How do I train a model using TensorFlow?

How do I train a model using TensorFlow?

To train a model in TensorFlow, you first need to define your model architecture, compile it, and then fit it to your data.

What kind of data do I need for training?

You typically need labeled data for supervised learning. This means you should have input-output pairs.

Can you show me a simple code example?

Sure! Here's a basic example:

import tensorflow as tf

# Define a simple model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(input_shape,)),
    tf.keras.layers.Dense(1)
])

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Fit the model
model.fit(x_train, y_train, epochs=10)

What are epochs and how do I choose the right number?

Epochs refer to the number of times the learning algorithm will work through the entire training dataset. A common approach is to start with a small number and increase it while monitoring performance.