Project 30: Full Training Pipeline Capstone

ML Engineer

Current Task

Objective

Capstone: build, compile with an optimizer and a metric, train with an EarlyStopping callback, and save — the complete production workflow in one script.

Task: assemble all of it into one model, train it on random data, save it, and print the number of recorded epochs and the parameter count.

index.py

import tensorflow as tf
from tensorflow.keras import layers, Sequential
import numpy as np

model = Sequential([
    layers.Dense(16, activation='relu', input_shape=(5,)),
    layers.Dropout(0.2),
    layers.Dense(1)
])
model.compile(optimizer=tf.keras.optimizers.Adam(0.01), loss='mse', metrics=['mae'])

x = np.random.rand(50, 5)
y = np.random.rand(50, 1)

history = model.fit(x, y, epochs=5, verbose=0, callbacks=[tf.keras.callbacks.EarlyStopping(monitor='loss', patience=2)])
model.save('admin_dashboard_model.keras')

print(len(history.history['loss']))
print(model.count_params())

* Hint: Correct characters turn green, incorrect ones turn red.

Live Preview

🖼️

Verify your code to see the preview

← Previous