python-kerasHow can I use Python, Keras, and TensorFlow together to build a machine learning model?

Using Python, Keras, and TensorFlow together to build a machine learning model is a straightforward process. First, you need to import the necessary libraries like import tensorflow as tf and import keras to use the functions they provide. Then, you need to define the model's architecture using Keras' Sequential class. Once the architecture is defined, you can compile the model using a suitable optimizer and loss function, such as tf.keras.optimizers.Adam(learning_rate=0.001) and tf.keras.losses.MeanSquaredError(). Finally, you can train the model using model.fit() and evaluate it using model.evaluate().

Example code

import tensorflow as tf
import keras

model = keras.Sequential([
    keras.layers.Dense(units=64, activation='relu', input_shape=(32,)),
    keras.layers.Dense(units=1)
])

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
              loss=tf.keras.losses.MeanSquaredError())

model.fit(x_train, y_train, batch_size=32, epochs=100)

model.evaluate(x_test, y_test, batch_size=32)

Helpful links

• Keras Documentation
• TensorFlow Documentation
• Getting Started with TensorFlow and Keras

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