progres bara

import tensorflow as tf
import numpy as np
from tensorflow import keras
from tqdm import tqdm

# Sample data
X = np.random.rand(300, 20)
y = np.random.randint(0, 2, size=(300,))

# Custom AdamW optimizer configuration
learning_rate = 0.001
weight_decay = 0.004

# Build a simple neural network
model = keras.Sequential([
    keras.layers.Dense(32, activation='relu', input_shape=(20,)),
    keras.layers.Dense(1, activation='sigmoid')
])

# Create an Adam optimizer
adam_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

# Apply weight decay to kernel (weight) variables
for var in model.trainable_variables:
    if 'kernel' in var.name:
        adam_optimizer.apply_gradients([(-learning_rate * weight_decay * var, var)])

# Compile the model using the custom AdamW optimizer with weight decay
model.compile(optimizer=adam_optimizer, loss='binary_crossentropy', metrics=['accuracy'])

# Define the number of epochs
total_epochs = 1000

# Train the model with a custom progress bar
for epoch in tqdm(range(total_epochs), unit="epoch"):
    if (epoch + 1) % 100 == 0:
        verbose = 1
        tqdm.write(f"Epoch {epoch + 1}/{total_epochs}")
    else:
        verbose = 0
    model.fit(X, y, epochs=1, batch_size=32, verbose=verbose)