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HenryAI
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KerasBERTv1-Data

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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: