I'm getting this error
'ValueError: Tensor Tensor("Placeholder:0", shape=(1, 1), dtype=int32) is not an element of this graph.'
The code is running perfectly fine without with tf.Graph(). as_default():. However I need to call M.sample(...) multiple times and each time the memory won't be free after session.close(). Probably there is a memory leak but not sure where is it.
I want to restore a pre-trained neural network, set it as default graph, and testing it multiple times (like 10000) over the default graph without making it larger each time.
The code is:
def SessionOpener(save): grph = tf.get_default_graph() sess = tf.Session(graph=grph) ckpt = tf.train.get_checkpoint_state(save) saver = tf.train.import_meta_graph('./predictor/save/model.ckpt.meta') if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) tf.global_variables_initializer().run(session=sess) return sess def LoadPredictor(save): with open(os.path.join(save, 'config.pkl'), 'rb') as f: saved_args = cPickle.load(f) with open(os.path.join(save, 'words_vocab.pkl'), 'rb') as f: words, vocab = cPickle.load(f) model = Model(saved_args, True) return model, words, vocab if __name__ == '__main__': Save = './save' M, W, V = LoadPredictor(Save) Sess = SessionOpener(Save) word = M.sample(Sess, W, V, 1, str(123), 2, 1, 4) Sess.close() And the model is:
class Model(): def __init__(self, args, infer=False): with tf.Graph().as_default(): self.args = args if infer: args.batch_size = 1 args.seq_length = 1 if args.model == 'rnn': cell_fn = rnn.BasicRNNCell elif args.model == 'gru': cell_fn = rnn.GRUCell elif args.model == 'lstm': cell_fn = rnn.BasicLSTMCell else: raise Exception("model type not supported: {}".format(args.model)) cells = [] for _ in range(args.num_layers): cell = cell_fn(args.rnn_size) cells.append(cell) self.cell = cell = rnn.MultiRNNCell(cells) self.input_data = tf.placeholder(tf.int32, [args.batch_size, args.seq_length]) self.targets = tf.placeholder(tf.int32, [args.batch_size, args.seq_length]) self.initial_state = cell.zero_state(args.batch_size, tf.float32) self.batch_pointer = tf.Variable(0, name="batch_pointer", trainable=False, dtype=tf.int32) self.inc_batch_pointer_op = tf.assign(self.batch_pointer, self.batch_pointer + 1) self.epoch_pointer = tf.Variable(0, name="epoch_pointer", trainable=False) self.batch_time = tf.Variable(0.0, name="batch_time", trainable=False) tf.summary.scalar("time_batch", self.batch_time) def variable_summaries(var): """Attach a lot of summaries to a Tensor (for TensorBoard visualization).""" with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.summary.scalar('mean', mean) tf.summary.scalar('max', tf.reduce_max(var)) tf.summary.scalar('min', tf.reduce_min(var)) with tf.variable_scope('rnnlm'): softmax_w = tf.get_variable("softmax_w", [args.rnn_size, args.vocab_size]) variable_summaries(softmax_w) softmax_b = tf.get_variable("softmax_b", [args.vocab_size]) variable_summaries(softmax_b) with tf.device("/cpu:0"): embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size]) inputs = tf.split(tf.nn.embedding_lookup(embedding, self.input_data), args.seq_length, 1) inputs = [tf.squeeze(input_, [1]) for input_ in inputs] def loop(prev, _): prev = tf.matmul(prev, softmax_w) + softmax_b prev_symbol = tf.stop_gradient(tf.argmax(prev, 1)) return tf.nn.embedding_lookup(embedding, prev_symbol) outputs, last_state = legacy_seq2seq.rnn_decoder(inputs, self.initial_state, cell, loop_function=loop if infer else None, scope='rnnlm') output = tf.reshape(tf.concat(outputs, 1), [-1, args.rnn_size]) self.logits = tf.matmul(output, softmax_w) + softmax_b self.probs = tf.nn.softmax(self.logits) loss = legacy_seq2seq.sequence_loss_by_example([self.logits], [tf.reshape(self.targets, [-1])], [tf.ones([args.batch_size * args.seq_length])], args.vocab_size) self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length tf.summary.scalar("cost", self.cost) self.final_state = last_state self.lr = tf.Variable(0.0, trainable=False) tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars), args.grad_clip) optimizer = tf.train.AdamOptimizer(self.lr) self.train_op = optimizer.apply_gradients(zip(grads, tvars)) def sample(self, sess, words, vocab, num=200, prime='first all', sampling_type=1, pick=0, width=4): def weighted_pick(weights): t = np.cumsum(weights) s = np.sum(weights) return(int(np.searchsorted(t, np.random.rand(1)*s))) ret = '' if pick == 1: state = sess.run(self.cell.zero_state(1, tf.float32)) if not len(prime) or prime == ' ': prime = random.choice(list(vocab.keys())) for word in prime.split()[:-1]: x = np.zeros((1, 1)) x[0, 0] = vocab.get(word,0) feed = {self.input_data: x, self.initial_state:state} [state] = sess.run([self.final_state], feed) ret = prime word = prime.split()[-1] for n in range(num): x = np.zeros((1, 1)) x[0, 0] = vocab.get(word, 0) feed = {self.input_data: x, self.initial_state:state} [probs, state] = sess.run([self.probs, self.final_state], feed) p = probs[0] if sampling_type == 0: sample = np.argmax(p) elif sampling_type == 2: if word == '\n': sample = weighted_pick(p) else: sample = np.argmax(p) else: # sampling_type == 1 default: sample = weighted_pick(p) ret = words[sample] return ret and the output is:
Traceback (most recent call last): File "/rcg/software/Linux/Ubuntu/16.04/amd64/TOOLS/TENSORFLOW/1.2.1-GPU-PY352/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 942, in _run allow_operation=False) File "/rcg/software/Linux/Ubuntu/16.04/amd64/TOOLS/TENSORFLOW/1.2.1-GPU-PY352/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2584, in as_graph_element return self._as_graph_element_locked(obj, allow_tensor, allow_operation) File "/rcg/software/Linux/Ubuntu/16.04/amd64/TOOLS/TENSORFLOW/1.2.1-GPU-PY352/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2663, in _as_graph_element_locked raise ValueError("Tensor %s is not an element of this graph." % obj) ValueError: Tensor Tensor("Placeholder:0", shape=(1, 1), dtype=int32) is not an element of this graph.