关于python：TensorFlow InvalidArgumentError / Value错误，代码变化很小

TensorFlow InvalidArgumentError/Value error occurs with small change of code

代码：

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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()

效果很好。
但是，如果我将y_true = np.sin(x_data)行更改为y_true=tf.square(x_data)，我会收到错误：

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].

如果我使用** 2而不是tf.square，代码也无法编译，错误：
错误：tensorflow：模型与损失= NaN分歧。
Traceback(最近一次调用最后一次)：

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.

这一行[y_true = tf.square(x_data)]有什么问题？