InvalidArgumentError: 发现 2 个根错误。 Tensorflow 文本分类模型中不兼容的形状

Question

I am trying to get code working from the following repo , which is based off this paper . It had a lot of errors, but I mostly got it working. However, I keep getting the same problem and I really do not understand how to troubleshoot this/what is even going wrong.

The error occurs the second time the validation if statement critera is met. The first time is always works, then breaks on the second. I'm including the output it prints before breaking if its helpful. See error below:

 step = 1, train_loss = 1204.7784423828125, train_accuracy = 0.13725490868091583 counter = 1, dev_loss = 1188.6639287274584, dev_accuacy = 0.2814199453625912 step = 2, train_loss = 1000.983154296875, train_accuracy = 0.26249998807907104 --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _do_call(self, fn, *args) 1364 try: -> 1365 return fn(*args) 1366 except errors.OpError as e: 7 frames InvalidArgumentError: 2 root error(s) found. (0) Invalid argument: Incompatible shapes: [2,185] vs. [2,229] [[{{node loss/cond/add_1}}]] [[viterbi_decode/cond/rnn_1/while/Switch_3/_541]] (1) Invalid argument: Incompatible shapes: [2,185] vs. [2,229] [[{{node loss/cond/add_1}}]] 0 successful operations. 0 derived errors ignored. During handling of the above exception, another exception occurred: InvalidArgumentError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _do_call(self, fn, *args) 1382 '\\nsession_config.graph_options.rewrite_options.' 1383 'disable_meta_optimizer = True') -> 1384 raise type(e)(node_def, op, message) 1385 1386 def _extend_graph(self): InvalidArgumentError: 2 root error(s) found. (0) Invalid argument: Incompatible shapes: [2,185] vs. [2,229] [[node loss/cond/add_1 (defined at /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py:1748) ]] [[viterbi_decode/cond/rnn_1/while/Switch_3/_541]] (1) Invalid argument: Incompatible shapes: [2,185] vs. [2,229] [[node loss/cond/add_1 (defined at /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py:1748) ]] 0 successful operations. 0 derived errors ignored. Original stack trace for 'loss/cond/add_1': File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "/usr/lib/python3.6/runpy.py", line 85, in _run_code exec(code, run_globals) File "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py", line 16, in <module> app.launch_new_instance() File "/usr/local/lib/python3.6/dist-packages/traitlets/config/application.py", line 664, in launch_instance app.start() File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelapp.py", line 477, in start ioloop.IOLoop.instance().start() File "/usr/local/lib/python3.6/dist-packages/tornado/ioloop.py", line 888, in start handler_func(fd_obj, events) File "/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py", line 277, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 450, in _handle_events self._handle_recv() File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 480, in _handle_recv self._run_callback(callback, msg) File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 432, in _run_callback callback(*args, **kwargs) File "/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py", line 277, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher return self.dispatch_shell(stream, msg) File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell handler(stream, idents, msg) File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request user_expressions, allow_stdin) File "/usr/local/lib/python3.6/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "/usr/local/lib/python3.6/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell interactivity=interactivity, compiler=compiler, result=result) File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes if self.run_code(code, result): File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-11-90859dc83f76>", line 66, in <module> main() File "<ipython-input-11-90859dc83f76>", line 12, in main model = DAModel() File "<ipython-input-9-682db36e2a23>", line 148, in __init__ self.logits, self.labels, self.dialogue_lengths) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/crf/python/ops/crf.py", line 257, in crf_log_likelihood transition_params) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/crf/python/ops/crf.py", line 116, in crf_sequence_score false_fn=_multi_seq_fn) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/layers/utils.py", line 202, in smart_cond pred, true_fn=true_fn, false_fn=false_fn, name=name) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/smart_cond.py", line 59, in smart_cond name=name) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/util/deprecation.py", line 507, in new_func return func(*args, **kwargs) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/control_flow_ops.py", line 1235, in cond orig_res_f, res_f = context_f.BuildCondBranch(false_fn) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/control_flow_ops.py", line 1061, in BuildCondBranch original_result = fn() File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/crf/python/ops/crf.py", line 104, in _multi_seq_fn unary_scores = crf_unary_score(tag_indices, sequence_lengths, inputs) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/crf/python/ops/crf.py", line 287, in crf_unary_score flattened_tag_indices = array_ops.reshape(offsets + tag_indices, [-1]) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_ops.py", line 899, in binary_op_wrapper return func(x, y, name=name) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_ops.py", line 1197, in _add_dispatch return gen_math_ops.add_v2(x, y, name=name) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/gen_math_ops.py", line 549, in add_v2 "AddV2", x=x, y=y, name=name) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/op_def_library.py", line 794, in _apply_op_helper op_def=op_def) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/util/deprecation.py", line 507, in new_func return func(*args, **kwargs) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py", line 3357, in create_op attrs, op_def, compute_device) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py", line 3426, in _create_op_internal op_def=op_def) File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py", line 1748, in __init__ self._traceback = tf_stack.extract_stack()

