Datasets:
HenryAI
/
KerasCodeExamples.txt

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Epoch 7/10
9/9 [==============================] - 6s 651ms/step - loss: 0.4413 - accuracy: 0.8072
Epoch 8/10
9/9 [==============================] - 6s 682ms/step - loss: 0.4542 - accuracy: 0.8014
Epoch 9/10
9/9 [==============================] - 6s 649ms/step - loss: 0.4235 - accuracy: 0.8053
Epoch 10/10
9/9 [==============================] - 6s 686ms/step - loss: 0.4445 - accuracy: 0.7936
Comparing performance
print(\"Not using Gradient Centralization\")
print(f\"Loss: {history_no_gc.history['loss'][-1]}\")
print(f\"Accuracy: {history_no_gc.history['accuracy'][-1]}\")
print(f\"Training Time: {sum(time_callback_no_gc.times)}\")
print(\"Using Gradient Centralization\")
print(f\"Loss: {history_gc.history['loss'][-1]}\")
print(f\"Accuracy: {history_gc.history['accuracy'][-1]}\")
print(f\"Training Time: {sum(time_callback_gc.times)}\")
Not using Gradient Centralization
Loss: 0.5814347863197327
Accuracy: 0.6932814121246338
Training Time: 136.35903406143188
Using Gradient Centralization
Loss: 0.4444807469844818
Accuracy: 0.7935734987258911
Training Time: 131.61780261993408
Readers are encouraged to try out Gradient Centralization on different datasets from different domains and experiment with it's effect. You are strongly advised to check out the original paper as well - the authors present several studies on Gradient Centralization showing how it can improve general performance, genera...
Many thanks to Ali Mustufa Shaikh for reviewing this implementation.
Training a handwriting recognition model with variable-length sequences.
Introduction
This example shows how the Captcha OCR example can be extended to the IAM Dataset, which has variable length ground-truth targets. Each sample in the dataset is an image of some handwritten text, and its corresponding target is the string present in the image. The IAM Dataset is widely used across many OCR benchmarks, ...
Data collection
!wget -q https://git.io/J0fjL -O IAM_Words.zip
!unzip -qq IAM_Words.zip
!
!mkdir data
!mkdir data/words
!tar -xf IAM_Words/words.tgz -C data/words
!mv IAM_Words/words.txt data
Preview how the dataset is organized. Lines prepended by \"#\" are just metadata information.
!head -20 data/words.txt
#--- words.txt ---------------------------------------------------------------#
#
# iam database word information
#
# format: a01-000u-00-00 ok 154 1 408 768 27 51 AT A
#
# a01-000u-00-00 -> word id for line 00 in form a01-000u
# ok -> result of word segmentation
# ok: word was correctly
# er: segmentation of word can be bad
#
# 154 -> graylevel to binarize the line containing this word
# 1 -> number of components for this word
# 408 768 27 51 -> bounding box around this word in x,y,w,h format
# AT -> the grammatical tag for this word, see the
# file tagset.txt for an explanation
# A -> the transcription for this word
#
a01-000u-00-00 ok 154 408 768 27 51 AT A
a01-000u-00-01 ok 154 507 766 213 48 NN MOVE
Imports
from tensorflow.keras.layers.experimental.preprocessing import StringLookup
from tensorflow import keras
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import os
np.random.seed(42)
tf.random.set_seed(42)
Dataset splitting
base_path = \"data\"
words_list = []
words = open(f\"{base_path}/words.txt\", \"r\").readlines()
for line in words:
if line[0] == \"#\":
continue
if line.split(\" \")[1] != \"err\": # We don't need to deal with errored entries.
words_list.append(line)
len(words_list)
np.random.shuffle(words_list)
We will split the dataset into three subsets with a 90:5:5 ratio (train:validation:test).
split_idx = int(0.9 * len(words_list))
train_samples = words_list[:split_idx]
test_samples = words_list[split_idx:]
val_split_idx = int(0.5 * len(test_samples))
validation_samples = test_samples[:val_split_idx]
test_samples = test_samples[val_split_idx:]