Text prediction with LSTMs

During the following exercises you will build a toy LSTM model that is able to predict the next word using a small text dataset. This dataset consist of cleaned quotes from the The Lord of the Ring movies. You can find them in the text variable.

You will turn this text into sequences of length 4 and make use of the Keras Tokenizer to prepare the features and labels for your model!

The Keras Tokenizer is already imported for you to use. It assigns a unique number to each unique word, and stores the mappings in a dictionary. This is important since the model deals with numbers but we later will want to decode the output numbers back into words.

This exercise is part of the course

Introduction to Deep Learning with Keras

Exercise instructions

Split the text into an array of words using .split().
Make sentences of 4 words each, moving one word at a time.
Instantiate a Tokenizer(), then fit it on the sentences with .fit_on_texts().
Turn sentences into a sequence of numbers calling .texts_to_sequences().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Split text into an array of words 
words = ____.____

# Make sentences of 4 words each, moving one word at a time
sentences = []
for i in range(4, len(words)):
  sentences.append(' '.join(words[i-____:i]))

# Instantiate a Tokenizer, then fit it on the sentences
tokenizer = ____
tokenizer.____(____)

# Turn sentences into a sequence of numbers
sequences = tokenizer.____(____)
print("Sentences: \n {} \n Sequences: \n {}".format(sentences[:5],sequences[:5]))

Edit and Run Code

This exercise is part of the course

Introduction to Deep Learning with Keras

IntermediateSkill Level

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 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

Current Exercise

Exercise 13: Build your LSTM model Exercise 14: Decode your predictions Exercise 15: Test your model!Exercise 16: You're done!