Datasets:
HenryAI
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KerasCodeExamples.txt

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4.99k
1563/1563 [==============================] - 37s 24ms/step - loss: 1.2987 - accuracy: 0.7099 - val_loss: 0.8409 - val_accuracy: 0.7766
Epoch 15/15
1563/1563 [==============================] - 37s 24ms/step - loss: 1.2953 - accuracy: 0.7099 - val_loss: 0.7850 - val_accuracy: 0.8014
313/313 [==============================] - 3s 9ms/step - loss: 0.7850 - accuracy: 0.8014
Test accuracy: 80.14%
Train the model using the original non-augmented dataset
model = training_model()
model.load_weights(\"initial_weights.h5\")
model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])
model.fit(train_ds_simple, validation_data=test_ds, epochs=15)
test_loss, test_accuracy = model.evaluate(test_ds)
print(\"Test accuracy: {:.2f}%\".format(test_accuracy * 100))
Epoch 1/15
1563/1563 [==============================] - 38s 23ms/step - loss: 1.4864 - accuracy: 0.5173 - val_loss: 1.3694 - val_accuracy: 0.5708
Epoch 2/15
1563/1563 [==============================] - 36s 23ms/step - loss: 1.0682 - accuracy: 0.6779 - val_loss: 1.1424 - val_accuracy: 0.6686
Epoch 3/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.8955 - accuracy: 0.7449 - val_loss: 1.0555 - val_accuracy: 0.7007
Epoch 4/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.7890 - accuracy: 0.7878 - val_loss: 1.0575 - val_accuracy: 0.7079
Epoch 5/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.7107 - accuracy: 0.8175 - val_loss: 1.1395 - val_accuracy: 0.7062
Epoch 6/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.6524 - accuracy: 0.8397 - val_loss: 1.1716 - val_accuracy: 0.7042
Epoch 7/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.6098 - accuracy: 0.8594 - val_loss: 1.4120 - val_accuracy: 0.6786
Epoch 8/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.5715 - accuracy: 0.8765 - val_loss: 1.3159 - val_accuracy: 0.7011
Epoch 9/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.5477 - accuracy: 0.8872 - val_loss: 1.2873 - val_accuracy: 0.7182
Epoch 10/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.5233 - accuracy: 0.8988 - val_loss: 1.4118 - val_accuracy: 0.6964
Epoch 11/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.5165 - accuracy: 0.9045 - val_loss: 1.3741 - val_accuracy: 0.7230
Epoch 12/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.5008 - accuracy: 0.9124 - val_loss: 1.3984 - val_accuracy: 0.7181
Epoch 13/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.4896 - accuracy: 0.9190 - val_loss: 1.3642 - val_accuracy: 0.7209
Epoch 14/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.4845 - accuracy: 0.9231 - val_loss: 1.5469 - val_accuracy: 0.6992
Epoch 15/15
1563/1563 [==============================] - 36s 23ms/step - loss: 0.4749 - accuracy: 0.9294 - val_loss: 1.4034 - val_accuracy: 0.7362
313/313 [==============================] - 3s 9ms/step - loss: 1.4034 - accuracy: 0.7362
Test accuracy: 73.62%
Notes
In this example, we trained our model for 15 epochs. In our experiment, the model with CutMix achieves a better accuracy on the CIFAR-10 dataset (80.36% in our experiment) compared to the model that doesn't use the augmentation (72.70%). You may notice it takes less time to train the model with the CutMix augmentation.
You can experiment further with the CutMix technique by following the original paper.
Few-shot classification of the Omniglot dataset using Reptile.
Introduction
The Reptile algorithm was developed by OpenAI to perform model agnostic meta-learning. Specifically, this algorithm was designed to quickly learn to perform new tasks with minimal training (few-shot learning). The algorithm works by performing Stochastic Gradient Descent using the difference between weights trained on ...
import matplotlib.pyplot as plt
import numpy as np
import random
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_datasets as tfds
Define the Hyperparameters
learning_rate = 0.003
meta_step_size = 0.25
inner_batch_size = 25
eval_batch_size = 25
meta_iters = 2000
eval_iters = 5
inner_iters = 4
eval_interval = 1
train_shots = 20
shots = 5
classes = 5
Prepare the data
The Omniglot dataset is a dataset of 1,623 characters taken from 50 different alphabets, with 20 examples for each character. The 20 samples for each character were drawn online via Amazon's Mechanical Turk. For the few-shot learning task, k samples (or \"shots\") are drawn randomly from n randomly-chosen classes. Thes...
class Dataset:
# This class will facilitate the creation of a few-shot dataset
# from the Omniglot dataset that can be sampled from quickly while also
# allowing to create new labels at the same time.
def __init__(self, training):
# Download the tfrecord files containing the omniglot data and convert to a
# dataset.
split = \"train\" if training else \"test\"
ds = tfds.load(\"omniglot\", split=split, as_supervised=True, shuffle_files=False)
# Iterate over the dataset to get each individual image and its class,
# and put that data into a dictionary.
self.data = {}
def extraction(image, label):
# This function will shrink the Omniglot images to the desired size,
# scale pixel values and convert the RGB image to grayscale
image = tf.image.convert_image_dtype(image, tf.float32)
image = tf.image.rgb_to_grayscale(image)
image = tf.image.resize(image, [28, 28])
return image, label