text
stringlengths 0
4.99k
|
---|
initial_model = keras.Sequential( |
[ |
keras.Input(shape=(250, 250, 3)), |
layers.Conv2D(32, 5, strides=2, activation="relu"), |
layers.Conv2D(32, 3, activation="relu", name="my_intermediate_layer"), |
layers.Conv2D(32, 3, activation="relu"), |
] |
) |
feature_extractor = keras.Model( |
inputs=initial_model.inputs, |
outputs=initial_model.get_layer(name="my_intermediate_layer").output, |
) |
# Call feature extractor on test input. |
x = tf.ones((1, 250, 250, 3)) |
features = feature_extractor(x) |
Transfer learning with a Sequential model |
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: |
model = keras.Sequential([ |
keras.Input(shape=(784)) |
layers.Dense(32, activation='relu'), |
layers.Dense(32, activation='relu'), |
layers.Dense(32, activation='relu'), |
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 |
# Use a Sequential model to add a trainable classifier on top |
model = keras.Sequential([ |
base_model, |
layers.Dense(1000), |
]) |
# Compile & train |
model.compile(...) |
model.fit(...) |
If you do transfer learning, you will probably find yourself frequently using these two patterns. |
That's about all you need to know about Sequential models! |
To find out more about building models in Keras, see: |
Guide to the Functional API |
Guide to making new Layers & Models via subclassingThe Functional API |
Author: fchollet |
Date created: 2019/03/01 |
Last modified: 2020/04/12 |
Description: Complete guide to the functional API. |
View in Colab - GitHub source |
Setup |
import numpy as np |
import tensorflow as tf |
from tensorflow import keras |
from tensorflow.keras import layers |
Introduction |
The Keras functional API is a way to create models that are more flexible than the tf.keras.Sequential API. The functional API can handle models with non-linear topology, shared layers, and even multiple inputs or outputs. |
The main idea is that a deep learning model is usually a directed acyclic graph (DAG) of layers. So the functional API is a way to build graphs of layers. |
Consider the following model: |
(input: 784-dimensional vectors) |
[Dense (64 units, relu activation)] |
[Dense (64 units, relu activation)] |
[Dense (10 units, softmax activation)] |
(output: logits of a probability distribution over 10 classes) |
This is a basic graph with three layers. To build this model using the functional API, start by creating an input node: |
inputs = keras.Input(shape=(784,)) |
The shape of the data is set as a 784-dimensional vector. The batch size is always omitted since only the shape of each sample is specified. |
If, for example, you have an image input with a shape of (32, 32, 3), you would use: |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.