Chapter 5: Language Modeling
5.3 Recurrent Neural Networks (RNNs)
Recurrent Neural Networks, or RNNs, are a powerful type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or the spoken word. They are particularly effective for tasks where sequential data is involved, as they can use their internal state (memory) to process sequences of inputs.
In order to achieve this, RNNs are designed to perform the same task for every element of a sequence, with the output being dependent on the previous computations. This recurrence of operation gives them a kind of memory and allows them to build up a representation of the entire sequence, rather than just processing each input in isolation.
One of the key benefits of RNNs is their ability to handle variable-length sequences of data. This means that they can be used to process inputs of different lengths, which can be particularly useful in natural language processing tasks, where sentences can vary greatly in length.
Another advantage of RNNs is their ability to model temporal dependencies between elements in a sequence. This means that they can learn patterns in the data that are related to the order in which the inputs were presented, which can be useful in tasks such as speech recognition or music composition.
Overall, RNNs are a versatile and powerful type of neural network that have a wide range of applications across a variety of fields. Their ability to handle sequential data and model temporal dependencies make them well-suited to many real-world problems, and they are likely to play an increasingly important role in the development of artificial intelligence in the years to come.
5.3.1 RNN Architecture
Recurrent Neural Networks (RNNs) are a type of neural network architecture that are particularly adept at processing sequential data. The power behind RNNs lies in their ability to capture the hidden state of a sequence of inputs. This hidden state carries information about the history of the sequence, and is passed along from one time step to the next. By reusing these same weights at each time step, RNNs are able to process sequences of varying lengths, without requiring a fixed number of input or output layers.
RNNs are capable of performing a wide range of tasks, from language modeling to speech recognition and even image captioning. Due to their ability to keep track of sequential dependencies, RNNs are ideal for processing data that has a temporal component. For example, they can be used to generate music or predict stock prices based on historical trends.
The use of RNNs has revolutionized the field of deep learning, enabling researchers and practitioners to tackle complex problems that were previously considered unsolvable. With their ability to process sequential data and capture hidden dependencies, RNNs are a powerful tool for any data scientist or machine learning practitioner to have in their toolkit.
Example:
Here's a simple diagram of what the RNN architecture looks like:
_____
/ \
| h_t-1 |----->| h_t |----->| h_t+1 |
\_____/
/ | / | / |
x_t-1 x_t x_t+1Where h_t is the hidden state at time t, and x_t is the input at time t. The same function and set of parameters are used at every time step.
5.3.2 The Problem of Long-Term Dependencies
Recurrent Neural Networks (RNNs) are a type of neural network that are designed to handle sequential data. These networks have the ability to remember past inputs and use that information to influence the output at the current time step. However, despite their theoretical ability to handle "long-term dependencies", they often struggle to do so due to the "vanishing gradients" problem.
During training, the network adjusts its weights by backpropagating the error gradient from the output back into the network. However, when the sequences are long, the gradients become increasingly small, eventually vanishing altogether. This makes it difficult for the network to learn effectively, as it cannot effectively adjust its weights based on the input data.
To address this issue, various techniques have been developed to help RNNs learn to handle long-term dependencies. One such technique is the Long Short-Term Memory (LSTM) network, which uses a memory cell to store information over multiple time steps. Another technique is the Gated Recurrent Unit (GRU), which uses a gating mechanism to selectively update the memory cell.
While these techniques have been successful in improving the performance of RNNs, the "vanishing gradients" problem remains a significant challenge in the field of deep learning. As such, ongoing research is focused on developing new techniques to address this issue and improve the ability of RNNs to handle long-term dependencies.
5.3.3 LSTMs and GRUs
To overcome the vanishing gradient problem of a standard RNN, Long Short Term Memory cells (LSTMs) were introduced. LSTMs have a 'memory cell' that can keep information in memory for long periods of time. Essentially, it allows past information to be reinjected at a later time, thus helping the network to learn from important past information.
Gated Recurrent Units (GRUs) are a variation on LSTMs. The GRU unit controls the flow of information like the LSTM unit, but without having to use a memory unit. It just exposes the full hidden content without any control, but uses gating units to control the information flow, which makes it simpler than LSTMs as there are fewer parameters to train and thus, computationally more efficient.
One of the key advantages of LSTMs is that they can handle long-term dependencies. This means that the network can retain information over long periods of time, allowing it to make better predictions. However, the downside of LSTMs is that they require more computational resources to train, as they have a larger number of parameters.
On the other hand, GRUs are simpler than LSTMs, with fewer parameters to train. This makes them computationally more efficient, and thus more suitable for real-time applications. However, they may not perform as well as LSTMs when it comes to handling long-term dependencies.
Despite their differences, both LSTMs and GRUs are effective in dealing with the vanishing gradient problem. They have revolutionized the field of deep learning and have led to significant improvements in a range of applications, from speech recognition to natural language processing.
5.3.4 Building a Simple RNN in Python
Now that we've covered the basics of RNNs, let's delve deeper into the topic and build a more complex one in Python using the Keras library. Recurrent neural networks are a type of deep learning algorithm that can model sequential data, which makes them suitable for a wide range of applications, including natural language processing, speech recognition, and time series analysis. In order to build a more complex RNN, we'll need to consider the architecture, the number of layers, and the types of activation functions used in each layer.
For this exercise, we'll use the IMDB movie review dataset, which is a binary classification problem consisting of 50,000 movie reviews, half of which are positive and half are negative. The dataset has already been preprocessed, so we can focus on building the RNN. To begin, we'll first split the dataset into training and testing sets and then preprocess the text data by tokenizing it and converting the words into numerical vectors.
Once the data is preprocessed, we can begin building the RNN using Keras. We'll start by defining the architecture of the network, which will consist of an embedding layer, a recurrent layer, and a dense output layer. The embedding layer will be responsible for converting the numerical vectors into a dense representation that can be fed into the recurrent layer. The recurrent layer will use LSTM units to model the sequential nature of the data, and the dense output layer will produce a binary classification output.
