid.dygraph.Embedding(size=[vocab_size, embed_size], dtype=’float32′)

self.conv_0 = fluid.dygraph.Conv2D(num_channels=1, num_filters=num_filters, filter_size=[filter_sizes[0], embed_size], stride=1, padding=0, act=’relu’)

self.conv_1 = fluid.dygraph.Conv2D(num_channels=1, num_filters=num_filters, filter_size=[filter_sizes[1], embed_size], stride=1, padding=0, act=’relu’)

self.conv_2 = fluid.dygraph.Conv2D(num_channels=1, num_filters=num_filters, filter_size=[filter_sizes[2], embed_size], stride=1, padding=0, act=’relu’)

self.fc = fluid.dygraph.Linear(input_dim=3*num_filters, output_dim=num_labels, act=’softmax’)

def forward(self, inputs):

x = self.embedding(inputs)

x = fluid.layers.reshape(x, shape=[-1, 1, x.shape[1], x.shape[2]])

x_0 = self.conv_0(x)

x_1 = self.conv_1(x)

x_2 = self.conv_2(x)

x_0 = fluid.layers.pool2d(x_0, pool_size=[x_0.shape[2], 1], pool_type=’max’, pool_stride=[1, 1])

x_1 = fluid.layers.pool2d(x_1, pool_size=[x_1.shape[2], 1], pool_type=’max’, pool_stride=[1, 1])

x_2 = fluid.layers.pool2d(x_2, pool_size=[x_2.shape[2], 1], pool_type=’max’, pool_stride=[1, 1])

x = fluid.layers.concat([x_0, x_1, x_2], axis=1)

x = fluid.layers.flatten(x, axis=1)

x = self.fc(x)

return x

# 开启动态图模式

fluid.enable_dygraph()

# 预处理数据

vocab = {}

with open(‘data/stopwords.txt’, ‘r’, encoding=’utf-8′) as f:

for line in f:

word = line.strip()

vocab[word] = 1

def process_lines(lines):

texts = []

labels = []

for line in lines:

line = line.strip().split(‘\t’)

label = int(line[0])

text = line[1]

words = [word for word in jieba.cut(text) if word not in vocab]

seq = [word_dict.get(word, unk_idx) for word in words]

if len(seq) > max_length:

seq = seq[:max_length]

else:

seq += [pad_idx] * (max_length – len(seq))

texts.append(seq)

labels.append(label)

return np.array(texts, dtype=’int64′), np.array(labels, dtype=’int64′)

word_dict = {}

with open(‘data/word_dict.txt’, ‘r’, encoding=’utf-8′) as f:

for line in f:

word, freq = line.strip().split(‘\t’)

if int(freq) < 5:

continue

word_dict[word] = len(word_dict)

train_data = []

with open(‘data/train.txt’, ‘r’, encoding=’utf-8′) as f:

for line in f:

train_data.append(line)

train_data = process_lines(train_data)

val_data = []

with open(‘data/val.txt’, ‘r’, encoding=’utf-8′) as f:

for line in f:

val_data.append(line)

val_data = process_lines(val_data)

test_data = []

with open(‘data/test.txt’, ‘r’, encoding=’utf-8′) as f:

for line in f:

test_data.append(line)

test_data = process_lines(test_data)

# 定义模型、优化器和损失

batch_size = 128

vocab_size = len(word_dict)

embed_size = 256

num_filters = 128

filter_sizes = [3, 4, 5]

num_labels = 2

max_length = 64

pad_idx = 0

unk_idx = 1

model = SentimentAnalysis(vocab_size, embed_size, num_filters, filter_sizes, num_labels)

optimizer = fluid.optimizer.Adam(learning_rate=0.001, parameter_list=model.parameters())

criterion = fluid.dygraph.CrossEntropyLoss()

# 开始训练

model.train()

for epoch in range(10):

np.random.shuffle(train_data)

total_loss, total_acc = 0.0, 0.0

total_steps = len(train_data) // batch_size + 1

for i in range(total_steps):

batch_data = train_data[i*batch_size:min((i+1)*batch_size, len(train_data))]

inputs, labels = batch_data

inputs = fluid.dygraph.to_variable(inputs)

labels = fluid.dygraph.to_variable(labels)

logits = model(inputs)

loss = criterion(logits, labels)

loss.backward()

optimizer.minimize(loss)

model.clear_gradients()

pred = np.argmax(logits.numpy(), axis=1)

acc = np.mean(pred == labels.numpy())

total_loss += loss.numpy()[0]

total_acc += acc

if (i + 1) % 100 == 0:

print(‘Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Accuracy: {:.2f}%’.format(epoch+1, 10, i+1, total_steps, total_loss/100, total_acc*100/100))

total_loss, total_acc = 0.0, 0.0

# 在验证集上测试模型效果

model.eval()

total_loss, total_acc = 0.0, 0.0

total_steps = len(val_data) // batch_size + 1

with fluid.dygraph.no_grad():

for i in range(total_steps):

batch_data = val_data[i*batch_size:min((i+1)*batch_size, len(val_data))]

inputs, labels = batch_data

inputs = fluid.dygraph.to_variable(inputs)

labels = fluid.dygraph.to_variable(labels)

logits = model(inputs)

loss = criterion(logits, labels)

pred = np.argmax(logits.numpy(), axis=1)

acc = np.mean(pred == labels.numpy())

total_loss += loss.numpy()[0]

total_acc += acc

print(‘Validation, Loss: {:.4f}, Accuracy: {:.2f}%’.format(total_loss/total_steps, total_acc*100/total_steps))

model.train()

# 在测试集上测试模型效果

model.eval()

total_loss, total_acc = 0.0, 0.0

total_steps = len(test_data) // batch_size + 1

with fluid.dygraph.no_grad():

for i in range(total_steps):

batch_data = test_data[i*batch_size:min((i+1)*batch_size, len(test_data))]

inputs, labels = batch_data

inputs = fluid.dygraph.to_variable(inputs)

labels = fluid.dygraph.to_variable(labels)

logits = model(inputs)

loss = criterion(logits, labels)

pred = np.argmax(logits.numpy(), axis=1)

acc = np.mean(pred == labels.numpy())

total_loss += loss.numpy()[0]

total_acc += acc

print(‘Test, Loss: {:.4f}, Accuracy: {:.2f}%’.format(total_loss/total_steps, total_acc*100/total_steps))

“`

四、CV作业答案

1. 利用飞桨完成物体检测任务

“`

import paddle.fluid as fluid

import CV2

import numpy as np