Comparing activation functions II

What you coded in the previous exercise has been executed to obtain theactivation_results variable, this time 100 epochs were used instead of 20. This way you will have more epochs to further compare how the training evolves per activation function.

For every h_callback of each activation function in activation_results:

The h_callback.history['val_loss'] has been extracted.
The h_callback.history['val_accuracy'] has been extracted.

Both are saved into two dictionaries: val_loss_per_function and val_acc_per_function.

Pandas is also loaded as pd for you to use. Let's plot some quick validation loss and accuracy charts!

Cet exercice fait partie du cours

Introduction to Deep Learning with Keras

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Instructions

Use pd.DataFrame()to create a new DataFrame from the val_loss_per_function dictionary.
Call plot() on the DataFrame.
Create another pandas DataFrame from val_acc_per_function.
Once again, plot the DataFrame.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Create a dataframe from val_loss_per_function
val_loss= ____.____(____)

# Call plot on the dataframe
____
plt.show()

# Create a dataframe from val_acc_per_function
val_acc = _____

# Call plot on the dataframe
____
plt.show()

Modifier et exécuter le code

Cet exercice fait partie du cours

Introduction to Deep Learning with Keras

IntermédiaireNiveau de compétence

4.8+

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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

Exercice en cours

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 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!