In order to improve the performance of the RNN, we can experiment with different hyperparameters, such as the number of units in the recurrent layer, the learning rate, and the batch size. We can also use techniques such as dropout and early stopping to prevent overfitting and improve generalization.
Overall, building a more complex RNN using Keras is a challenging yet rewarding task that requires a deep understanding of the underlying principles and techniques. By following the steps outlined above, we can gain valuable experience in building and optimizing recurrent neural networks for a wide range of applications.
Examle:
Here's a basic example of how to create a simple RNN model using Keras:
from keras.models import Sequential
from keras.layers import Embedding, SimpleRNN, Dense
from keras.datasets import imdb
from keras.preprocessing import sequence
# Number of words to consider as features
max_features = 10000
# Cut texts after this number of words (among top max_features most common words)
maxlen = 500
# Load data
(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)
# Pad sequences
input_train = sequence.pad_sequences(input_train, maxlen=maxlen)
input_test = sequence.pad_sequences(input_test, maxlen=maxlen)
# Define model
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
# Train model
history = model.fit(input_train, y_train, epochs=10, batch_size=128, validation_split=0.2)In this code, we first load and preprocess the IMDB movie review dataset. We then define our model, which is a simple RNN with an embedding layer, a SimpleRNN layer, and a dense output layer. The model is then compiled with a binary crossentropy loss function (as this is a binary classification problem), and the RMSProp optimizer. Finally, we train our model using our training data.
Please note that training neural networks can take a considerable amount of time, especially when you're using larger datasets or more complex architectures. For more complicated tasks, LSTMs and GRUs might be a better choice due to their ability to capture long-term dependencies.
5.3.5 Practical Exercise: Text Generation with RNNs
A fun and educational exercise that can help solidify your understanding of RNNs is to use them for text generation. This involves training a network on a large volume of text data, then having it generate new text character by character or word by word.
For example, you could train a RNN on all of Shakespeare's works, then have it generate new "Shakespearean" text. Or train it on a bunch of recipes and have it come up with new recipes. The possibilities are endless! This exercise can help you understand the challenges and intricacies of working with sequence data and RNNs.
Example:
Let's take a look at a simple example of text generation using RNNs. In this case, we'll use a simple LSTM-based model and we'll use a sample dataset of Shakespeare's works for training.
import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.optimizers import Adam
# Load data
path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')
text = open(path_to_file, 'rb').read().decode(encoding='utf-8')
# Create a mapping from unique characters to indices
vocab = sorted(set(text))
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)
text_as_int = np.array([char2idx[c] for c in text])
# The maximum length sentence we want for a single input in characters
seq_length = 100
examples_per_epoch = len(text) // (seq_length+1)
# Create training examples / targets
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)
sequences = char_dataset.batch(seq_length+1, drop_remainder=True)
def split_input_target(chunk):
input_text = chunk[:-1]
target_text = chunk[1:]
return input_text, target_text
dataset = sequences.map(split_input_target)
# Batch size
BATCH_SIZE = 64
# Buffer size to shuffle the dataset
BUFFER_SIZE = 10000
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
# Building the model
vocab_size = len(vocab)
embedding_dim = 256
rnn_units = 1024
def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim,
batch_input_shape=[batch_size, None]),
tf.keras.layers.GRU(rnn_units,
return_sequences=True,
stateful=True,
recurrent_initializer='glorot_uniform'),
tf.keras.layers.Dense(vocab_size)
])
return model
model = build_model(
vocab_size=len(vocab),
embedding_dim=embedding_dim,
rnn_units=rnn_units,
batch_size=BATCH_SIZE)
# Training the model
def loss(labels, logits):
return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)
model.compile(optimizer='adam', loss=loss)
# Directory where the checkpoints will be saved
checkpoint_dir = './training_checkpoints'
# Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_prefix,
save_weights_only=True)
history = model.fit(dataset, epochs=10, callbacks=[checkpoint_callback])This code first loads a dataset of Shakespeare's writings, which is then processed and tokenized. We then define an LSTM-based model, which includes an embedding layer, an LSTM layer, and a dense output layer. The model is then compiled and trained on our dataset.
To generate text after training, we'll use a batch size of 1. Because of the way the RNN state is passed from timestep to timestep, the model only accepts a fixed batch size once built. To run the model with a different batch_size, we'll need to rebuild the model and restore the weights from the checkpoint:
model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)
model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))
model.buildAfter we rebuild the model and restore the weights from the checkpoint, we can then use the model to generate text:
model.build(tf.TensorShape([1, None]))
def generate_text(model, start_string):
# Evaluation step (generating text using the learned model)
# Number of characters to generate
num_generate = 1000
# Converting our start string to numbers (vectorizing)
input_eval = [char2idx[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0)
# Empty string to store our results
text_generated = []
# Low temperature results in more predictable text.
# Higher temperature results in more surprising text.
temperature = 1.0
# Here batch size == 1
model.reset_states()
for i in range(num_generate):
predictions = model(input_eval)
# remove the batch dimension
predictions = tf.squeeze(predictions, 0)
# using a categorical distribution to predict the character returned by the model
predictions = predictions / temperature
predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
# Pass the predicted character as the next input to the model
# along with the previous hidden state
input_eval = tf.expand_dims([predicted_id], 0)
text_generated.append(idx2char[predicted_id])
return (start_string + ''.join(text_generated))
print(generate_text(model, start_string=u"ROMEO: "))This code defines a generate_text function that takes in a model and a start string as input. The function then uses the model to generate a specified number of characters of text, using the start string as a seed. The generated text is then printed out.
In this case, the start string is "ROMEO: ", so the generated text will be a continuation of this string in the style of Shakespeare's writing. It's important to note that the quality of the generated text will depend on the amount of training the model has received. With only a few epochs of training, the generated text might not make much sense, but with more training, the model should be able to generate more coherent and interesting text.
