Neural separation

Put on your gloves because you're going to perform brain surgery!

Neurons learn by updating their weights to output values that help them better distinguish between the different output classes in your dataset. You will make use of the inp_to_out() function you just built to visualize the output of two neurons in the first layer of the Banknote Authentication model as it learns.

The model you built in chapter 2 is ready for you to use, just like X_test and y_test. Paste show_code(plot) in the console if you want to check plot().

You're performing heavy duty, once all is done, click through the graphs to watch the separation live!

Cet exercice fait partie du cours

Introduction to Deep Learning with Keras

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Instructions

Use the previously defined inp_to_out() function to get the outputs of the first layer when fed with X_test.
Use the model.evaluate() method to obtain the validation accuracy for the test dataset at each epoch.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

for i in range(0, 21):
  	# Train model for 1 epoch
    h = model.fit(X_train, y_train, batch_size = 16, epochs = 1, verbose = 0)
    if i%4==0: 
      # Get the output of the first layer
      layer_output = ____([____])[0]
      
      # Evaluate model accuracy for this epoch
      test_accuracy = model.____(____, ____)[1] 
      
      # Plot 1st vs 2nd neuron output
      plot()

Modifier et exécuter le code

Cet exercice fait partie du cours

Introduction to Deep Learning with Keras

IntermédiaireNiveau de compétence

4.8+

Commencer le cours gratuitement

In this first chapter, you will get introduced to neural networks, understand what kind of problems they can solve, and when to use them. You will also build several networks and save the earth by training a regression model that approximates the orbit of a meteor that is approaching us!

Exercise 1: What is Keras?Exercise 2: Describing Keras Exercise 3: Would you use deep learning?Exercise 4: Your first neural network Exercise 5: Hello nets!Exercise 6: Counting parameters Exercise 7: Build as shown!Exercise 8: Surviving a meteor strike Exercise 9: Specifying a model Exercise 10: Training Exercise 11: Predicting the orbit!

By the end of this chapter, you will know how to solve binary, multi-class, and multi-label problems with neural networks. All of this by solving problems like detecting fake dollar bills, deciding who threw which dart at a board, and building an intelligent system to water your farm. You will also be able to plot model training metrics and to stop training and save your models when they no longer improve.

Exercise 1: Binary classification Exercise 2: Exploring dollar bills Exercise 3: A binary classification model Exercise 4: Is this dollar bill fake ?Exercise 5: Multi-class classification Exercise 6: A multi-class model Exercise 7: Prepare your dataset Exercise 8: Training on dart throwers Exercise 9: Softmax predictions Exercise 10: Multi-label classification Exercise 11: An irrigation machine Exercise 12: Training with multiple labels Exercise 13: Keras callbacks Exercise 14: The history callback Exercise 15: Early stopping your model Exercise 16: A combination of callbacks

In the previous chapters, you've trained a lot of models! You will now learn how to interpret learning curves to understand your models as they train. You will also visualize the effects of activation functions, batch-sizes, and batch-normalization. Finally, you will learn how to perform automatic hyperparameter optimization to your Keras models using sklearn.

Exercise 1: Learning curves Exercise 2: Learning the digits Exercise 3: Is the model overfitting?Exercise 4: Do we need more data?Exercise 5: Activation functions Exercise 6: Different activation functions Exercise 7: Comparing activation functions Exercise 8: Comparing activation functions II Exercise 9: Batch size and batch normalization Exercise 10: Changing batch sizes Exercise 11: Batch normalizing a familiar model Exercise 12: Batch normalization effects Exercise 13: Hyperparameter tuning Exercise 14: Preparing a model for tuning Exercise 15: Tuning the model parameters Exercise 16: Training with cross-validation

It's time to get introduced to more advanced architectures! You will create an autoencoder to reconstruct noisy images, visualize convolutional neural network activations, use deep pre-trained models to classify images and learn more about recurrent neural networks and working with text as you build a network that predicts the next word in a sentence.

Exercise 1: Tensors, layers, and autoencoders Exercise 2: It's a flow of tensors Exercise 3: Neural separation

Exercice en cours

Exercise 4: Building an autoencoder Exercise 5: De-noising like an autoencoder Exercise 6: Intro to CNNs Exercise 7: Building a CNN model Exercise 8: Looking at convolutions Exercise 9: Preparing your input image Exercise 10: Using a real world model Exercise 11: Intro to LSTMs Exercise 12: Text prediction with LSTMs Exercise 13: Build your LSTM model Exercise 14: Decode your predictions Exercise 15: Test your model!Exercise 16: You're done!