Here is the code (which is slightly different from the repo in order to get it to run:

Versions: Python 3

tensorflow == 1.15.0

pandas == 0.25.3

numpy == 1.17.5

 import glob import pandas as pd import tensorflow as tf import pandas as pd import numpy as np # preprocess data file_list = [] for f in glob.glob('swda/*'): file_list.append(f) df_list = [] for i in file_list: df = pd.read_csv(i) df_list.append(df) text_list = [] label_list = [] for df in df_list: df['utterance_no_specialchar_'] = df.utterance_no_specialchar.astype(str) text = df.utterance_no_specialchar_.tolist() labels = df.da_category.tolist() text_list.append(text) label_list.append(labels) ### new preprocessing step text_list = [[[j] for j in i] for i in text_list] tok_data = [y[0] for x in text_list for y in x] tokenizer = tf.keras.preprocessing.text.Tokenizer() tokenizer.fit_on_texts(tok_data) sequences = [] for x in text_list: tmp = [] for y in x: tmp.append(tokenizer.texts_to_sequences(y)[0]) sequences.append(tmp) def _pad_sequences(sequences, pad_tok, max_length): """ Args: sequences: a generator of list or tuple pad_tok: the char to pad with Returns: a list of list where each sublist has same length """ sequence_padded, sequence_length = [], [] for seq in sequences: seq = list(seq) seq_ = seq[:max_length] + [pad_tok]*max(max_length - len(seq), 0) sequence_padded += [seq_] sequence_length += [min(len(seq), max_length)] return sequence_padded, sequence_length def pad_sequences(sequences, pad_tok, nlevels=1): """ Args: sequences: a generator of list or tuple pad_tok: the char to pad with nlevels: "depth" of padding, for the case where we have characters ids Returns: a list of list where each sublist has same length """ if nlevels == 1: max_length = max(map(lambda x : len(x), sequences)) sequence_padded, sequence_length = _pad_sequences(sequences, pad_tok, max_length) elif nlevels == 2: max_length_word = max([max(map(lambda x: len(x), seq)) for seq in sequences]) sequence_padded, sequence_length = [], [] for seq in sequences: # all words are same length now sp, sl = _pad_sequences(seq, pad_tok, max_length_word) sequence_padded += [sp] sequence_length += [sl] max_length_sentence = max(map(lambda x : len(x), sequences)) sequence_padded, _ = _pad_sequences(sequence_padded, [pad_tok]*max_length_word, max_length_sentence) sequence_length, _ = _pad_sequences(sequence_length, 0, max_length_sentence) return sequence_padded, sequence_length def minibatches(data, labels, batch_size): data_size = len(data) start_index = 0 num_batches_per_epoch = int((len(data) + batch_size - 1) / batch_size) for batch_num in range(num_batches_per_epoch): start_index = batch_num * batch_size end_index = min((batch_num + 1) * batch_size, data_size) yield data[start_index: end_index], labels[start_index: end_index] def select(parameters, length): """Select the last valid time step output as the sentence embedding :params parameters: [batch, seq_len, hidden_dims] :params length: [batch] :Returns : [batch, hidden_dims] """ shape = tf.shape(parameters) idx = tf.range(shape[0]) idx = tf.stack([idx, length - 1], axis = 1) return tf.gather_nd(parameters, idx) class DAModel(): def __init__(self): with tf.variable_scope("placeholder"): self.dialogue_lengths = tf.placeholder(tf.int32, shape = [None], name = "dialogue_lengths") self.word_ids = tf.placeholder(tf.int32, shape = [None,None,None], name = "word_ids") self.utterance_lengths = tf.placeholder(tf.int32, shape = [None, None], name = "utterance_lengths") self.labels = tf.placeholder(tf.int32, shape = [None, None], name = "labels") self.clip = tf.placeholder(tf.float32, shape = [], name = 'clip') ######################## EMBEDDINGS ########################################### with