Google Colab 深度学习教程列表

Google Colab是一种以云计算为基础，可供程序员使用的免费Jupyter环境，可以运行深度学习模型，并且不需要安装任何软件或设置昂贵的硬件。本教程列表将列举一些适合初学者或经验丰富的程序员使用Google Colab进行深度学习的教程。

1. 使用Google Colab进行MNIST分类

该教程通过使用Google Colab, TensorFlow和Keras，介绍如何使用卷积神经网络来对手写数字进行分类。

# 代码片段

# 引入数据集
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D

# 加载数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 转换数据
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# 定义模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

# 编译和训练模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=128, verbose=1, validation_data=(x_test, y_test))

2. 使用Google Colab进行图像风格转换

该教程介绍了如何使用TensorFlow和Google Colab，使用深度学习技术来将一张图片的风格转换成另一张图片的风格，同时，还可以在线可视化风格转换的结果。

# 代码片段

# 引入必要的库
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

# 下载模型
!wget https://github.com/lengstrom/fast-style-transfer/raw/master/examples/style/seated-nude.jpg
!wget https://github.com/lengstrom/fast-style-transfer/raw/master/examples/style/starry-night.jpg
!wget https://github.com/lengstrom/fast-style-transfer/raw/master/examples/style/wave.jpg
!wget https://github.com/lengstrom/fast-style-transfer/raw/master/examples/content/chicago.jpg

# 设置参数
STYLE_IMAGE_PATH = 'starry-night.jpg'
CONTENT_IMAGE_PATH = 'chicago.jpg'

STYLE_WEIGHT = 1e-2
CONTENT_WEIGHT = 1e4

# 加载图像
def load_img(path_to_img):
  img = tf.io.read_file(path_to_img)
  img = tf.image.decode_image(img, channels=3)
  img = tf.image.convert_image_dtype(img, tf.float32)
  img = img[tf.newaxis, :]
  return img

# 显示图像
def imshow(image, title=None):
  if len(image.shape) > 3:
    image = tf.squeeze(image, axis=0)

  plt.imshow(image)
  if title:
    plt.title(title)

# 加载图像
style_image = load_img(STYLE_IMAGE_PATH)
content_image = load_img(CONTENT_IMAGE_PATH)

# 创建模型
vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')

# 定义损失函数
def get_content_loss(base_content, target):
    return tf.reduce_mean(tf.square(base_content - target))

def gram_matrix(input_tensor):
    channels = int(input_tensor.shape[-1])
    a = tf.reshape(input_tensor, [-1, channels])
    n = tf.shape(a)[0]
    gram = tf.matmul(a, a, transpose_a=True)
    return gram / tf.cast(n, tf.float32)

def get_style_loss(base_style, gram_target):
    gram_style = gram_matrix(base_style)

    return tf.reduce_mean(tf.square(gram_style - gram_target))

def calculate_loss(model, base_input, style_targets, content_targets, STYLE_WEIGHT=1e-2, CONTENT_WEIGHT=1e4):
    style_features = model(style_targets)['block5_conv2']
    base_style = style_features[0, :, :, :]
    gram_style_target = gram_matrix(gram_matrix(style_features))

    content_features = model(content_targets)['block5_conv2']
    base_content = content_features[0, :, :, :]

    style_loss = tf.add_n([get_style_loss(base_style, gram_style_target) for gram_style_target in gram_style_target])
    style_loss *= STYLE_WEIGHT / len(style_targets)

    content_loss = tf.add_n([get_content_loss(base_content, base_input_target) for base_input_target in base_input])
    content_loss *= CONTENT_WEIGHT / len(content_targets)

    loss = style_loss + content_loss

    return loss

# 开始风格转换
def run_style_transfer(content_path, style_path, num_iterations=100, content_weight=1e4, style_weight=1e-2):
  # 加载图像
  content_image = load_img(content_path)
  style_image = load_img(style_path)

  # 创建模型
  vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')

  # 激活模型
  content_layers = ['block5_conv2']

  style_layers = ['block1_conv1',
                  'block2_conv1',
                  'block3_conv1',
                  'block4_conv1',
                  'block5_conv1']

  num_content_layers = len(content_layers)
  num_style_layers = len(style_layers)

  # 定义优化器
  opt = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)

  # 定义变量
  style_targets = vgg(style_image)['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']
  content_targets = vgg(content_image)['block5_conv2']
  base_input = content_image
  base_input = tf.Variable(base_input)

  # 记录中间结果
  style_weights = {'block1_conv1': 1.0,
                    'block2_conv1': 0.8,
                    'block3_conv1': 0.5,
                    'block4_conv1': 0.3,
                    'block5_conv1': 0.1}

  loss_history = []

  # 重复迭代
  for i in range(num_iterations):
    # 计算损失函数
    loss = calculate_loss(vgg, base_input, style_targets, content_targets, style_weight, content_weight)

    # 计算梯度
    grads, _ = tf.GradientTape().gradient(loss, [base_input])

    # 调整输入图像的值
    opt.apply_gradients([(grads, base_input)])

    # 记录历史记录
    loss_history.append(loss.numpy())

    # 打印中间信息
    if i % 10 == 0:
        print("Iteration %d: Loss=%.2f" % (i, loss))

  # 显示结果
  plt.figure(figsize=(15, 15))

  display_list = [content_path, style_path]

  for i in range(len(display_list)):
    plt.subplot(1, len(display_list), i+1)
    imshow(load_img(display_list[i]))
    plt.axis('off')

  plt.show()

  plt.figure(figsize=(10, 10))

  for i in range(num_iterations):
      plt.plot(loss_history[i], 'b')
      plt.xlabel('Iteration')
      plt.ylabel('Loss')
      plt.title('Style Transfer Loss')

  plt.show()

  imshow(base_input, 'Output Image')

run_style_transfer(CONTENT_IMAGE_PATH, STYLE_IMAGE_PATH)

3. 使用Google Colab进行情感分析

该教程介绍如何使用Google Colab，Python和Keras，使用深度学习技术来进行电影评论的情感分析。

# 代码片段

# 引入必要的库
import tensorflow_datasets as tfds
import tensorflow as tf

# 下载IMDB数据集
train_data, test_data = tfds.load(name="imdb_reviews", split=["train", "test"], batch_size=-1, as_supervised=True)

# 转换数据
train_examples, train_labels = tfds.as_numpy(train_data)
test_examples, test_labels = tfds.as_numpy(test_data)

# 构建模型
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(10000, 16, input_length=100),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# 编译和训练模型
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

history = model.fit(train_examples, train_labels, epochs=20, validation_split=0.2, verbose=1)

# 测试模型
test_loss, test_acc = model.evaluate(test_examples, test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

结论

以上是一些适合初学者和有经验的程序员使用Google Colab进行深度学习的教程。Google Colab提供了免费的云端GPU，并且提供了强大的工具，帮助开发者更加高效地开发和测试深度学习模型。