5.3 Recurrent Neural Networks (RNNs)
Recurrent Neural Networks, or RNNs, are a powerful type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or the spoken word. They are particularly effective for tasks where sequential data is involved, as they can use their internal state (memory) to process sequences of inputs.
In order to achieve this, RNNs are designed to perform the same task for every element of a sequence, with the output being dependent on the previous computations. This recurrence of operation gives them a kind of memory and allows them to build up a representation of the entire sequence, rather than just processing each input in isolation.
One of the key benefits of RNNs is their ability to handle variable-length sequences of data. This means that they can be used to process inputs of different lengths, which can be particularly useful in natural language processing tasks, where sentences can vary greatly in length.
Another advantage of RNNs is their ability to model temporal dependencies between elements in a sequence. This means that they can learn patterns in the data that are related to the order in which the inputs were presented, which can be useful in tasks such as speech recognition or music composition.
Overall, RNNs are a versatile and powerful type of neural network that have a wide range of applications across a variety of fields. Their ability to handle sequential data and model temporal dependencies make them well-suited to many real-world problems, and they are likely to play an increasingly important role in the development of artificial intelligence in the years to come.
5.3.1 RNN Architecture
Recurrent Neural Networks (RNNs) are a type of neural network architecture that are particularly adept at processing sequential data. The power behind RNNs lies in their ability to capture the hidden state of a sequence of inputs. This hidden state carries information about the history of the sequence, and is passed along from one time step to the next. By reusing these same weights at each time step, RNNs are able to process sequences of varying lengths, without requiring a fixed number of input or output layers.
RNNs are capable of performing a wide range of tasks, from language modeling to speech recognition and even image captioning. Due to their ability to keep track of sequential dependencies, RNNs are ideal for processing data that has a temporal component. For example, they can be used to generate music or predict stock prices based on historical trends.
The use of RNNs has revolutionized the field of deep learning, enabling researchers and practitioners to tackle complex problems that were previously considered unsolvable. With their ability to process sequential data and capture hidden dependencies, RNNs are a powerful tool for any data scientist or machine learning practitioner to have in their toolkit.
Example:
Here's a simple diagram of what the RNN architecture looks like:
_____
/ \
| h_t-1 |----->| h_t |----->| h_t+1 |
\_____/
/ | / | / |
x_t-1 x_t x_t+1Where h_t is the hidden state at time t, and x_t is the input at time t. The same function and set of parameters are used at every time step.
5.3.2 The Problem of Long-Term Dependencies
Recurrent Neural Networks (RNNs) are a type of neural network that are designed to handle sequential data. These networks have the ability to remember past inputs and use that information to influence the output at the current time step. However, despite their theoretical ability to handle "long-term dependencies", they often struggle to do so due to the "vanishing gradients" problem.
During training, the network adjusts its weights by backpropagating the error gradient from the output back into the network. However, when the sequences are long, the gradients become increasingly small, eventually vanishing altogether. This makes it difficult for the network to learn effectively, as it cannot effectively adjust its weights based on the input data.
To address this issue, various techniques have been developed to help RNNs learn to handle long-term dependencies. One such technique is the Long Short-Term Memory (LSTM) network, which uses a memory cell to store information over multiple time steps. Another technique is the Gated Recurrent Unit (GRU), which uses a gating mechanism to selectively update the memory cell.
While these techniques have been successful in improving the performance of RNNs, the "vanishing gradients" problem remains a significant challenge in the field of deep learning. As such, ongoing research is focused on developing new techniques to address this issue and improve the ability of RNNs to handle long-term dependencies.
5.3.3 LSTMs and GRUs
To overcome the vanishing gradient problem of a standard RNN, Long Short Term Memory cells (LSTMs) were introduced. LSTMs have a 'memory cell' that can keep information in memory for long periods of time. Essentially, it allows past information to be reinjected at a later time, thus helping the network to learn from important past information.
Gated Recurrent Units (GRUs) are a variation on LSTMs. The GRU unit controls the flow of information like the LSTM unit, but without having to use a memory unit. It just exposes the full hidden content without any control, but uses gating units to control the information flow, which makes it simpler than LSTMs as there are fewer parameters to train and thus, computationally more efficient.
One of the key advantages of LSTMs is that they can handle long-term dependencies. This means that the network can retain information over long periods of time, allowing it to make better predictions. However, the downside of LSTMs is that they require more computational resources to train, as they have a larger number of parameters.
On the other hand, GRUs are simpler than LSTMs, with fewer parameters to train. This makes them computationally more efficient, and thus more suitable for real-time applications. However, they may not perform as well as LSTMs when it comes to handling long-term dependencies.
Despite their differences, both LSTMs and GRUs are effective in dealing with the vanishing gradient problem. They have revolutionized the field of deep learning and have led to significant improvements in a range of applications, from speech recognition to natural language processing.
5.3.4 Building a Simple RNN in Python
Now that we've covered the basics of RNNs, let's delve deeper into the topic and build a more complex one in Python using the Keras library. Recurrent neural networks are a type of deep learning algorithm that can model sequential data, which makes them suitable for a wide range of applications, including natural language processing, speech recognition, and time series analysis. In order to build a more complex RNN, we'll need to consider the architecture, the number of layers, and the types of activation functions used in each layer.
For this exercise, we'll use the IMDB movie review dataset, which is a binary classification problem consisting of 50,000 movie reviews, half of which are positive and half are negative. The dataset has already been preprocessed, so we can focus on building the RNN. To begin, we'll first split the dataset into training and testing sets and then preprocess the text data by tokenizing it and converting the words into numerical vectors.
Once the data is preprocessed, we can begin building the RNN using Keras. We'll start by defining the architecture of the network, which will consist of an embedding layer, a recurrent layer, and a dense output layer. The embedding layer will be responsible for converting the numerical vectors into a dense representation that can be fed into the recurrent layer. The recurrent layer will use LSTM units to model the sequential nature of the data, and the dense output layer will produce a binary classification output.
