TensorFlow Machine Learning Projects

Building the model

This model will consist of an embedding layer, followed by three layers of GRU and a fully connected layer with sigmoid activation. For the optimization and accuracy metric, we will use an Adam optimizer and binary_crossentropy, respectively:

1. The model is defined using the following parameters:

def define_model(num_tokens,max_tokens):    '''    Defines the model definition based on input parameters    '''    model = Sequential()    model.add(Embedding(input_dim=num_tokens,                    output_dim=EMBEDDING_SIZE,                    input_length=max_tokens,                    name='layer_embedding'))    model.add(GRU(units=16, name = "gru_1",return_sequences=True))    model.add(GRU(units=8, name = "gru_2",return_sequences=True))    model.add(GRU(units=4, name= "gru_3")) model.add(Dense(1, activation='sigmoid',name="dense_1")) ...