tf.variable_scope("embeddings"): _word_embeddings = tf.get_variable( name = "_word_embeddings", dtype = tf.float32, shape = [words, word_dim], initializer = tf.random_uniform_initializer() ) word_embeddings = tf.nn.embedding_lookup(_word_embeddings,self.word_ids, name="word_embeddings") self.word_embeddings = tf.nn.dropout(word_embeddings, 0.8) with tf.variable_scope("utterance_encoder"): s = tf.shape(self.word_embeddings) batch_size = s[0] * s[1] time_step = s[-2] word_embeddings = tf.reshape(self.word_embeddings, [batch_size, time_step, word_dim]) length = tf.reshape(self.utterance_lengths, [batch_size]) fw = tf.nn.rnn_cell.LSTMCell(hidden_size_lstm_1, forget_bias=0.8, state_is_tuple= True) bw = tf.nn.rnn_cell.LSTMCell(hidden_size_lstm_1, forget_bias=0.8, state_is_tuple= True) output, _ = tf.nn.bidirectional_dynamic_rnn(fw, bw, word_embeddings,sequence_length=length, dtype = tf.float32) output = tf.concat(output, axis = -1) # [batch_size, time_step, dim] # Select the last valid time step output as the utterance embedding, # this method is more concise than TensorArray with while_loop # output = select(output, self.utterance_lengths) # [batch_size, dim] output = select(output, length) # [batch_size, dim] # output = tf.reshape(output, s[0], s[1], 2 * hidden_size_lstm_1) output = tf.reshape(output, [s[0], s[1], 2 * hidden_size_lstm_1]) output = tf.nn.dropout(output, 0.8) with tf.variable_scope("bi-lstm"): cell_fw = tf.contrib.rnn.BasicLSTMCell(hidden_size_lstm_2, state_is_tuple = True) cell_bw = tf.contrib.rnn.BasicLSTMCell(hidden_size_lstm_2, state_is_tuple = True) (output_fw, output_bw), _ = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, output, sequence_length = self.dialogue_lengths, dtype = tf.float32) outputs = tf.concat([output_fw, output_bw], axis = -1) outputs = tf.nn.dropout(outputs, 0.8) with tf.variable_scope("proj1"): output = tf.reshape(outputs, [-1, 2 * hidden_size_lstm_2]) W = tf.get_variable("W", dtype = tf.float32, shape = [2 * hidden_size_lstm_2, proj1], initializer= tf.contrib.layers.xavier_initializer()) b = tf.get_variable("b", dtype = tf.float32, shape = [proj1], initializer=tf.zeros_initializer()) output = tf.nn.relu(tf.matmul(output, W) + b) with tf.variable_scope("proj2"): W = tf.get_variable("W", dtype = tf.float32, shape = [proj1, proj2], initializer= tf.contrib.layers.xavier_initializer()) b = tf.get_variable("b", dtype = tf.float32, shape = [proj2], initializer=tf.zeros_initializer()) output = tf.nn.relu(tf.matmul(output, W) + b) with tf.variable_scope("logits"): nstep = tf.shape(outputs)[1] W = tf.get_variable("W", dtype = tf.float32,shape=[proj2, tags], initializer = tf.random_uniform_initializer()) b = tf.get_variable("b", dtype = tf.float32,shape = [tags],initializer=tf.zeros_initializer()) pred = tf.matmul(output, W) + b self.logits = tf.reshape(pred, [-1, nstep, tags]) with tf.variable_scope("loss"): log_likelihood, self.trans_params = tf.contrib.crf.crf_log_likelihood( self.logits, self.labels, self.dialogue_lengths) self.loss = tf.reduce_mean(-log_likelihood) + tf.nn.l2_loss(W) + tf.nn.l2_loss(b) #tf.summary.scalar("loss", self.loss) with tf.variable_scope("viterbi_decode"): viterbi_sequence, _ = tf.contrib.crf.crf_decode(self.logits, self.trans_params, self.dialogue_lengths) batch_size = tf.shape(self.dialogue_lengths)[0] output_ta = tf.TensorArray(dtype = tf.float32, size = 1, dynamic_size = True) def body(time, output_ta_1): length = self.dialogue_lengths[time] vcode = viterbi_sequence[time][:length] true_labs = self.labels[time][:length] accurate = tf.reduce_sum(tf.cast(tf.equal(vcode, true_labs), tf.float32)) output_ta_1 = output_ta_1.write(time, accurate) return time + 1, output_ta_1 def