【深度学习】：从人工神经网络的基础原理到循环神经网络的先进技术，跨越智能算法的关键发展阶段及其未来趋势，探索技术进步与应用挑战

import tensorflow as tf
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt

# 加载MNIST数据集
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 数据预处理
x_train, x_test = x_train / 255.0, x_test / 255.0

# 可视化部分数据
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(x_train[i], cmap=plt.cm.binary)
    plt.xlabel(y_train[i])
plt.show()

# 构建ANN模型
ann_model = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dropout(0.2),
    layers.Dense(10, activation='softmax')
])

# 编译模型
ann_model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

# 训练模型
ann_model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))

# 评估模型
test_loss, test_acc = ann_model.evaluate(x_test, y_test, verbose=2)
print('\n测试准确率:', test_acc)

import tensorflow as tf
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt
import numpy as np

# 加载CIFAR-10数据集
cifar10 = tf.keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# 数据预处理
x_train = x_train.astype('float32') / 255.0  # 归一化到0-1
x_test = x_test.astype('float32') / 255.0

# 可视化部分数据
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(x_train[i])
    plt.xlabel(y_train[i])
plt.show()

# 构建CNN模型
cnn_model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# 编译模型
cnn_model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

# 训练模型
cnn_model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# 评估模型
test_loss, test_acc = cnn_model.evaluate(x_test, y_test, verbose=2)
print('\n测试准确率:', test_acc)

# 可视化训练过程
plt.figure(figsize=(12,5))

plt.subplot(1,2,1)
plt.plot(cnn_model.history.history['accuracy'], label='训练准确率')
plt.plot(cnn_model.history.history['val_accuracy'], label='验证准确率')
plt.xlabel('Epoch')
plt.ylabel('准确率')
plt.legend()

plt.subplot(1,2,2)
plt.plot(cnn_model.history.history['loss'], label='训练损失')
plt.plot(cnn_model.history.history['val_loss'], label='验证损失')
plt.xlabel('Epoch')
plt.ylabel('损失')
plt.legend()

plt.show()

import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import matplotlib.patches as patches

# 加载预训练模型
model = hub.load("https://tfhub.dev/tensorflow/faster_rcnn/openimages_v4/inception_resnet_v2/1")

# 加载图像
image_path = 'path_to_image.jpg'
image = Image.open(image_path).convert('RGB')
plt.imshow(image)
plt.axis('off')
plt.show()

# 预处理图像
image_resized = np.array(image.resize((224, 224)))
image_input = tf.expand_dims(image_resized, axis=0)

# 进行目标检测
detections = model(image_input)

# 可视化结果
plt.figure(figsize=(10,10))
plt.imshow(image)
ax = plt.gca()

for detection in detections[0]:
    # 类别ID
    class_id = int(detection['detection_class_id'])
    # 置信度
    score = detection['detection_score']
    # 边界框
    box = detection['detection_boxes']
    # 绘制边界框
    if score > 0.5:
        x, y, width, height = box[1], box[0], box[3] - box[1], box[2] - box[0]
        rect = patches.Rectangle((x, y), width, height, linewidth=2, edgecolor='red', facecolor='none')
        ax.add_patch(rect)
        plt.text(x, y, f'{class_id}: {score}', fontsize=10, color='red')

plt.axis('off')
plt.show()

from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.datasets import imdb

# 加载IMDB数据集
vocab_size = 10000
max_length = 500
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=vocab_size)

# 数据预处理
x_train = pad_sequences(x_train, maxlen=max_length, padding='post')
x_test = pad_sequences(x_test, maxlen=max_length, padding='post')

# 构建RNN模型
rnn_model = models.Sequential([
    layers.Embedding(vocab_size, 128, input_length=max_length),
    layers.LSTM(64),
    layers.Dense(1, activation='sigmoid')
])

# 编译模型
rnn_model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])

# 训练模型
rnn_model.fit(x_train, y_train, epochs=3, validation_data=(x_test, y_test))

# 评估模型
test_loss, test_acc = rnn_model.evaluate(x_test, y_test, verbose=2)
print('\n测试准确率:', test_acc)