In Keras, metrics are functions that are used to evaluate the performance of a neural network model. The "metrics" module in Keras provides several options for metrics, such as accuracy, precision, recall, and F1-score.
Here's an example of how to use the metrics module in Keras:
pythonfrom keras.models import Sequential from keras.layers import Dense from keras.metrics import binary_accuracy model = Sequential() model.add(Dense(64, input_dim=100, activation='relu')) model.add(Dense(32, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[binary_accuracy])
In the example above, we are creating a sequential model with three dense layers. The first layer has 64 units and is activated using the 'relu' activation function. The second layer has 32 units and is also activated using the 'relu' activation function. The third and final layer has one unit and is activated using the sigmoid function.
We have specified the binary cross-entropy loss function using the "loss" parameter in the "compile" method. The "optimizer" parameter specifies the optimizer that will be used to minimize the loss during training. Additionally, we have specified the binary accuracy metric using the "metrics" parameter in the "compile" method. This means that the binary accuracy will be computed and displayed during training and evaluation.
By default, Keras uses the accuracy metric for classification problems. However, you can choose to use any of the metrics available in the "metrics" module for your specific problem.
Metrics help you to evaluate the performance of your neural network model. The goal is to maximize the selected metric, which means that the predicted output of the model should be as close as possible to the actual output.
Keywords: Keras metrics, evaluation metrics, accuracy, precision, recall, F1-score, neural network performance, how to use Keras metrics.
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