" 'NoneType' object is not subscriptable " when tring to build Chatbot from Udemy

[ravirebel0809]

############# Buildimg the Sec2Sec model #########

In[37]:

Creating placeholers for the inputs and the targets.

def model_inputs(): inputs = tf.placeholder(tf.int32, [None, None], name = 'input') targets = tf.placeholder(tf.int32, [None, None], name = 'target') lr = tf.placeholder(tf.float32, name = 'learning_rate') keep_prob = tf.placeholder(tf.float32, name = 'keep_prob') return inputs, targets, lr, keep_prob

In[38]:

Preprocessing the targets.

def preprocess_targets(targets, word2int, batch_size): left_side = tf.fill([batch_size, 1], word2int['<SOS>']) right_side = tf.strided_slice(targets, [0,0], [batch_size, -1], [1,1]) preprocessed_targets = tf.concat([left_side, right_side], 1) return preprocessed_targets

In[39]:

Creating the Encoder RNN Layer.

def encoder_rnn(rnn_inputs, rnn_size, num_layers, keep_prob, sequence_length): lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size) lstm_dropout = tf.contrib.rnn.DropoutWrapper(lstm, input_keep_prob = keep_prob) encoder_cell = tf.contrib.rnn.MultiRNNCell([lstm_dropout] * num_layers) encoder_output, encoder_state = tf.nn.bidirectional_dynamic_rnn(cell_fw = encoder_cell, cell_bw = encoder_cell, sequence_length = sequence_length, inputs = rnn_inputs, dtype = tf.float32)

In[40]:

Decoding the Training set.

def decode_training_set(encoder_state, decoder_cell, decoder_embedded_input, sequence_length, decoding_scope, output_function, keep_prob, batch_size): attention_states = tf.zeros([batch_size, 1, decoder_cell.output_size]) attention_keys, attention_values, attention_score_function, attention_construct_function = tf.contrib.seq2seq.prepare_attention(attention_states, attention_option = "bahdanau", num_units = decoder_cell.output_size) training_decoder_function = tf.contrib.seq2seq.attention_decoder_fn_train(encoder_state[0], attention_keys, attention_values, attention_score_function, attention_construct_function, name = "attn_dec_train") decoder_output, decoder_final_state, decoder_final_context_state = tf.contrib.seq2seq.dynamic_rnn_decoder(decoder_cell, training_decoder_function, decoder_embedded_input, sequence_length, scope = decoding_scope) decoder_output_dropout = tf.nn.dropout(decoder_output, keep_prob) return output_function(decoder_output_dropout)

In[41]:

Decoding the Test / Validation Set.

def decode_test_set(encoder_state, decoder_cell, decoder_embeddings_matrix, sos_id, eos_id, maximum_length, num_words, decoding_scope, output_function, keep_prob, batch_size): attention_states = tf.zeros([batch_size, 1, decoder_cell.output_size]) attention_keys, attention_values, attention_score_function, attention_construct_function = tf.contrib.seq2seq.prepare_attention(attention_states, attention_option = "bahdanau", num_units = decoder_cell.output_size) test_decoder_function = tf.contrib.seq2seq.attention_decoder_fn_inference(output_function, encoder_state[0], attention_keys, attention_values, attention_score_function, attention_construct_function, decoder_embeddings_matrix, sos_id, eos_id, maximum_length, num_words, name = "attn_dec_inf") test_predictions, decoder_final_state, decoder_final_context_state = tf.contrib.seq2seq.dynamic_rnn_decoder(decoder_cell, test_decoder_function, scope = decoding_scope) return test_predictions

In[42]:

Creating the Decoder RNN

def decoder_rnn(decoder_embedded_input, decoder_embeddings_matrix, encoder_state, num_words, sequence_length, rnn_size, num_layers, word2int, keep_prob, batch_size): with tf.variable_scope("decoding") as decoding_scope: lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size) lstm_dropout = tf.contrib.rnn.DropoutWrapper(lstm, input_keep_prob = keep_prob) decoder_cell = tf.contrib.rnn.MultiRNNCell([lstm_dropout] * num_layers) weights = tf.truncated_normal_initializer(stddev = 0.1) biases = tf.zeros_initializer() output_function = lambda x: tf.contrib.layers.fully_connected(x, num_words, None, scope = decoding_scope, weights_initializer = weights, biases_initializer = biases) training_predictions = decode_training_set(encoder_state, decoder_cell, decoder_embedded_input, sequence_length, decoding_scope, output_function, keep_prob, batch_size) decoding_scope.reuse_variables() test_predictions = decode_test_set(encoder_state, decoder_cell, decoder_embeddings_matrix, word2int['<SOS>'], word2int['<EOS>'], sequence_length - 1, num_words, decoding_scope, output_function, keep_prob, batch_size) return training_predictions, test_predictions

In[43]:

Building the Sec2Sec 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.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(decoder_embeddings_matrix, 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

In[44]:

Setting the Hyperparameters.

epochs = 100 batch_size = 64 rnn_size = 512 num_layers = 3 encoding_embedding_size = 512 decoding_embedding_size = 512 learning_rate = 0.01 learning_name_decay = 0.9 min_learning_rate = 0.0001 keep_probability = 0.5

In[45]:

Defining a session

tf.reset_default_graph() session = tf.InteractiveSession()

In[46]:

Loading the model inputs

inputs, targets, lr, keep_prob = model_inputs()

In[47]:

Setting the sequence_length

sequence_length = tf.placeholder_with_default(25, None, name = 'sequence_length')

In[48]:

Getting the shape of the inputs tensor.

input_shape = tf.shape(inputs)

In[49]:

Getting the training and test predictions.

training_predictions, test_predictions = seq2seq_model(tf.reverse(inputs, [-1]), targets, keep_prob, batch_size, sequence_length, len(answerswords2int), len(questionswords2int), encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, questionswords2int)

---

TypeError Traceback (most recent call last) in () 12 rnn_size, 13 num_layers, ---> 14 questionswords2int)

in 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) 19 questionswords2int, 20 keep_prob, ---> 21 batch_size) 22 return training_predictions, test_predictions

in decoder_rnn(decoder_embedded_input, decoder_embeddings_matrix, encoder_state, num_words, sequence_length, rnn_size, num_layers, word2int, keep_prob, batch_size) 21 output_function, 22 keep_prob, ---> 23 batch_size) 24 decoding_scope.reuse_variables() 25 test_predictions = decode_test_set(encoder_state,

in decode_training_set(encoder_state, decoder_cell, decoder_embedded_input, sequence_length, decoding_scope, output_function, keep_prob, batch_size) 4 attention_states = tf.zeros([batch_size, 1, decoder_cell.output_size]) 5 attention_keys, attention_values, attention_score_function, attention_construct_function = tf.contrib.seq2seq.prepare_attention(attention_states, attention_option = "bahdanau", num_units = decoder_cell.output_size) ----> 6 training_decoder_function = tf.contrib.seq2seq.attention_decoder_fn_train(encoder_state[0], 7 attention_keys, 8 attention_values,

TypeError: 'NoneType' object is not subscriptable