6b5813458e9fb1da4a99c2bafc335526955c3dc5,finetune/base_models/gpt/featurizer.py,,gpt_featurizer,#Any#Any#Any#Any#Any#Any#,224

Before Change

        else:
            embed_weights = tf.stop_gradient(embed_weights)

        X = tf.reshape(X, [-1, config.max_length, 2])

        clf_token = encoder.end_token
        pool_idx = tf.cast(tf.argmax(tf.cast(tf.equal(X[:, :, 0], clf_token), tf.float32), 1), tf.int32)

After Change

    
    initial_shape = tf.shape(X)
    X = tf.reshape(X, shape=tf.concat(([-1], initial_shape[-2:]), 0))
    sequence_length = tf.shape(X)[1]

    with tf.variable_scope("model/featurizer", reuse=reuse):
        embed_weights = tf.get_variable(
            name="we",

Instances