In order to improve the performance of the RNN, we can experiment with different hyperparameters, such as the number of units in the recurrent layer, the learning rate, and the batch size. We can also use techniques such as dropout and early stopping to prevent overfitting and improve generalization.
Overall, building a more complex RNN using Keras is a challenging yet rewarding task that requires a deep understanding of the underlying principles and techniques. By following the steps outlined above, we can gain valuable experience in building and optimizing recurrent neural networks for a wide range of applications.
Examle:
Here's a basic example of how to create a simple RNN model using Keras:
from keras.models import Sequential
from keras.layers import Embedding, SimpleRNN, Dense
from keras.datasets import imdb
from keras.preprocessing import sequence
# Number of words to consider as features
max_features = 10000
# Cut texts after this number of words (among top max_features most common words)
maxlen = 500
# Load data
(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)
# Pad sequences
input_train = sequence.pad_sequences(input_train, maxlen=maxlen)
input_test = sequence.pad_sequences(input_test, maxlen=maxlen)
# Define model
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
# Train model
history = model.fit(input_train, y_train, epochs=10, batch_size=128, validation_split=0.2)In this code, we first load and preprocess the IMDB movie review dataset. We then define our model, which is a simple RNN with an embedding layer, a SimpleRNN layer, and a dense output layer. The model is then compiled with a binary crossentropy loss function (as this is a binary classification problem), and the RMSProp optimizer. Finally, we train our model using our training data.
Please note that training neural networks can take a considerable amount of time, especially when you're using larger datasets or more complex architectures. For more complicated tasks, LSTMs and GRUs might be a better choice due to their ability to capture long-term dependencies.
5.3.5 Practical Exercise: Text Generation with RNNs
A fun and educational exercise that can help solidify your understanding of RNNs is to use them for text generation. This involves training a network on a large volume of text data, then having it generate new text character by character or word by word.
For example, you could train a RNN on all of Shakespeare's works, then have it generate new "Shakespearean" text. Or train it on a bunch of recipes and have it come up with new recipes. The possibilities are endless! This exercise can help you understand the challenges and intricacies of working with sequence data and RNNs.
Example:
Let's take a look at a simple example of text generation using RNNs. In this case, we'll use a simple LSTM-based model and we'll use a sample dataset of Shakespeare's works for training.
import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.optimizers import Adam
# Load data
path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')
text = open(path_to_file, 'rb').read().decode(encoding='utf-8')
# Create a mapping from unique characters to indices
vocab = sorted(set(text))
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)
text_as_int = np.array([char2idx[c] for c in text])
# The maximum length sentence we want for a single input in characters
seq_length = 100
examples_per_epoch = len(text) // (seq_length+1)
# Create training examples / targets
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)
sequences = char_dataset.batch(seq_length+1, drop_remainder=True)
def split_input_target(chunk):
input_text = chunk[:-1]
target_text = chunk[1:]
return input_text, target_text
dataset = sequences.map(split_input_target)
# Batch size
BATCH_SIZE = 64
# Buffer size to shuffle the dataset
BUFFER_SIZE = 10000
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
# Building the model
vocab_size = len(vocab)
embedding_dim = 256
rnn_units = 1024
def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim,
batch_input_shape=[batch_size, None]),
tf.keras.layers.GRU(rnn_units,
return_sequences=True,
stateful=True,
recurrent_initializer='glorot_uniform'),
tf.keras.layers.Dense(vocab_size)
])
return model
model = build_model(
vocab_size=len(vocab),
embedding_dim=embedding_dim,
rnn_units=rnn_units,
batch_size=BATCH_SIZE)
# Training the model
def loss(labels, logits):
return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)
model.compile(optimizer='adam', loss=loss)
# Directory where the checkpoints will be saved
checkpoint_dir = './training_checkpoints'
# Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_prefix,
save_weights_only=True)
history = model.fit(dataset, epochs=10, callbacks=[checkpoint_callback])This code first loads a dataset of Shakespeare's writings, which is then processed and tokenized. We then define an LSTM-based model, which includes an embedding layer, an LSTM layer, and a dense output layer. The model is then compiled and trained on our dataset.
To generate text after training, we'll use a batch size of 1. Because of the way the RNN state is passed from timestep to timestep, the model only accepts a fixed batch size once built. To run the model with a different batch_size, we'll need to rebuild the model and restore the weights from the checkpoint:
model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)
model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))
model.buildAfter we rebuild the model and restore the weights from the checkpoint, we can then use the model to generate text:
model.build(tf.TensorShape([1, None]))
def generate_text(model, start_string):
# Evaluation step (generating text using the learned model)
# Number of characters to generate
num_generate = 1000
# Converting our start string to numbers (vectorizing)
input_eval = [char2idx[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0)
# Empty string to store our results
text_generated = []
# Low temperature results in more predictable text.
# Higher temperature results in more surprising text.
temperature = 1.0
# Here batch size == 1
model.reset_states()
for i in range(num_generate):
predictions = model(input_eval)
# remove the batch dimension
predictions = tf.squeeze(predictions, 0)
# using a categorical distribution to predict the character returned by the model
predictions = predictions / temperature
predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
# Pass the predicted character as the next input to the model
# along with the previous hidden state
input_eval = tf.expand_dims([predicted_id], 0)
text_generated.append(idx2char[predicted_id])
return (start_string + ''.join(text_generated))
print(generate_text(model, start_string=u"ROMEO: "))This code defines a generate_text function that takes in a model and a start string as input. The function then uses the model to generate a specified number of characters of text, using the start string as a seed. The generated text is then printed out.
In this case, the start string is "ROMEO: ", so the generated text will be a continuation of this string in the style of Shakespeare's writing. It's important to note that the quality of the generated text will depend on the amount of training the model has received. With only a few epochs of training, the generated text might not make much sense, but with more training, the model should be able to generate more coherent and interesting text.
