Keras

1.Keras 介绍

• Keras: The Python Deep Learning library

  • keras
  • tensorflow.keras

• 为什么要使用 Keras？

  • Keras 优先考虑开发人员
  • Keras 已在业界和研究界广泛使用
  • Keras 使得将模型转化为产品变得容易
  • Keras 支持多种后端引擎
  • Keras 具有强大的多 GPU 支持和分布式训练支持
  • Keras 开发得到了深度学习生态系统中主要公司的支持

2.Keras 入门

• Keras 核心数据结构:

  • tensorflow.keras.layers
  • tensorflow.keras.models

• Keras Model 类型:

  • Sequential model
  • Keras functional API
  • Scratch via subclassing

• Keras 模型实现:

  • 类 Scikit-Learn API 示例:

    from tensorflow import keras
    from tensorflow.keras import layers, models
    from tensorflow.keras.datasets import mnist
    
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0
    model = models.Sequential()
    model.add(layers.Dense(units = 64, activation = "relu"))
    model.add(layers.Dense(units = 10, activation = "softmax"))
    model.compile(
       loss = "categorical_crossentropy",
       optimizer = "sgd",
       metrics = ["accuracy"]
    )
    # model.compile(
    #     loss = keras.losses.categorical_crossentropy,
    #     optimizer = keras.optimizers.SGD(learning_rate = 0.01, momentum = 0.9, nesterov = True)
    # )
    model.fit(x_train, y_train, epochs = 5, batch_size = 32)
    loss_and_metrics = model.evaluate(x_test, y_test, batch_size = 128)
    classes = model.predict(x_test, batch_size = 128)
    

  • 低级循环训练示例:

    import tensorflow as tf
    
    # prepare an optimizer.
    optimizer = tf.keras.optimizers.Adam()
    # prepare a loss function.
    loss_fn = tf.keras.losses.kl_divergence
    
    # Iterate over the batches of a dataset.
    for inputs, targets in dataset:
       # Open a GradientTape
       with tf.GradientTape() as tape:
          # Forward pass.
          predictions = model(inputs)
          # Compute the loss value for this batch.
          loss_value = loss_fn(targets, predictions)
    
       # Get gradients of loss wrt the weights.
       gradients = tape.gradient(loss_value, model.trainable_weights)
       # Update the weights of the model
       optimizer.apply_gradients(zip(gradients, model.trainable_weights))