Course 1 and 2: Computer Vision
Course 4: Time Series and Predictions
Sequence models are a type of machine learning model that are designed to handle sequential data, such as text or time series data. They are widely used in natural language processing tasks, such as language translation, sentiment analysis, and text generation.
In TensorFlow, sequence models can be built using recurrent neural networks (RNNs) or transformers. RNNs are particularly effective for processing sequential data because they have a memory component that allows them to capture dependencies between elements in a sequence.
Here's an example of how to build a simple sequence model using TensorFlow:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense
# Define the model architecture
model = Sequential()
model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_sequence_length))
model.add(LSTM(units=128))
model.add(Dense(units=num_classes, activation='softmax'))
# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=32)
# Evaluate the model
loss, accuracy = model.evaluate(X_test, y_test)
In this example, we first define the model architecture using the Sequential API. The model consists of an embedding layer, an LSTM layer, and a dense layer. The embedding layer is used to convert the input sequences into dense vectors. The LSTM layer processes the sequences and captures the dependencies between elements. The dense layer is the output layer that predicts the class labels.
We then compile the model by specifying the optimizer, loss function, and evaluation metrics. After that, we train the model on the training data using the fit method. Finally, we evaluate the model on the test data using the evaluate method.
Note that this is just a basic example, and there are many variations and improvements that can be made to sequence models depending on the specific task and dataset.