CODEWITHSUNDEEP
pythontensorflowneural-networkdeep-learning
mikinoqwert
mikinoqwert

Reputation: 415

TensorFlow InvalidArgumentError/Value error occurs with small change of code

The code:

import numpy as np
import tensorflow as tf
import pandas as pd
from sklearn.model_selection import train_test_split

x_data = np.linspace(0, 1000000, 1000)
y_true = np.sin(x_data)
y_true += np.random.randn(len(x_data))




feature_columns = [tf.feature_column.numeric_column('x', shape=[1])]
estimator = tf.estimator.DNNRegressor(feature_columns=feature_columns, hidden_units=[10,10,10], optimizer=lambda:
                                  tf.train.AdamOptimizer(
                                      learning_rate=0.1
                                  ))


X_train, X_test, y_train, y_test = train_test_split(x_data, y_true,    test_size=0.3)

input_function = tf.estimator.inputs.numpy_input_fn({'x': X_train},y_train,
                                                batch_size=8,     num_epochs=None,
                                                shuffle=True)

train_input_function = tf.estimator.inputs.numpy_input_fn({'x': X_train},y_train,
                                                      batch_size=8, num_epochs=1000,
                                                      shuffle=False)
test_input_function = tf.estimator.inputs.numpy_input_fn({'x': X_test},y_test,
                                                     batch_size=8, num_epochs=1000,
                                                     shuffle=False)


estimator.train(input_fn=input_function, steps=1000)

train_metrics = estimator.evaluate(input_fn=train_input_function, steps=1000)
test_metrics = estimator.evaluate(input_fn=test_input_function, steps=1000)


print('TRAINING DATA METRICS')
print(train_metrics)
print()

print('TEST DATA METRICS')
print(test_metrics)
print()

Works very well. But if I change line y_true = np.sin(x_data) into y_true=tf.square(x_data), I get an error:

Traceback (most recent call last): File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1576, in _create_c_op c_op = c_api.TF_FinishOperation(op_desc) tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank 1 but is rank 2 for 'strided_slice' (op: 'StridedSlice') with input shapes: [1000], [1,700], [1,700], [1].

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "C:/Users/Admin/Documents/PycharmProjects/TF_API_2/api.py", line 21, in X_train, X_test, y_train, y_test = train_test_split(x_data, y_true, test_size=0.3) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\model_selection_split.py", line 2059, in train_test_split safe_indexing(a, test)) for a in arrays)) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\model_selection_split.py", line 2059, in safe_indexing(a, test)) for a in arrays)) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\utils__init__.py", line 162, in safe_indexing return X[indices] File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\array_ops.py", line 524, in _slice_helper name=name) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\array_ops.py", line 690, in strided_slice shrink_axis_mask=shrink_axis_mask) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 10187, in strided_slice name=name) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\util\deprecation.py", line 454, in new_func return func(*args, **kwargs) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 3155, in create_op op_def=op_def) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1731, in init control_input_ops) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1579, in _create_c_op raise ValueError(str(e)) ValueError: Shape must be rank 1 but is rank 2 for 'strided_slice' (op: 'StridedSlice') with input shapes: [1000], [1,700], [1,700], [1].

If I use **2 instead of tf.square, the code cannot compiled too, with error: ERROR:tensorflow:Model diverged with loss = NaN. Traceback (most recent call last):

File "C:/Users/Admin/Documents/PycharmProjects/TF_API_2/api.py", line 35, in estimator.train(input_fn=input_function, steps=1000) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 376, in train loss = self._train_model(input_fn, hooks, saving_listeners) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1145, in _train_model return self._train_model_default(input_fn, hooks, saving_listeners) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1173, in _train_model_default saving_listeners) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1451, in _train_with_estimator_spec _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss]) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\monitored_session.py", line 583, in run run_metadata=run_metadata) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\monitored_session.py", line 1059, in run run_metadata=run_metadata) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\monitored_session.py", line 1150, in run raise six.reraise(*original_exc_info) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\six.py", line 693, in reraise raise value File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\monitored_session.py", line 1135, in run return self._sess.run(*args, **kwargs) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\monitored_session.py", line 1215, in run run_metadata=run_metadata)) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\basic_session_run_hooks.py", line 635, in after_run raise NanLossDuringTrainingError tensorflow.python.training.basic_session_run_hooks.NanLossDuringTrainingError: NaN loss during training.

What is the problem with this one line [y_true = tf.square(x_data)]?

Upvotes: 0

Views: 2019

Answers (1)

cs95
cs95

Reputation: 403050

There are two distinct issues here:

#1, don't mix numpy and tensorflow operations together. Unless you're evaluating your graph in eager execution mode, they almost always never go together.

#2, when your network produces NaNs after a few iterations, that's usually a good sign you're running into numeric overflow/underflow. In this case, the culprit is x_data which has input too large. Either normalize it (0-1) or reduce the range of the generated data (how about np.random.randint?).

Upvotes: 1

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