condition(time, output_ta_1): return time < batch_size i = 0 [time, output_ta] = tf.while_loop(condition, body, loop_vars = [i, output_ta]) output_ta = output_ta.stack() accuracy = tf.reduce_sum(output_ta) self.accuracy = accuracy / tf.reduce_sum(tf.cast(self.dialogue_lengths, tf.float32)) #tf.summary.scalar("accuracy", self.accuracy) with tf.variable_scope("train_op"): optimizer = tf.train.AdagradOptimizer(0.1) #if tf.greater(self.clip , 0): grads, vs = zip(*optimizer.compute_gradients(self.loss)) grads, gnorm = tf.clip_by_global_norm(grads, self.clip) self.train_op = optimizer.apply_gradients(zip(grads, vs)) #else: # self.train_op = optimizer.minimize(self.loss) #self.merged = tf.summary.merge_all() ### Set model variables hidden_size_lstm_1 = 200 hidden_size_lstm_2 = 200 tags = 39 # assuming number of classes to predict? word_dim = 300 proj1 = 200 proj2 = 100 words = 20001 # words = 8759 + 1 # max(num_unique_word_tokens) batchSize = 2 log_dir = "train" model_dir = "DAModel" model_name = "ckpt" ### Run model def main(): # tokenize and vectorize text data to prepare for embedding train_data = sequences[:75] train_labels = label_list[:75] dev_data = sequences[75:] dev_labels = label_list[75:] config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.4 with tf.Session(config = config) as sess: model = DAModel() sess.run(tf.global_variables_initializer()) clip = 2 saver = tf.train.Saver() #writer = tf.summary.FileWriter("D:\\\\Experimemts\\\\tensorflow\\\\DA\\\\train", sess.graph) writer = tf.summary.FileWriter("train", sess.graph) counter = 0 for epoch in range(10): for dialogues, labels in minibatches(train_data, train_labels, batchSize): _, dialogue_lengthss = pad_sequences(dialogues, 0) word_idss, utterance_lengthss = pad_sequences(dialogues, 0, nlevels = 2) true_labs = labels labs_t, _ = pad_sequences(true_labs, 0) counter += 1 train_loss, train_accuracy, _ = sess.run([model.loss, model.accuracy,model.train_op], feed_dict = {model.word_ids: word_idss, model.utterance_lengths: utterance_lengthss, model.dialogue_lengths: dialogue_lengthss, model.labels:labs_t, model.clip :clip} ) #writer.add_summary(summary, global_step = counter) print("step = {}, train_loss = {}, train_accuracy = {}".format(counter, train_loss, train_accuracy)) train_precision_summ = tf.Summary() train_precision_summ.value.add( tag='train_accuracy', simple_value=train_accuracy) writer.add_summary(train_precision_summ, counter) train_loss_summ = tf.Summary() train_loss_summ.value.add( tag='train_loss', simple_value=train_loss) writer.add_summary(train_loss_summ, counter) if counter % 1 == 0: loss_dev = [] acc_dev = [] for dev_dialogues, dev_labels in minibatches(dev_data, dev_labels, batchSize): _, dialogue_lengthss = pad_sequences(dev_dialogues, 0) word_idss, utterance_lengthss = pad_sequences(dev_dialogues, 0, nlevels = 2) true_labs = dev_labels labs_t, _ = pad_sequences(true_labs, 0) dev_loss, dev_accuacy = sess.run([model.loss, model.accuracy], feed_dict = {model.word_ids: word_idss, model.utterance_lengths: utterance_lengthss, model.dialogue_lengths: dialogue_lengthss, model.labels:labs_t}) loss_dev.append(dev_loss) acc_dev.append(dev_accuacy) valid_loss = sum(loss_dev) / len(loss_dev) valid_accuracy = sum(acc_dev) / len(acc_dev) dev_precision_summ = tf.Summary() dev_precision_summ.value.add( tag='dev_accuracy', simple_value=valid_accuracy) writer.add_summary(dev_precision_summ, counter) dev_loss_summ = tf.Summary() dev_loss_summ.value.add( tag='dev_loss', simple_value=valid_loss) writer.add_summary(dev_loss_summ, counter) print("counter = {}, dev_loss = {}, dev_accuacy = {}".format(counter, valid_loss, valid_accuracy)) if __name__ == "__main__": tf.reset_default_graph() main()

