HenryAI/KerasBERTv1-Data · Datasets at Fast360

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For batch 4, loss is 3.47.
The average loss for epoch 8 is 3.47 and mean absolute error is 1.47.
Epoch 00009: Learning rate is 0.0050.
For batch 0, loss is 3.39.
For batch 1, loss is 3.04.
For batch 2, loss is 3.10.
For batch 3, loss is 3.22.
For batch 4, loss is 3.14.
The average loss for epoch 9 is 3.14 and mean absolute error is 1.38.
Epoch 00010: Learning rate is 0.0050.
For batch 0, loss is 2.77.
For batch 1, loss is 2.89.
For batch 2, loss is 2.94.
For batch 3, loss is 2.85.
For batch 4, loss is 2.78.
The average loss for epoch 10 is 2.78 and mean absolute error is 1.30.
Epoch 00011: Learning rate is 0.0050.
For batch 0, loss is 3.69.
For batch 1, loss is 3.33.
For batch 2, loss is 3.22.
For batch 3, loss is 3.57.
For batch 4, loss is 3.79.
The average loss for epoch 11 is 3.79 and mean absolute error is 1.51.
Epoch 00012: Learning rate is 0.0010.
For batch 0, loss is 3.61.
For batch 1, loss is 3.21.
For batch 2, loss is 3.07.
For batch 3, loss is 3.34.
For batch 4, loss is 3.23.
The average loss for epoch 12 is 3.23 and mean absolute error is 1.42.
Epoch 00013: Learning rate is 0.0010.
For batch 0, loss is 2.03.
For batch 1, loss is 3.25.
For batch 2, loss is 3.23.
For batch 3, loss is 3.36.
For batch 4, loss is 3.44.
The average loss for epoch 13 is 3.44 and mean absolute error is 1.46.
Epoch 00014: Learning rate is 0.0010.
For batch 0, loss is 3.28.
For batch 1, loss is 3.14.
For batch 2, loss is 2.89.
For batch 3, loss is 2.94.
For batch 4, loss is 3.02.
The average loss for epoch 14 is 3.02 and mean absolute error is 1.38.

<tensorflow.python.keras.callbacks.History at 0x15d844410>
Built-in Keras callbacks
Be sure to check out the existing Keras callbacks by reading the API docs. Applications include logging to CSV, saving the model, visualizing metrics in TensorBoard, and a lot more!Transfer learning & fine-tuning
Author: fchollet
Date created: 2020/04/15
Last modified: 2020/05/12
Description: Complete guide to transfer learning & fine-tuning in Keras.

View in Colab • GitHub source

Setup
import numpy as np
import tensorflow as tf
from tensorflow import keras
Introduction
Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.

Transfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch.

The most common incarnation of transfer learning in the context of deep learning is the following worfklow:

Take layers from a previously trained model.
Freeze them, so as to avoid destroying any of the information they contain during future training rounds.
Add some new, trainable layers on top of the frozen layers. They will learn to turn the old features into predictions on a new dataset.
Train the new layers on your dataset.
A last, optional step, is fine-tuning, which consists of unfreezing the entire model you obtained above (or part of it), and re-training it on the new data with a very low learning rate. This can potentially achieve meaningful improvements, by incrementally adapting the pretrained features to the new data.

First, we will go over the Keras trainable API in detail, which underlies most transfer learning & fine-tuning workflows.

Then, we'll demonstrate the typical workflow by taking a model pretrained on the ImageNet dataset, and retraining it on the Kaggle "cats vs dogs" classification dataset.

This is adapted from Deep Learning with Python and the 2016 blog post "building powerful image classification models using very little data".

Freezing layers: understanding the trainable attribute
Layers & models have three weight attributes:

weights is the list of all weights variables of the layer.
trainable_weights is the list of those that are meant to be updated (via gradient descent) to minimize the loss during training.
non_trainable_weights is the list of those that aren't meant to be trained. Typically they are updated by the model during the forward pass.
Example: the Dense layer has 2 trainable weights (kernel & bias)

layer = keras.layers.Dense(3)
layer.build((None, 4)) # Create the weights

print("weights:", len(layer.weights))
print("trainable_weights:", len(layer.trainable_weights))
print("non_trainable_weights:", len(layer.non_trainable_weights))
weights: 2
trainable_weights: 2
non_trainable_weights: 0
In general, all weights are trainable weights. The only built-in layer that has non-trainable weights is the BatchNormalization layer. It uses non-trainable weights to keep track of the mean and variance of its inputs during training. To learn how to use non-trainable weights in your own custom layers, see the guide to writing new layers from scratch.

Example: the BatchNormalization layer has 2 trainable weights and 2 non-trainable weights

layer = keras.layers.BatchNormalization()

For batch 4, loss is 3.47.