5.3 Recurrent Neural Networks (RNNs)
Recurrent Neural Networks, or RNNs, are a powerful type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or the spoken word. They are particularly effective for tasks where sequential data is involved, as they can use their internal state (memory) to process sequences of inputs.
In order to achieve this, RNNs are designed to perform the same task for every element of a sequence, with the output being dependent on the previous computations. This recurrence of operation gives them a kind of memory and allows them to build up a representation of the entire sequence, rather than just processing each input in isolation.
One of the key benefits of RNNs is their ability to handle variable-length sequences of data. This means that they can be used to process inputs of different lengths, which can be particularly useful in natural language processing tasks, where sentences can vary greatly in length.
Another advantage of RNNs is their ability to model temporal dependencies between elements in a sequence. This means that they can learn patterns in the data that are related to the order in which the inputs were presented, which can be useful in tasks such as speech recognition or music composition.
Overall, RNNs are a versatile and powerful type of neural network that have a wide range of applications across a variety of fields. Their ability to handle sequential data and model temporal dependencies make them well-suited to many real-world problems, and they are likely to play an increasingly important role in the development of artificial intelligence in the years to come.
5.3.1 RNN Architecture
Recurrent Neural Networks (RNNs) are a type of neural network architecture that are particularly adept at processing sequential data. The power behind RNNs lies in their ability to capture the hidden state of a sequence of inputs. This hidden state carries information about the history of the sequence, and is passed along from one time step to the next. By reusing these same weights at each time step, RNNs are able to process sequences of varying lengths, without requiring a fixed number of input or output layers.
RNNs are capable of performing a wide range of tasks, from language modeling to speech recognition and even image captioning. Due to their ability to keep track of sequential dependencies, RNNs are ideal for processing data that has a temporal component. For example, they can be used to generate music or predict stock prices based on historical trends.
The use of RNNs has revolutionized the field of deep learning, enabling researchers and practitioners to tackle complex problems that were previously considered unsolvable. With their ability to process sequential data and capture hidden dependencies, RNNs are a powerful tool for any data scientist or machine learning practitioner to have in their toolkit.
Example:
Here's a simple diagram of what the RNN architecture looks like:
_____
/ \
| h_t-1 |----->| h_t |----->| h_t+1 |
\_____/
/ | / | / |
x_t-1 x_t x_t+1Where h_t is the hidden state at time t, and x_t is the input at time t. The same function and set of parameters are used at every time step.
5.3.2 The Problem of Long-Term Dependencies
Recurrent Neural Networks (RNNs) are a type of neural network that are designed to handle sequential data. These networks have the ability to remember past inputs and use that information to influence the output at the current time step. However, despite their theoretical ability to handle "long-term dependencies", they often struggle to do so due to the "vanishing gradients" problem.
During training, the network adjusts its weights by backpropagating the error gradient from the output back into the network. However, when the sequences are long, the gradients become increasingly small, eventually vanishing altogether. This makes it difficult for the network to learn effectively, as it cannot effectively adjust its weights based on the input data.
To address this issue, various techniques have been developed to help RNNs learn to handle long-term dependencies. One such technique is the Long Short-Term Memory (LSTM) network, which uses a memory cell to store information over multiple time steps. Another technique is the Gated Recurrent Unit (GRU), which uses a gating mechanism to selectively update the memory cell.
While these techniques have been successful in improving the performance of RNNs, the "vanishing gradients" problem remains a significant challenge in the field of deep learning. As such, ongoing research is focused on developing new techniques to address this issue and improve the ability of RNNs to handle long-term dependencies.
5.3.3 LSTMs and GRUs
To overcome the vanishing gradient problem of a standard RNN, Long Short Term Memory cells (LSTMs) were introduced. LSTMs have a 'memory cell' that can keep information in memory for long periods of time. Essentially, it allows past information to be reinjected at a later time, thus helping the network to learn from important past information.
Gated Recurrent Units (GRUs) are a variation on LSTMs. The GRU unit controls the flow of information like the LSTM unit, but without having to use a memory unit. It just exposes the full hidden content without any control, but uses gating units to control the information flow, which makes it simpler than LSTMs as there are fewer parameters to train and thus, computationally more efficient.
One of the key advantages of LSTMs is that they can handle long-term dependencies. This means that the network can retain information over long periods of time, allowing it to make better predictions. However, the downside of LSTMs is that they require more computational resources to train, as they have a larger number of parameters.
On the other hand, GRUs are simpler than LSTMs, with fewer parameters to train. This makes them computationally more efficient, and thus more suitable for real-time applications. However, they may not perform as well as LSTMs when it comes to handling long-term dependencies.
Despite their differences, both LSTMs and GRUs are effective in dealing with the vanishing gradient problem. They have revolutionized the field of deep learning and have led to significant improvements in a range of applications, from speech recognition to natural language processing.
5.3.4 Building a Simple RNN in Python
Now that we've covered the basics of RNNs, let's delve deeper into the topic and build a more complex one in Python using the Keras library. Recurrent neural networks are a type of deep learning algorithm that can model sequential data, which makes them suitable for a wide range of applications, including natural language processing, speech recognition, and time series analysis. In order to build a more complex RNN, we'll need to consider the architecture, the number of layers, and the types of activation functions used in each layer.
For this exercise, we'll use the IMDB movie review dataset, which is a binary classification problem consisting of 50,000 movie reviews, half of which are positive and half are negative. The dataset has already been preprocessed, so we can focus on building the RNN. To begin, we'll first split the dataset into training and testing sets and then preprocess the text data by tokenizing it and converting the words into numerical vectors.
Once the data is preprocessed, we can begin building the RNN using Keras. We'll start by defining the architecture of the network, which will consist of an embedding layer, a recurrent layer, and a dense output layer. The embedding layer will be responsible for converting the numerical vectors into a dense representation that can be fed into the recurrent layer. The recurrent layer will use LSTM units to model the sequential nature of the data, and the dense output layer will produce a binary classification output.
