sarcasm_model.py

# Build and train neural network
embedding_dim = 128
 
model = models.Sequential([
    layers.Embedding(vocab, embedding_dim, input_length = mlen),
    layers.LSTM(128, activation='tanh'),
    layers.Dense(32, activation = 'relu'),
    layers.Dense(16, activation = 'relu'),
    layers.Dense(1, activation = 'sigmoid')
])
 
cb = [callbacks.EarlyStopping(patience = 5, restore_best_weights = True)]

model.compile(optimizer = optimizers.Adam(0.01), loss = losses.BinaryCrossentropy(), metrics = ['accuracy'])
history = model.fit(Xtrain, ytrain, batch_size=64, epochs = 256, validation_data=(Xval, yval), callbacks = cb)