MyWeight | body mass tracker focused on make easy | iOS library

import tensorflow as tf

RANDOM_SEED_CONSTANT = 42  # FOR_REPRODUCIBILITY
tf.random.set_seed(RANDOM_SEED_CONSTANT)

# Prevent NHWC errors https://www.nuomiphp.com/eplan/en/50125.html
from tensorflow.keras import backend as K
K.set_image_data_format("channels_last")

from tensorflow import keras
from tensorflow.keras import datasets, layers, models

(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
train_images, test_images = train_images / 255.0, test_images / 255.0 # Normalize pixel values to be between 0 and 1

# Create a simple CNN
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, 
                       activation='relu', 
                       kernel_initializer=tf.keras.initializers.HeNormal(seed=RANDOM_SEED_CONSTANT)))
model.add(layers.Dense(10, 
                       kernel_initializer=tf.keras.initializers.HeNormal(seed=RANDOM_SEED_CONSTANT)))

print(model.summary())

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.save_weights('myweights.h5')

# Run1
history = model.fit(train_images, train_labels, epochs=1, 
                    shuffle=False,
                    validation_data=(test_images, test_labels))

# Run2
model.load_weights('myweights.h5')
history = model.fit(train_images, train_labels, epochs=1, 
                    shuffle=False,
                    validation_data=(test_images, test_labels))

# Run3
model.load_weights('myweights.h5')
history = model.fit(train_images, train_labels, epochs=1, 
                    shuffle=False,
                    validation_data=(test_images, test_labels))