In order to improve the performance of the RNN, we can experiment with different hyperparameters, such as the number of units in the recurrent layer, the learning rate, and the batch size. We can also use techniques such as dropout and early stopping to prevent overfitting and improve generalization.
Overall, building a more complex RNN using Keras is a challenging yet rewarding task that requires a deep understanding of the underlying principles and techniques. By following the steps outlined above, we can gain valuable experience in building and optimizing recurrent neural networks for a wide range of applications.
Examle:
Here's a basic example of how to create a simple RNN model using Keras:
from keras.models import Sequential
from keras.layers import Embedding, SimpleRNN, Dense
from keras.datasets import imdb
from keras.preprocessing import sequence
# Number of words to consider as features
max_features = 10000
# Cut texts after this number of words (among top max_features most common words)
maxlen = 500
# Load data
(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)
# Pad sequences
input_train = sequence.pad_sequences(input_train, maxlen=maxlen)
input_test = sequence.pad_sequences(input_test, maxlen=maxlen)
# Define model
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
# Train model
history = model.fit(input_train, y_train, epochs=10, batch_size=128, validation_split=0.2)In this code, we first load and preprocess the IMDB movie review dataset. We then define our model, which is a simple RNN with an embedding layer, a SimpleRNN layer, and a dense output layer. The model is then compiled with a binary crossentropy loss function (as this is a binary classification problem), and the RMSProp optimizer. Finally, we train our model using our training data.
Please note that training neural networks can take a considerable amount of time, especially when you're using larger datasets or more complex architectures. For more complicated tasks, LSTMs and GRUs might be a better choice due to their ability to capture long-term dependencies.
5.3.5 Practical Exercise: Text Generation with RNNs
A fun and educational exercise that can help solidify your understanding of RNNs is to use them for text generation. This involves training a network on a large volume of text data, then having it generate new text character by character or word by word.
For example, you could train a RNN on all of Shakespeare's works, then have it generate new "Shakespearean" text. Or train it on a bunch of recipes and have it come up with new recipes. The possibilities are endless! This exercise can help you understand the challenges and intricacies of working with sequence data and RNNs.
Example:
Let's take a look at a simple example of text generation using RNNs. In this case, we'll use a simple LSTM-based model and we'll use a sample dataset of Shakespeare's works for training.
import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.optimizers import Adam
# Load data
path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')
text = open(path_to_file, 'rb').read().decode(encoding='utf-8')
# Create a mapping from unique characters to indices
vocab = sorted(set(text))
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)
text_as_int = np.array([char2idx[c] for c in text])
# The maximum length sentence we want for a single input in characters
seq_length = 100
examples_per_epoch = len(text) // (seq_length+1)
# Create training examples / targets
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)
sequences = char_dataset.batch(seq_length+1, drop_remainder=True)
def split_input_target(chunk):
input_text = chunk[:-1]
target_text = chunk[1:]
return input_text, target_text
dataset = sequences.map(split_input_target)
# Batch size
BATCH_SIZE = 64
# Buffer size to shuffle the dataset
BUFFER_SIZE = 10000
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
# Building the model
vocab_size = len(vocab)
embedding_dim = 256
rnn_units = 1024
def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim,
batch_input_shape=[batch_size, None]),
tf.keras.layers.GRU(rnn_units,
return_sequences=True,
stateful=True,
recurrent_initializer='glorot_uniform'),
tf.keras.layers.Dense(vocab_size)
])
return model
model = build_model(
vocab_size=len(vocab),
embedding_dim=embedding_dim,
rnn_units=rnn_units,
batch_size=BATCH_SIZE)
# Training the model
def loss(labels, logits):
return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)
model.compile(optimizer='adam', loss=loss)
# Directory where the checkpoints will be saved
checkpoint_dir = './training_checkpoints'
# Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_prefix,
save_weights_only=True)
history = model.fit(dataset, epochs=10, callbacks=[checkpoint_callback])This code first loads a dataset of Shakespeare's writings, which is then processed and tokenized. We then define an LSTM-based model, which includes an embedding layer, an LSTM layer, and a dense output layer. The model is then compiled and trained on our dataset.
To generate text after training, we'll use a batch size of 1. Because of the way the RNN state is passed from timestep to timestep, the model only accepts a fixed batch size once built. To run the model with a different batch_size, we'll need to rebuild the model and restore the weights from the checkpoint:
model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)
model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))
model.buildAfter we rebuild the model and restore the weights from the checkpoint, we can then use the model to generate text:
model.build(tf.TensorShape([1, None]))
def generate_text(model, start_string):
# Evaluation step (generating text using the learned model)
# Number of characters to generate
num_generate = 1000
# Converting our start string to numbers (vectorizing)
input_eval = [char2idx[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0)
# Empty string to store our results
text_generated = []
# Low temperature results in more predictable text.
# Higher temperature results in more surprising text.
temperature = 1.0
# Here batch size == 1
model.reset_states()
for i in range(num_generate):
predictions = model(input_eval)
# remove the batch dimension
predictions = tf.squeeze(predictions, 0)
# using a categorical distribution to predict the character returned by the model
predictions = predictions / temperature
predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
# Pass the predicted character as the next input to the model
# along with the previous hidden state
input_eval = tf.expand_dims([predicted_id], 0)
text_generated.append(idx2char[predicted_id])
return (start_string + ''.join(text_generated))
print(generate_text(model, start_string=u"ROMEO: "))This code defines a generate_text function that takes in a model and a start string as input. The function then uses the model to generate a specified number of characters of text, using the start string as a seed. The generated text is then printed out.
In this case, the start string is "ROMEO: ", so the generated text will be a continuation of this string in the style of Shakespeare's writing. It's important to note that the quality of the generated text will depend on the amount of training the model has received. With only a few epochs of training, the generated text might not make much sense, but with more training, the model should be able to generate more coherent and interesting text.
