在Spyder界面中使用tensorflow进行fashion_mnist数据集学习,结果loss为非数,并且准确率一直未变

1.建立了一个3个全连接层的神经网络;
2.代码如下:

import matplotlib as mpl
import matplotlib.pyplot as plt
#%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf

from tensorflow import keras

print(tf.__version__)

print(sys.version_info)
for module in mpl, np, sklearn, tf, keras:
    print(module.__name__,module.__version__)

fashion_mnist  = keras.datasets.fashion_mnist
(x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()
x_valid, x_train = x_train_all[:5000], x_train_all[5000:]
y_valid, y_train = y_train_all[:5000], y_train_all[5000:]



#tf.keras.models.Sequential
model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape= [28,28]))
model.add(keras.layers.Dense(300, activation="relu"))
model.add(keras.layers.Dense(100, activation="relu"))
model.add(keras.layers.Dense(10,activation="softmax"))
###sparse为最后输出为index类型,如果为one hot类型,则不需加sparse
model.compile(loss = "sparse_categorical_crossentropy",optimizer = "sgd", metrics = ["accuracy"])

#model.layers
#model.summary()
history = model.fit(x_train, y_train, epochs=10, validation_data=(x_valid,y_valid))

3.输出结果:

runfile('F:/new/new world/deep learning/tensorflow/ex2/tf_keras_classification_model.py', wdir='F:/new/new world/deep learning/tensorflow/ex2')
2.0.0
sys.version_info(major=3, minor=7, micro=4, releaselevel='final', serial=0)
matplotlib 3.1.1
numpy 1.16.5
sklearn 0.21.3
tensorflow 2.0.0
tensorflow_core.keras 2.2.4-tf
Train on 55000 samples, validate on 5000 samples
Epoch 1/10
WARNING:tensorflow:Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x0000025EAB633798> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: 
WARNING: Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x0000025EAB633798> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: 
55000/55000 [==============================] - 3s 58us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
Epoch 2/10
55000/55000 [==============================] - 3s 48us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
Epoch 3/10
55000/55000 [==============================] - 3s 47us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
Epoch 4/10
55000/55000 [==============================] - 3s 48us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
Epoch 5/10
55000/55000 [==============================] - 3s 47us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
Epoch 6/10
55000/55000 [==============================] - 3s 48us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
Epoch 7/10
55000/55000 [==============================] - 3s 47us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
Epoch 8/10
55000/55000 [==============================] - 3s 48us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
Epoch 9/10
55000/55000 [==============================] - 3s 48us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
Epoch 10/10
55000/55000 [==============================] - 3s 48us/sample - loss: nan - accuracy: 0.1008 - val_loss: nan - val_accuracy: 0.0914
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877, in run run_metadata_ptr) File "D:\soft\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1100, in _run feed_dict_tensor, options, run_metadata) File "D:\soft\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1272, in _do_run run_metadata) File "D:\soft\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1291, in _do_call raise type(e)(node_def, op, message) InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_3' with dtype float and shape [?,10] [[Node: Placeholder_3 = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]] [[Node: Mean_3/_29 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_18_Mean_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]] Caused by op 'Placeholder_3', defined at: File "D:\soft\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 268, in <module> main() File "D:\soft\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 264, in main kernel.start() File "D:\soft\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 478, in start self.io_loop.start() File "D:\soft\Anaconda3\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start super(ZMQIOLoop, self).start() File "D:\soft\Anaconda3\lib\site-packages\tornado\ioloop.py", line 888, in start handler_func(fd_obj, events) File "D:\soft\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper return fn(*args, **kwargs) File "D:\soft\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events self._handle_recv() File "D:\soft\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv self._run_callback(callback, msg) File "D:\soft\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback callback(*args, **kwargs) File "D:\soft\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper