Building an LSTM Neural Network for Binary Classification with TensorFlow
Python
In this example we're using TensorFlow to build an LSTM neural network for a binary classification problem. We define the architecture of the LSTM model using the Sequential class from TensorFlow's Keras API. We add an LSTM layer with 64 units and a dense output layer with a sigmoid activation function.
Then we compile the model using the binary cross-entropy loss function, the Adam optimizer and accuracy as the evaluation metric. We then train the model on the training set using 10 epochs and a batch size of 32. Finally, we evaluate the performance of the model on the testing set and print the test loss and accuracy.
1| import tensorflow as tf 2| from tensorflow.keras.models import Sequential 3| from tensorflow.keras.layers import LSTM, Dense 4| 5| # Define model architecture 6| model = Sequential() 7| model.add(LSTM(64, input_shape=(X_train.shape[1], X_train.shape[2]))) 8| model.add(Dense(1, activation='sigmoid')) 9| 10| # Compile the model 11| model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) 12| 13| # Train the model 14| history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test)) 15| 16| # Evaluate the model 17| loss, accuracy = model.evaluate(X_test, y_test) 18| print(f'Test loss: {loss}, Test accuracy: {accuracy}')
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