Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
4.99k
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
np.random.seed(42)
tf.random.set_seed(42)
Load the CIFAR-10 dataset
In this example, we will use the CIFAR-10 image classification dataset.
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
class_names = [
\"Airplane\",
\"Automobile\",
\"Bird\",
\"Cat\",
\"Deer\",
\"Dog\",
\"Frog\",
\"Horse\",
\"Ship\",
\"Truck\",
]
(50000, 32, 32, 3)
(50000, 10)
(10000, 32, 32, 3)
(10000, 10)
Define hyperparameters
AUTO = tf.data.AUTOTUNE
BATCH_SIZE = 32
IMG_SIZE = 32
Define the image preprocessing function
def preprocess_image(image, label):
image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
image = tf.image.convert_image_dtype(image, tf.float32) / 255.0
return image, label
Convert the data into TensorFlow Dataset objects
train_ds_one = (
tf.data.Dataset.from_tensor_slices((x_train, y_train))
.shuffle(1024)
.map(preprocess_image, num_parallel_calls=AUTO)
)
train_ds_two = (
tf.data.Dataset.from_tensor_slices((x_train, y_train))
.shuffle(1024)
.map(preprocess_image, num_parallel_calls=AUTO)
)
train_ds_simple = tf.data.Dataset.from_tensor_slices((x_train, y_train))
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))
train_ds_simple = (
train_ds_simple.map(preprocess_image, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
# Combine two shuffled datasets from the same training data.
train_ds = tf.data.Dataset.zip((train_ds_one, train_ds_two))
test_ds = (
test_ds.map(preprocess_image, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
Define the CutMix data augmentation function
The CutMix function takes two image and label pairs to perform the augmentation. It samples λ(l) from the Beta distribution and returns a bounding box from get_box function. We then crop the second image (image2) and pad this image in the final padded image at the same location.
def sample_beta_distribution(size, concentration_0=0.2, concentration_1=0.2):
gamma_1_sample = tf.random.gamma(shape=[size], alpha=concentration_1)
gamma_2_sample = tf.random.gamma(shape=[size], alpha=concentration_0)
return gamma_1_sample / (gamma_1_sample + gamma_2_sample)
@tf.function
def get_box(lambda_value):
cut_rat = tf.math.sqrt(1.0 - lambda_value)
cut_w = IMG_SIZE * cut_rat # rw
cut_w = tf.cast(cut_w, tf.int32)
cut_h = IMG_SIZE * cut_rat # rh
cut_h = tf.cast(cut_h, tf.int32)
cut_x = tf.random.uniform((1,), minval=0, maxval=IMG_SIZE, dtype=tf.int32) # rx
cut_y = tf.random.uniform((1,), minval=0, maxval=IMG_SIZE, dtype=tf.int32) # ry
boundaryx1 = tf.clip_by_value(cut_x[0] - cut_w // 2, 0, IMG_SIZE)
boundaryy1 = tf.clip_by_value(cut_y[0] - cut_h // 2, 0, IMG_SIZE)
bbx2 = tf.clip_by_value(cut_x[0] + cut_w // 2, 0, IMG_SIZE)
bby2 = tf.clip_by_value(cut_y[0] + cut_h // 2, 0, IMG_SIZE)