I have codes with remanants of Tensorflow v1.0 , but i want to adapt it to 2.0 This is the bit of code where it keeps stopping ? how do i adapt this ?
# Building the seq2seq model
def seq2seq_model(inputs, targets, keep_prob, batch_size, sequence_length, answers_num_words, questions_num_words, encoder_embedding_size, decoder_embedding_size, rnn_size, num_layers, questionswords2int):
encoder_embedded_input = tf.contrib.layers.embed_sequence(inputs,
answers_num_words + 1,
encoder_embedding_size,
initializer = tf.compat.v1.random_uniform_initializer(0, 1))
encoder_state = encoder_rnn(encoder_embedded_input, rnn_size, num_layers, keep_prob, sequence_length)
preprocessed_targets = preprocess_targets(targets, questionswords2int, batch_size)
decoder_embeddings_matrix = tf.Variable(tf.random.uniform([questions_num_words + 1, decoder_embedding_size], 0, 1))
decoder_embedded_input = tf.nn.embedding_lookup(params=decoder_embeddings_matrix, ids=preprocessed_targets)
training_predictions, test_predictions = decoder_rnn(decoder_embedded_input,
decoder_embeddings_matrix,
encoder_state,
questions_num_words,
sequence_length,
rnn_size,
num_layers,
questionswords2int,
keep_prob,
batch_size)
return training_predictions, test_predictions
It is this bit that is currently holding up my code script ?