return fn(*args, **kwargs) File "D:\soft\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher return self.dispatch_shell(stream, msg) File "D:\soft\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 233, in dispatch_shell handler(stream, idents, msg) File "D:\soft\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request user_expressions, allow_stdin) File "D:\soft\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 208, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "D:\soft\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 537, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "D:\soft\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2728, in run_cell interactivity=interactivity, compiler=compiler, result=result) File "D:\soft\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2856, in run_ast_nodes if self.run_code(code, result): File "D:\soft\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2910, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-3b25b2404fa0>", line 1, in <module> runfile('D:/project/Spyder/MNIST_data.py', wdir='D:/project/Spyder') File "D:\soft\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile execfile(filename, namespace) File "D:\soft\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile exec(compile(f.read(), filename, 'exec'), namespace) File "D:/project/Spyder/MNIST_data.py", line 20, in <module> y_=tf.placeholder(tf.float32,[None,10]) File "D:\soft\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1735, in placeholder return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name) File "D:\soft\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 5928, in placeholder "Placeholder", dtype=dtype, shape=shape, name=name) File "D:\soft\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "D:\soft\Anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py", line 454, in new_func return func(*args, **kwargs) File "D:\soft\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3155, in create_op op_def=op_def) File "D:\soft\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1717, in __init__ self._traceback = tf_stack.extract_stack() InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_3' with dtype float and shape [?,10] [[Node: Placeholder_3 = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]] [[Node: Mean_3/_29 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_18_Mean_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
用spyder跑mnist报错显示
ModuleNotFoundError: No module named 'mnist_inference' 但是我已经在目录下加了这个程序
Anaconda的tensorflow下spyder错误:程序停止 不显示异常
Anaconda的tensorflow下spyder错误 求助,为什么程序没问题 总是出现这种错误 做线性回归的时候是没问题的 debug是第一个图中圈出来的部分处产生的错误,求解 然后就会出现Kernel died,restaring (这个程序之前执行时没问题的 重装系统之后 重新安装的 再执行mnist的程序就会出现这个问题 其他的都可以执行) 我觉得是在tensorflow的使用中,from tensorflow.examples.tutorials.mnist import input_data报错 ![图片说明](https://img-ask.csdn.net/upload/201901/15/1547523875_882023.png)![图片说明](https://img-ask.csdn.net/upload/201901/15/1547523887_569028.png)![图片说明](https://img-ask.csdn.net/upload/201901/15/1547523915_431063.png)
Tensorflow测试训练styleGAN时报错 No OpKernel was registered to support Op 'NcclAllReduce' with these attrs.
在测试官方StyleGAN。 运行官方与训练模型pretrained_example.py generate_figures.py 没有问题。GPU工作正常。 运行train.py时报错 尝试只用单个GPU训练时没有报错。 NcclAllReduce应该跟多GPU通信有关,不太了解。 InvalidArgumentError (see above for traceback): No OpKernel was registered to support Op 'NcclAllReduce' with these attrs. Registered devices: [CPU,GPU], Registered kernels: <no registered kernels> [[Node: TrainD/SumAcrossGPUs/NcclAllReduce = NcclAllReduce[T=DT_FLOAT, num_devices=2, reduction="sum", shared_name="c112", _device="/device:GPU:0"](GPU0/TrainD_grad/gradients/AddN_160)]] 经过多番google 尝试过 重启 conda install keras-gpu 重新安装tensorflow-gpu==1.10.0(跟官方版本保持一致) ``` …… Building TensorFlow graph... Setting up snapshot image grid... Setting up run dir... Training... Traceback (most recent call last): File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\client\session.py", line 1278, in _do_call return fn(*args) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\client\session.py", line 1263, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\client\session.py", line 1350, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError: No OpKernel was registered to support Op 'NcclAllReduce' with these attrs. Registered devices: [CPU,GPU], Registered kernels: <no registered kernels> [[Node: TrainD/SumAcrossGPUs/NcclAllReduce = NcclAllReduce[T=DT_FLOAT, num_devices=2, reduction="sum", shared_name="c112", _device="/device:GPU:0"](GPU0/TrainD_grad/gradients/AddN_160)]] During handling of the above exception, another exception occurred: Traceback (most recent call last): File "train.py", line 191, in <module> main() File "train.py", line 