Hands-On Natural Language Processing with Python

Training

Next, a function is created to train the data. The placeholders are created for the question pairs and their labels. The output of the model created in the preceding function is taken through cross-entropy softmax as the loss function. Using the Adam optimizer, the model weights are optimized, as follows:

def train(train_x1, train_x2, train_y, val_x1, val_x2, val_y, max_sent_len, char_map, epochs=2, batch_size=1024, num_classes=2):    with tf.name_scope('Placeholders'):        x1_pls = tf.placeholder(tf.int32, shape=[None, max_sent_len])        x2_pls = tf.placeholder(tf.int32, shape=[None, max_sent_len])        y_pls = tf.placeholder(tf.int64, [None])        keep_prob = tf.placeholder(tf.float32)  # Dropout

Next, the model is created and followed by logit computation. ...