The average loss for epoch 8 is 3.47 and mean absolute error is 1.47.

Epoch 00009: Learning rate is 0.0050.

For batch 0, loss is 3.39.

For batch 1, loss is 3.04.

For batch 2, loss is 3.10.

For batch 3, loss is 3.22.

For batch 4, loss is 3.14.

The average loss for epoch 9 is 3.14 and mean absolute error is 1.38.

Epoch 00010: Learning rate is 0.0050.

For batch 0, loss is 2.77.

For batch 1, loss is 2.89.

For batch 2, loss is 2.94.

For batch 3, loss is 2.85.

For batch 4, loss is 2.78.

The average loss for epoch 10 is 2.78 and mean absolute error is 1.30.

Epoch 00011: Learning rate is 0.0050.

For batch 0, loss is 3.69.

For batch 1, loss is 3.33.

For batch 2, loss is 3.22.

For batch 3, loss is 3.57.

For batch 4, loss is 3.79.

The average loss for epoch 11 is 3.79 and mean absolute error is 1.51.

Epoch 00012: Learning rate is 0.0010.

For batch 0, loss is 3.61.

For batch 1, loss is 3.21.

For batch 2, loss is 3.07.

For batch 3, loss is 3.34.

For batch 4, loss is 3.23.

The average loss for epoch 12 is 3.23 and mean absolute error is 1.42.

Epoch 00013: Learning rate is 0.0010.

For batch 0, loss is 2.03.

For batch 1, loss is 3.25.

For batch 2, loss is 3.23.

For batch 3, loss is 3.36.

For batch 4, loss is 3.44.

The average loss for epoch 13 is 3.44 and mean absolute error is 1.46.

Epoch 00014: Learning rate is 0.0010.

For batch 0, loss is 3.28.

For batch 1, loss is 3.14.

For batch 2, loss is 2.89.

For batch 3, loss is 2.94.

For batch 4, loss is 3.02.

The average loss for epoch 14 is 3.02 and mean absolute error is 1.38.

<tensorflow.python.keras.callbacks.History at 0x15d844410>

Built-in Keras callbacks

Be sure to check out the existing Keras callbacks by reading the API docs. Applications include logging to CSV, saving the model, visualizing metrics in TensorBoard, and a lot more!Transfer learning & fine-tuning

Author: fchollet

Date created: 2020/04/15

Last modified: 2020/05/12

Description: Complete guide to transfer learning & fine-tuning in Keras.

View in Colab • GitHub source

Setup

import numpy as np

import tensorflow as tf

from tensorflow import keras

Introduction

Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.

Transfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch.

The most common incarnation of transfer learning in the context of deep learning is the following worfklow:

Take layers from a previously trained model.

Freeze them, so as to avoid destroying any of the information they contain during future training rounds.

Add some new, trainable layers on top of the frozen layers. They will learn to turn the old features into predictions on a new dataset.

Train the new layers on your dataset.

A last, optional step, is fine-tuning, which consists of unfreezing the entire model you obtained above (or part of it), and re-training it on the new data with a very low learning rate. This can potentially achieve meaningful improvements, by incrementally adapting the pretrained features to the new data.

First, we will go over the Keras trainable API in detail, which underlies most transfer learning & fine-tuning workflows.

Then, we'll demonstrate the typical workflow by taking a model pretrained on the ImageNet dataset, and retraining it on the Kaggle "cats vs dogs" classification dataset.

This is adapted from Deep Learning with Python and the 2016 blog post "building powerful image classification models using very little data".

Freezing layers: understanding the trainable attribute

Layers & models have three weight attributes:

weights is the list of all weights variables of the layer.

trainable_weights is the list of those that are meant to be updated (via gradient descent) to minimize the loss during training.

non_trainable_weights is the list of those that aren't meant to be trained. Typically they are updated by the model during the forward pass.

Example: the Dense layer has 2 trainable weights (kernel & bias)

layer = keras.layers.Dense(3)

layer.build((None, 4)) # Create the weights

print("weights:", len(layer.weights))

print("trainable_weights:", len(layer.trainable_weights))

print("non_trainable_weights:", len(layer.non_trainable_weights))

weights: 2

trainable_weights: 2

non_trainable_weights: 0

In general, all weights are trainable weights. The only built-in layer that has non-trainable weights is the BatchNormalization layer. It uses non-trainable weights to keep track of the mean and variance of its inputs during training. To learn how to use non-trainable weights in your own custom layers, see the guide to writing new layers from scratch.

Example: the BatchNormalization layer has 2 trainable weights and 2 non-trainable weights

layer = keras.layers.BatchNormalization()