The data comes from here and looks like this:

 [[['what '], ['do you want to start '], ['f uh laughter you hit you hit f uh '], ['it doesnt matter '], ['f um were discussing the capital punishment i believe '], ['right '], ['you are right '], ['yeah '], [' ii suppose i should have '], ['f uh which '], ['i am am pro capital punishment except that i dont like the way its done '], ['uhhuh '], ['f uh yeah '], ['f uh if uh i guess ii hate to see anyone die f uh '] ... ]]

The dataset to train the model can be found here: https://github.com/cmeaton/Hierarchical_BiLSTM-CRF_Encoder/tree/master/swda_parsed

I'm having a hard time understanding what this error even means and how to approach understanding it. Any advice would be much appreciated. Thanks.

Answer 1

Introduction

I think the main problem is a data mismatch in the sizes of the arrays (or matrixes or other structure) you are feeding sess.run . Specifically when you are calling:

train_loss, train_accuracy, _ = sess.run([model.loss, model.accuracy,model.train_op], feed_dict = {model.word_ids: word_idss, model.utterance_lengths: utterance_lengthss, model.dialogue_lengths: dialogue_lengthss, model.labels:labs_t, model.clip :clip} )

And more specifically, this error here hints that it's a mismatch problem:

tensorflow.python.framework.errors_impl.InvalidArgumentError: 
indices[317] = [317, -1] does not index into param shape [318,39,400]
             [[{{node utterance_encoder/GatherNd}}]]

I considered maybe that running on a fresh install might result in a error-free run.

I am getting similar errors but also a whole list of warnings. Please note I am running on windows 7 and using python 3.6.1.

---

Versions

I have tried the following tensorflow versions but with no success:

• tf 1.15
• tf 1.14
• tf 1.13.1
• tf 1.12
• tf 1.11
• tf 1.10
• tf 1.10 with downgraded keras to 2.2.1

---

Steps

• Installed python 3.6.1 (supported version of tensorflow). Installed for All Users. Set the Path. Installed in C:\\Python36
• pip3 install --user --upgrade tensorflow==1.15
• pip3 install --user --upgrade pandas == 0.25.3
• pip3 install --user --upgrade numpy == 1.17.5
• Download the following: https://github.com/cmeaton/Hierarchical_BiLSTM-CRF_Encoder/tree/master/swda_parsed
• Run the provided code

---

Result (Includes Many Warnings)

I think the following might be important:

tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[317] = [317, -1] does not index into param shape [318,39,400]
         [[{{node utterance_encoder/GatherNd}}]]

Full Trace

WARNING:tensorflow:From test.py:313: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.

WARNING:tensorflow:From test.py:256: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

WARNING:tensorflow:From test.py:259: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2020-01-31 12:13:10.096283: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
WARNING:tensorflow:From test.py:119: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

WARNING:tensorflow:From test.py:121: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From test.py:130: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.

