# Chapter 8: Project: Text Generation with Autoregressive Models

## 8.2 Model Creation

Creating the autoregressive model involves defining the architecture of the model. In our case, we will be using an LSTM (Long Short-Term Memory) network for the purpose. LSTMs are a type of Recurrent Neural Network (RNN) that are particularly good at processing sequential data, making them ideal for a project like ours.

To create the model, we first import the necessary modules from Keras, a deep learning library in Python:

`from tensorflow.keras.models import Sequential`

from tensorflow.keras.layers import Embedding, LSTM, Dense

from tensorflow.keras.optimizers import Adam

Then, we define the architecture of the model:

`from tensorflow.keras.models import Sequential`

from tensorflow.keras.layers import Embedding, LSTM, Dense

from tensorflow.keras.optimizers import Adam

def create_model(vocab_size, seq_length):

model = Sequential()

model.add(Embedding(vocab_size, seq_length, input_length=seq_length))

model.add(LSTM(100, return_sequences=True))

model.add(LSTM(100))

model.add(Dense(100, activation='relu'))

model.add(Dense(vocab_size, activation='softmax'))

# Compile the model

optimizer = Adam(learning_rate=0.01)

model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

model.summary()

return model

In the above code, we define a function

which takes as input the vocabulary size and the sequence length, and returns the compiled model.**create_model**

The model is a Sequential model, which means that it is composed of a linear stack of layers. It has the following layers:

- An Embedding layer: This turns positive integers (indexes) into dense vectors of fixed size. This layer can only be used as the first layer in a model.
- Two LSTM layers: These are the recurrent layers of the model. The first LSTM layer returns sequences, which means that it outputs the full sequence of outputs for each sample. This is necessary for stacking LSTM layers.
- A Dense layer: This is a fully connected layer where each input node is connected to each output node.
- Another Dense layer: This is the output layer of the model. It has as many nodes as the size of the vocabulary and uses the softmax activation function, which means that it will output a probability distribution over the vocabulary - each output node will output a value between 0 and 1, and the sum of all the output values will be 1.

The model is then compiled with the categorical crossentropy loss function, which is suitable for multiclass classification problems, and the Adam optimizer.

The summary of the model is printed, which gives an overview of the architecture of the model and the number of parameters that it has.

The function returns the compiled model, ready for training.

In the next step, we will train our model using our preprocessed dataset.

## 8.2 Model Creation

Creating the autoregressive model involves defining the architecture of the model. In our case, we will be using an LSTM (Long Short-Term Memory) network for the purpose. LSTMs are a type of Recurrent Neural Network (RNN) that are particularly good at processing sequential data, making them ideal for a project like ours.

To create the model, we first import the necessary modules from Keras, a deep learning library in Python:

`from tensorflow.keras.models import Sequential`

from tensorflow.keras.layers import Embedding, LSTM, Dense

from tensorflow.keras.optimizers import Adam

Then, we define the architecture of the model:

`from tensorflow.keras.models import Sequential`

from tensorflow.keras.layers import Embedding, LSTM, Dense

from tensorflow.keras.optimizers import Adam

def create_model(vocab_size, seq_length):

model = Sequential()

model.add(Embedding(vocab_size, seq_length, input_length=seq_length))

model.add(LSTM(100, return_sequences=True))

model.add(LSTM(100))

model.add(Dense(100, activation='relu'))

model.add(Dense(vocab_size, activation='softmax'))

# Compile the model

optimizer = Adam(learning_rate=0.01)

model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

model.summary()

return model

In the above code, we define a function

which takes as input the vocabulary size and the sequence length, and returns the compiled model.**create_model**

The model is a Sequential model, which means that it is composed of a linear stack of layers. It has the following layers:

- An Embedding layer: This turns positive integers (indexes) into dense vectors of fixed size. This layer can only be used as the first layer in a model.
- Two LSTM layers: These are the recurrent layers of the model. The first LSTM layer returns sequences, which means that it outputs the full sequence of outputs for each sample. This is necessary for stacking LSTM layers.
- A Dense layer: This is a fully connected layer where each input node is connected to each output node.
- Another Dense layer: This is the output layer of the model. It has as many nodes as the size of the vocabulary and uses the softmax activation function, which means that it will output a probability distribution over the vocabulary - each output node will output a value between 0 and 1, and the sum of all the output values will be 1.

