keras Sequential model¶

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

1.什么时候使用 Sequential 模型¶

A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.

A Sequential model is not appropriate when:

Your model has multiple inputs or multiple outputs

Any of your layers has multiple inputs or multiple outputs

You need to do layer sharing

You want non-linear topology (e.g. a residual connection, a multi-branch model)

2.构建一个 Sequential 模型¶

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential(name = "my_sequential")
model.add(layers.Dense(2, activation = "relu", name = "layer1"))
model.add(layers.Dense(3, activation = "relu", name = "layer2"))
model.add(layers.Dense(4, name = "layer3"))
print(model.layers)

# delete latest layer of the model
model.pop()
print(len(model.layers))

4.Sequential 模型的特征提取功能¶

5.Sequential 模型实现 Transfer images¶

Transfer learning consists of freezing the bottom layers in a model and only training the top layers. If you aren’t familiar with it, make sure to read our guide to transfer learning.

Here are two common transfer learning blueprint involving Sequential models.

First, let’s say that you have a Sequential model, and you want to freeze all layers except the last one. In this case, you would simply iterate over model. layers and set layer.trainable = False on each layer, except the last one. Like this:

# Sequential model
model = keras.Sequential()
model.add(keras.Input(shape = (784))
model.add(layers.Dense(32, activation = "relu"))
model.add(layers.Dense(32, activation = "relu"))
model.add(layers.Dense(32, activation = "relu"))
model.add(layers.Dense(10))

# Presumably you would want to first load pre-trained weights
model.load_weights(...)

# Freeze all layers except the last one
for layer in model.layers[:-1]:
    layer.trainable = False

# Recompile and train(this will only update the weights of the last layer).
model.compile(...)
model.fit(...)

Another common blueprint is to use a Sequential model to stack a pre-trained model and some freshly initialized classification layers. Like this:

# Load a convolutional base with pre-trained weights
base_model = keras.applications.Xception(
    weights = "imagenet",
    include_top = False,
    pooling = "avg"
)

# Freeze the base model
base_model.trainable = False