WARNING:tensorflow:From test.py:137: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
WARNING:tensorflow:From test.py:147: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:From test.py:150: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API
WARNING:tensorflow:From D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\ops\rnn.py:464: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.RNN(cell)`, which is equivalent to this API
WARNING:tensorflow:From D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\ops\rnn_cell_impl.py:958: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.
WARNING:tensorflow:From D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\ops\rnn_cell_impl.py:962: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\ops\rnn.py:244: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From test.py:163: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:From test.py:223: The name tf.train.AdagradOptimizer is deprecated. Please use tf.compat.v1.train.AdagradOptimizer instead.

WARNING:tensorflow:From D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\training\adagrad.py:76: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From test.py:261: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.

WARNING:tensorflow:From test.py:263: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

WARNING:tensorflow:From test.py:265: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

2020-01-31 12:13:16.563989: W tensorflow/core/framework/op_kernel.cc:1651] OP_REQUIRES failed at gather_nd_op.cc:47 : Invalid argument: indices[317] = [317, -1] does not index into param shape [318,39,400]
Traceback (most recent call last):
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\client\session.py", line 1365, in _do_call
    return fn(*args)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\client\session.py", line 1350, in _run_fn
    target_list, run_metadata)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\client\session.py", line 1443, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[317] = [317, -1] does not index into param shape [318,39,400]
         [[{{node utterance_encoder/GatherNd}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "test.py", line 314, in <module>
    main()
  File "test.py", line 274, in main
    train_loss, train_accuracy, _ = sess.run([model.loss, model.accuracy,model.train_op], feed_dict = {model.word_ids: word_idss, model.utterance_lengths: utterance_lengthss, model.dialogue_lengths: dialogue_lengthss, model.labels:labs_t, model.clip :clip} )
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\client\session.py", line 956, in run
    run_metadata_ptr)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\client\session.py", line 1180, in _run
    feed_dict_tensor, options, run_metadata)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\client\session.py", line 1359, in _do_run
    run_metadata)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\client\session.py", line 1384, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[317] = [317, -1] does not index into param shape [318,39,400]
         [[node utterance_encoder/GatherNd (defined at D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\framework\ops.py:1748) ]]

Original stack trace for 'utterance_encoder/GatherNd':
  File "test.py", line 314, in <module>
    main()
  File "test.py", line 260, in main
    model = DAModel()
  File "test.py", line 155, in __init__
    output = select(output, length) # [batch_size, dim]
  File "test.py", line 114, in select
    return tf.gather_nd(parameters, idx)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\util\dispatch.py", line 180, in wrapper
    return target(*args, **kwargs)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\ops\array_ops.py", line 4277, in gather_nd
    return gen_array_ops.gather_nd(params, indices, name=name)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\ops\gen_array_ops.py", line 3975, in gather_nd
    "GatherNd", params=params, indices=indices, name=name)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\framework\op_def_library.py", line 794, in _apply_op_helper
    op_def=op_def)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\util\deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\framework\ops.py", line 3357, in create_op
    attrs, op_def, compute_device)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\framework\ops.py", line 3426, in _create_op_internal
    op_def=op_def)
  File "D:\Users\bakopme\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\framework\ops.py", line 1748, in __init__
    self._traceback = tf_stack.extract_stack()

Answer 2

Let's focus on the error:

Invalid argument: Incompatible shapes: [2,185] vs. [2,229]

The problem seems to be that an operation between two tensors fails, because their shapes are incompatible.

---

It's possible that the tensorflow version you've selected is less permissive than the one used by the author.

According to this issue , the author guesses he used tensorflow==1.8 .

So first I would suggest you try to use this earlier version, or others before\\after that (1.7, 1.9, 1.10 etc).

---

Also, earlier versions may not have the keras package integrated to them as it is today, so you may want to use a specific keras version as well.

For example according to this issue , what helped was to downgrade to keras==2.2.2 .

---

If that doesn't help, maybe one of these will: 1 , 2 , 3 , 4 , 5 , 6