The model is then compiled with the categorical crossentropy loss function, which is suitable for multiclass classification problems, and the Adam optimizer.

The summary of the model is printed, which gives an overview of the architecture of the model and the number of parameters that it has.

The function returns the compiled model, ready for training.

In the next step, we will train our model using our preprocessed dataset.

## 8.2 Model Creation

Creating the autoregressive model involves defining the architecture of the model. In our case, we will be using an LSTM (Long Short-Term Memory) network for the purpose. LSTMs are a type of Recurrent Neural Network (RNN) that are particularly good at processing sequential data, making them ideal for a project like ours.

To create the model, we first import the necessary modules from Keras, a deep learning library in Python:

`from tensorflow.keras.models import Sequential`

from tensorflow.keras.layers import Embedding, LSTM, Dense

from tensorflow.keras.optimizers import Adam

Then, we define the architecture of the model:

`from tensorflow.keras.models import Sequential`

from tensorflow.keras.layers import Embedding, LSTM, Dense

from tensorflow.keras.optimizers import Adam

def create_model(vocab_size, seq_length):

model = Sequential()

model.add(Embedding(vocab_size, seq_length, input_length=seq_length))

model.add(LSTM(100, return_sequences=True))

model.add(LSTM(100))

model.add(Dense(100, activation='relu'))

model.add(Dense(vocab_size, activation='softmax'))

# Compile the model

optimizer = Adam(learning_rate=0.01)

model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

model.summary()

return model

In the above code, we define a function

which takes as input the vocabulary size and the sequence length, and returns the compiled model.**create_model**

The model is a Sequential model, which means that it is composed of a linear stack of layers. It has the following layers:

- An Embedding layer: This turns positive integers (indexes) into dense vectors of fixed size. This layer can only be used as the first layer in a model.
- Two LSTM layers: These are the recurrent layers of the model. The first LSTM layer returns sequences, which means that it outputs the full sequence of outputs for each sample. This is necessary for stacking LSTM layers.
- A Dense layer: This is a fully connected layer where each input node is connected to each output node.
- Another Dense layer: This is the output layer of the model. It has as many nodes as the size of the vocabulary and uses the softmax activation function, which means that it will output a probability distribution over the vocabulary - each output node will output a value between 0 and 1, and the sum of all the output values will be 1.

The model is then compiled with the categorical crossentropy loss function, which is suitable for multiclass classification problems, and the Adam optimizer.

The summary of the model is printed, which gives an overview of the architecture of the model and the number of parameters that it has.

The function returns the compiled model, ready for training.

In the next step, we will train our model using our preprocessed dataset.

## 8.2 Model Creation

`from tensorflow.keras.models import Sequential`

from tensorflow.keras.layers import Embedding, LSTM, Dense

from tensorflow.keras.optimizers import Adam

Then, we define the architecture of the model:

`from tensorflow.keras.models import Sequential`

from tensorflow.keras.layers import Embedding, LSTM, Dense

from tensorflow.keras.optimizers import Adam

def create_model(vocab_size, seq_length):

model = Sequential()

model.add(Embedding(vocab_size, seq_length, input_length=seq_length))

model.add(LSTM(100, return_sequences=True))

model.add(LSTM(100))

model.add(Dense(100, activation='relu'))

model.add(Dense(vocab_size, activation='softmax'))

# Compile the model

optimizer = Adam(learning_rate=0.01)

model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

model.summary()

return model

which takes as input the vocabulary size and the sequence length, and returns the compiled model.**create_model**

The function returns the compiled model, ready for training.

In the next step, we will train our model using our preprocessed dataset.