5.3 Recurrent Neural Networks (RNNs)
Recurrent Neural Networks, or RNNs, are a powerful type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or the spoken word. They are particularly effective for tasks where sequential data is involved, as they can use their internal state (memory) to process sequences of inputs.
In order to achieve this, RNNs are designed to perform the same task for every element of a sequence, with the output being dependent on the previous computations. This recurrence of operation gives them a kind of memory and allows them to build up a representation of the entire sequence, rather than just processing each input in isolation.
One of the key benefits of RNNs is their ability to handle variable-length sequences of data. This means that they can be used to process inputs of different lengths, which can be particularly useful in natural language processing tasks, where sentences can vary greatly in length.
Another advantage of RNNs is their ability to model temporal dependencies between elements in a sequence. This means that they can learn patterns in the data that are related to the order in which the inputs were presented, which can be useful in tasks such as speech recognition or music composition.
Overall, RNNs are a versatile and powerful type of neural network that have a wide range of applications across a variety of fields. Their ability to handle sequential data and model temporal dependencies make them well-suited to many real-world problems, and they are likely to play an increasingly important role in the development of artificial intelligence in the years to come.
5.3.1 RNN Architecture
Recurrent Neural Networks (RNNs) are a type of neural network architecture that are particularly adept at processing sequential data. The power behind RNNs lies in their ability to capture the hidden state of a sequence of inputs. This hidden state carries information about the history of the sequence, and is passed along from one time step to the next. By reusing these same weights at each time step, RNNs are able to process sequences of varying lengths, without requiring a fixed number of input or output layers.
RNNs are capable of performing a wide range of tasks, from language modeling to speech recognition and even image captioning. Due to their ability to keep track of sequential dependencies, RNNs are ideal for processing data that has a temporal component. For example, they can be used to generate music or predict stock prices based on historical trends.
The use of RNNs has revolutionized the field of deep learning, enabling researchers and practitioners to tackle complex problems that were previously considered unsolvable. With their ability to process sequential data and capture hidden dependencies, RNNs are a powerful tool for any data scientist or machine learning practitioner to have in their toolkit.
Example:
Here's a simple diagram of what the RNN architecture looks like:
_____
/ \
| h_t-1 |----->| h_t |----->| h_t+1 |
\_____/
/ | / | / |
x_t-1 x_t x_t+1Where h_t is the hidden state at time t, and x_t is the input at time t. The same function and set of parameters are used at every time step.
5.3.2 The Problem of Long-Term Dependencies
Recurrent Neural Networks (RNNs) are a type of neural network that are designed to handle sequential data. These networks have the ability to remember past inputs and use that information to influence the output at the current time step. However, despite their theoretical ability to handle "long-term dependencies", they often struggle to do so due to the "vanishing gradients" problem.
During training, the network adjusts its weights by backpropagating the error gradient from the output back into the network. However, when the sequences are long, the gradients become increasingly small, eventually vanishing altogether. This makes it difficult for the network to learn effectively, as it cannot effectively adjust its weights based on the input data.
To address this issue, various techniques have been developed to help RNNs learn to handle long-term dependencies. One such technique is the Long Short-Term Memory (LSTM) network, which uses a memory cell to store information over multiple time steps. Another technique is the Gated Recurrent Unit (GRU), which uses a gating mechanism to selectively update the memory cell.
While these techniques have been successful in improving the performance of RNNs, the "vanishing gradients" problem remains a significant challenge in the field of deep learning. As such, ongoing research is focused on developing new techniques to address this issue and improve the ability of RNNs to handle long-term dependencies.
5.3.3 LSTMs and GRUs
To overcome the vanishing gradient problem of a standard RNN, Long Short Term Memory cells (LSTMs) were introduced. LSTMs have a 'memory cell' that can keep information in memory for long periods of time. Essentially, it allows past information to be reinjected at a later time, thus helping the network to learn from important past information.
Gated Recurrent Units (GRUs) are a variation on LSTMs. The GRU unit controls the flow of information like the LSTM unit, but without having to use a memory unit. It just exposes the full hidden content without any control, but uses gating units to control the information flow, which makes it simpler than LSTMs as there are fewer parameters to train and thus, computationally more efficient.
One of the key advantages of LSTMs is that they can handle long-term dependencies. This means that the network can retain information over long periods of time, allowing it to make better predictions. However, the downside of LSTMs is that they require more computational resources to train, as they have a larger number of parameters.
On the other hand, GRUs are simpler than LSTMs, with fewer parameters to train. This makes them computationally more efficient, and thus more suitable for real-time applications. However, they may not perform as well as LSTMs when it comes to handling long-term dependencies.
Despite their differences, both LSTMs and GRUs are effective in dealing with the vanishing gradient problem. They have revolutionized the field of deep learning and have led to significant improvements in a range of applications, from speech recognition to natural language processing.
5.3.4 Building a Simple RNN in Python
Now that we've covered the basics of RNNs, let's delve deeper into the topic and build a more complex one in Python using the Keras library. Recurrent neural networks are a type of deep learning algorithm that can model sequential data, which makes them suitable for a wide range of applications, including natural language processing, speech recognition, and time series analysis. In order to build a more complex RNN, we'll need to consider the architecture, the number of layers, and the types of activation functions used in each layer.
For this exercise, we'll use the IMDB movie review dataset, which is a binary classification problem consisting of 50,000 movie reviews, half of which are positive and half are negative. The dataset has already been preprocessed, so we can focus on building the RNN. To begin, we'll first split the dataset into training and testing sets and then preprocess the text data by tokenizing it and converting the words into numerical vectors.
Once the data is preprocessed, we can begin building the RNN using Keras. We'll start by defining the architecture of the network, which will consist of an embedding layer, a recurrent layer, and a dense output layer. The embedding layer will be responsible for converting the numerical vectors into a dense representation that can be fed into the recurrent layer. The recurrent layer will use LSTM units to model the sequential nature of the data, and the dense output layer will produce a binary classification output.
