model.py

embedding_dim = 16

# creating a model for sentiment analysis
model  = tf.keras.Sequential([
                # addinging an Embedding layer for Neural Network to learn the vectors
                tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length = max_length),
                # Global Average pooling is similar to adding up vectors in this case
                tf.keras.layers.GlobalAveragePooling1D(),
                tf.keras.layers.Dense(24, activation = 'relu'),
                tf.keras.layers.Dense(1, activation = 'sigmoid')
])

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


num_epochs = 10

history = model.fit(training_padded,training_labels, epochs = num_epochs,
                    validation_data = (testing_padded,testing_labels))