modelo.compile(optimizer=tf.keras.optimizers.Adam (1e4), loss='sparse_categorical_crossentropy' ,
By GEMINI
This line is used to configure your TensorFlow/Keras model for training. Here's a breakdown of what each part does:
1. modelo.compile(...): This function is used to specify the training configuration of your
model. It takes several arguments that define how the model should be trained.
2. optimizer=tf.keras.optimizers.Adam (1e-4): This specifies the optimization algorithm
to use during training.
o tf.keras.optimisers.Adam: This refers to the Adam optimizer, a popular algorithm
for training deep learning models. It's known for its efficiency and effectiveness.
o (1e-4): This sets the learning rate for the Adam optimizer. The learning rate controls
how much the model's weights are adjusted during each training step. A lower learning
rate (like 1e-4, which is 0.0001) means the model learns more slowly but can potentially
find better solutions.
3. loss='sparse_categorical_crossentropy' : This defines the loss function that the
model will try to minimize during training.
o sparse_categorical_crossentropy: This loss function is typically used for multi-
class classification problems where the target labels are integers representing the
different classes (e.g., 0 for class A, 1 for class B, etc.). It's appropriate when your labels
are not one-hot encoded.
4. metrics=['accuracy']: This specifies the metrics that will be used to evaluate the
performance of your model during and after training.
o ['accuracy']: This indicates that you want to track the accuracy of the model, which
is the percentage of correctly classified examples.
In summary:
This code is configuring your model to be trained using the Adam optimizer with a learning rate of 0.0001, aiming to minimize the sparse categorical cross-entropy loss, and tracking the accuracy metric during training. This setup is common for multi-class classification tasks.
Sources 1.keras.io/api/metrics/ 2.www.tensorflow.org/guide/keras/training_with_built_in_methods