In order to improve the performance of the RNN, we can experiment with different hyperparameters, such as the number of units in the recurrent layer, the learning rate, and the batch size. We can also use techniques such as dropout and early stopping to prevent overfitting and improve generalization.
Overall, building a more complex RNN using Keras is a challenging yet rewarding task that requires a deep understanding of the underlying principles and techniques. By following the steps outlined above, we can gain valuable experience in building and optimizing recurrent neural networks for a wide range of applications.
Examle:
Here's a basic example of how to create a simple RNN model using Keras:
from keras.models import Sequential
from keras.layers import Embedding, SimpleRNN, Dense
from keras.datasets import imdb
from keras.preprocessing import sequence
# Number of words to consider as features
max_features = 10000
# Cut texts after this number of words (among top max_features most common words)
maxlen = 500
# Load data
(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)
# Pad sequences
input_train = sequence.pad_sequences(input_train, maxlen=maxlen)
input_test = sequence.pad_sequences(input_test, maxlen=maxlen)
# Define model
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
# Train model
history = model.fit(input_train, y_train, epochs=10, batch_size=128, validation_split=0.2)In this code, we first load and preprocess the IMDB movie review dataset. We then define our model, which is a simple RNN with an embedding layer, a SimpleRNN layer, and a dense output layer. The model is then compiled with a binary crossentropy loss function (as this is a binary classification problem), and the RMSProp optimizer. Finally, we train our model using our training data.
Please note that training neural networks can take a considerable amount of time, especially when you're using larger datasets or more complex architectures. For more complicated tasks, LSTMs and GRUs might be a better choice due to their ability to capture long-term dependencies.
5.3.5 Practical Exercise: Text Generation with RNNs
A fun and educational exercise that can help solidify your understanding of RNNs is to use them for text generation. This involves training a network on a large volume of text data, then having it generate new text character by character or word by word.
For example, you could train a RNN on all of Shakespeare's works, then have it generate new "Shakespearean" text. Or train it on a bunch of recipes and have it come up with new recipes. The possibilities are endless! This exercise can help you understand the challenges and intricacies of working with sequence data and RNNs.
Example:
Let's take a look at a simple example of text generation using RNNs. In this case, we'll use a simple LSTM-based model and we'll use a sample dataset of Shakespeare's works for training.
import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.optimizers import Adam
# Load data
path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')
text = open(path_to_file, 'rb').read().decode(encoding='utf-8')
# Create a mapping from unique characters to indices
vocab = sorted(set(text))
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)
text_as_int = np.array([char2idx[c] for c in text])
# The maximum length sentence we want for a single input in characters
seq_length = 100
examples_per_epoch = len(text) // (seq_length+1)
# Create training examples / targets
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)
sequences = char_dataset.batch(seq_length+1, drop_remainder=True)
def split_input_target(chunk):
input_text = chunk[:-1]
target_text = chunk[1:]
return input_text, target_text
dataset = sequences.map(split_input_target)
# Batch size
BATCH_SIZE = 64
# Buffer size to shuffle the dataset
BUFFER_SIZE = 10000
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
# Building the model
vocab_size = len(vocab)
embedding_dim = 256
rnn_units = 1024
def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim,
batch_input_shape=[batch_size, None]),
tf.keras.layers.GRU(rnn_units,
return_sequences=True,
stateful=True,
recurrent_initializer='glorot_uniform'),
tf.keras.layers.Dense(vocab_size)
])
return model
model = build_model(
vocab_size=len(vocab),
embedding_dim=embedding_dim,
rnn_units=rnn_units,
batch_size=BATCH_SIZE)
# Training the model
def loss(labels, logits):
return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)
model.compile(optimizer='adam', loss=loss)
# Directory where the checkpoints will be saved
checkpoint_dir = './training_checkpoints'
# Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_prefix,
save_weights_only=True)
history = model.fit(dataset, epochs=10, callbacks=[checkpoint_callback])This code first loads a dataset of Shakespeare's writings, which is then processed and tokenized. We then define an LSTM-based model, which includes an embedding layer, an LSTM layer, and a dense output layer. The model is then compiled and trained on our dataset.
To generate text after training, we'll use a batch size of 1. Because of the way the RNN state is passed from timestep to timestep, the model only accepts a fixed batch size once built. To run the model with a different batch_size, we'll need to rebuild the model and restore the weights from the checkpoint:
model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)
model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))
model.buildAfter we rebuild the model and restore the weights from the checkpoint, we can then use the model to generate text:
model.build(tf.TensorShape([1, None]))
def generate_text(model, start_string):
# Evaluation step (generating text using the learned model)
# Number of characters to generate
num_generate = 1000
# Converting our start string to numbers (vectorizing)
input_eval = [char2idx[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0)
# Empty string to store our results
text_generated = []
# Low temperature results in more predictable text.
# Higher temperature results in more surprising text.
temperature = 1.0
# Here batch size == 1
model.reset_states()
for i in range(num_generate):
predictions = model(input_eval)
# remove the batch dimension
predictions = tf.squeeze(predictions, 0)
# using a categorical distribution to predict the character returned by the model
predictions = predictions / temperature
predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
# Pass the predicted character as the next input to the model
# along with the previous hidden state
input_eval = tf.expand_dims([predicted_id], 0)
text_generated.append(idx2char[predicted_id])
return (start_string + ''.join(text_generated))
print(generate_text(model, start_string=u"ROMEO: "))This code defines a generate_text function that takes in a model and a start string as input. The function then uses the model to generate a specified number of characters of text, using the start string as a seed. The generated text is then printed out.
In this case, the start string is "ROMEO: ", so the generated text will be a continuation of this string in the style of Shakespeare's writing. It's important to note that the quality of the generated text will depend on the amount of training the model has received. With only a few epochs of training, the generated text might not make much sense, but with more training, the model should be able to generate more coherent and interesting text.