Sequential model

sequential.py

#Train a sequential model

# Define the neural network
embedding_dim = 64

model = tf.keras.Sequential([
  # Add an Embedding layer expecting input vocab of size 6000, and output embedding dimension of size 64 we set at the top
  tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=1),
  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim)),
  #tf.keras.layers.Dense(embedding_dim, activation='relu'),

  # use ReLU in place of tanh function since they are very good alternatives of each other.
  tf.keras.layers.Dense(embedding_dim, activation='relu'),

  # Add a Dense layer with 25 units and softmax activation for probability distribution
  tf.keras.layers.Dense(26, activation='softmax')
])

model.summary()