Keras provides quite a few metrics as a module, metrics and they are as follows It is similar to loss function, but not used in training process. In machine learning, Metrics is used to evaluate the performance of your model. Import the optimizers module before using optimizers as specified below − (learning_rate = 0.01, momentum = 0.0, nesterov = False)
SGD − Stochastic gradient descent optimizer. Keras provides quite a few optimizer as a module, optimizers and they are as follows: In machine learning, Optimization is an important process which optimize the input weights by comparing the prediction and the loss function. Import the losses module before using loss function as specified below −
Y_pred − prediction with same shape as y_true Keras provides quite a few loss function in the losses module and they are as follows −Īll above loss function accepts two arguments − Keras requires loss function during model compilation process. In machine learning, Loss function is used to find error or deviation in the learning process. Let us learn few concepts required to better understand the compilation process. Once the compilation is done, we can move on to training phase.
The compilation is the final step in creating a model.
#KERAS SEQUENTIAL MODEL HOW TO#
This chapter explains about how to compile the model. Previously, we studied the basics of how to create model using Sequential and Functional API.
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