AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Keras sequential model9/8/2023 ![]() Note that the two models have the same architecture, but one of them uses a sigmoid activation in the first layer and the other uses a relu. Additionally, the input layers of the first and second models have been defined as m1_inputs and m2_inputs, respectively. Note that keras has been imported from tensorflow for you. summary() method to examine the joint model’s architecture. In this exercise, we will see how to do this. If, for instance, you want to train two models with different architectures jointly, you will need to use the functional API to do this. In some cases, the sequential API will not be sufficiently flexible to accommodate your desired model architecture and you will need to use the functional API instead. summary() method shows the layer type, output shape, and number of parameters of each layer. ![]() Great work! You’ve now defined and compiled a neural network using the keras sequential model. Note that keras has been imported from tensorflow for you and a sequential keras model has been defined as model. You will also use a method in keras to summarize your model’s architecture. Finally, you will compile the model to use the adam optimizer and the categorical_crossentropy loss. You will also apply dropout to prevent overfitting. There will be fewer layers, but more nodes. ![]() In this exercise, you will work towards classifying letters from the Sign Language MNIST dataset however, you will adopt a different network architecture than what you used in the previous exercise. summary() method allows us to view the model’s architecture. The compilation step in keras allows us to set the optimizer, loss function, and other useful training parameters in a single line of code. Excellent work! Notice that we’ve defined a model, but we haven’t compiled it. ![]()
0 Comments
Read More
Leave a Reply. |