186, in main dnnlib.submit_run(**kwargs) File "E:\MachineLearning\stylegan-master\dnnlib\submission\submit.py", line 290, in submit_run run_wrapper(submit_config) File "E:\MachineLearning\stylegan-master\dnnlib\submission\submit.py", line 242, in run_wrapper util.call_func_by_name(func_name=submit_config.run_func_name, submit_config=submit_config, **submit_config.run_func_kwargs) File "E:\MachineLearning\stylegan-master\dnnlib\util.py", line 257, in call_func_by_name return func_obj(*args, **kwargs) File "E:\MachineLearning\stylegan-master\training\training_loop.py", line 230, in training_loop tflib.run([D_train_op, Gs_update_op], {lod_in: sched.lod, lrate_in: sched.D_lrate, minibatch_in: sched.minibatch}) File "E:\MachineLearning\stylegan-master\dnnlib\tflib\tfutil.py", line 26, in run return tf.get_default_session().run(*args, **kwargs) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\client\session.py", line 877, in run run_metadata_ptr) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\client\session.py", line 1100, in _run feed_dict_tensor, options, run_metadata) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\client\session.py", line 1272, in _do_run run_metadata) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\client\session.py", line 1291, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: No OpKernel was registered to support Op 'NcclAllReduce' with these attrs. Registered devices: [CPU,GPU], Registered kernels: <no registered kernels> [[Node: TrainD/SumAcrossGPUs/NcclAllReduce = NcclAllReduce[T=DT_FLOAT, num_devices=2, reduction="sum", shared_name="c112", _device="/device:GPU:0"](GPU0/TrainD_grad/gradients/AddN_160)]] Caused by op 'TrainD/SumAcrossGPUs/NcclAllReduce', defined at: File "train.py", line 191, in <module> main() File "train.py", line 186, in main dnnlib.submit_run(**kwargs) File "E:\MachineLearning\stylegan-master\dnnlib\submission\submit.py", line 290, in submit_run run_wrapper(submit_config) File "E:\MachineLearning\stylegan-master\dnnlib\submission\submit.py", line 242, in run_wrapper util.call_func_by_name(func_name=submit_config.run_func_name, submit_config=submit_config, **submit_config.run_func_kwargs) File "E:\MachineLearning\stylegan-master\dnnlib\util.py", line 257, in call_func_by_name return func_obj(*args, **kwargs) File "E:\MachineLearning\stylegan-master\training\training_loop.py", line 185, in training_loop D_train_op = D_opt.apply_updates() File "E:\MachineLearning\stylegan-master\dnnlib\tflib\optimizer.py", line 135, in apply_updates g = nccl_ops.all_sum(g) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\contrib\nccl\python\ops\nccl_ops.py", line 49, in all_sum return _apply_all_reduce('sum', tensors) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\contrib\nccl\python\ops\nccl_ops.py", line 230, in _apply_all_reduce shared_name=shared_name)) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\contrib\nccl\ops\gen_nccl_ops.py", line 59, in nccl_all_reduce num_devices=num_devices, shared_name=shared_name, name=name) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\util\deprecation.py", line 454, in new_func return func(*args, **kwargs) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\framework\ops.py", line 3156, in create_op op_def=op_def) File "d:\Users\admin\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\framework\ops.py", line 1718, in __init__ self._traceback = tf_stack.extract_stack() InvalidArgumentError (see above for traceback): No OpKernel was registered to support Op 'NcclAllReduce' with these attrs. Registered devices: [CPU,GPU], Registered kernels: <no registered kernels> [[Node: TrainD/SumAcrossGPUs/NcclAllReduce = NcclAllReduce[T=DT_FLOAT, num_devices=2, reduction="sum", shared_name="c112", _device="/device:GPU:0"](GPU0/TrainD_grad/gradients/AddN_160)]] ``` ``` #conda list: # Name Version Build Channel _tflow_select 2.1.0 gpu absl-py 0.8.1 pypi_0 pypi alabaster 0.7.12 py36_0 asn1crypto 1.2.0 py36_0 astor 0.8.0 pypi_0 pypi astroid 2.3.2 py36_0 attrs 19.3.0 py_0 babel 2.7.0 py_0 backcall 0.1.0 py36_0 blas 1.0 mkl bleach 3.1.0 py36_0 ca-certificates 2019.10.16 0 certifi 2019.9.11 py36_0 cffi 1.13.1 py36h7a1dbc1_0 chardet 3.0.4 py36_1003 cloudpickle 1.2.2 py_0 colorama 0.4.1 py36_0 cryptography 2.8 py36h7a1dbc1_0 cudatoolkit 9.0 1 cudnn 7.6.4 cuda9.0_0 decorator 4.4.1 py_0 defusedxml 0.6.0 py_0 django 2.2.7 pypi_0 pypi docutils 0.15.2 py36_0 entrypoints 0.3 py36_0 gast 0.3.2 py_0 grpcio 1.25.0 pypi_0 pypi h5py 2.9.0 py36h5e291fa_0 hdf5 1.10.4 h7ebc959_0 icc_rt 2019.0.0 h0cc432a_1 icu 58.2 ha66f8fd_1 idna 2.8 pypi_0 pypi image 1.5.27 pypi_0 pypi imagesize 1.1.0 py36_0 importlib_metadata 0.23 py36_0 intel-openmp 2019.4 245 ipykernel 5.1.3 py36h39e3cac_0 ipython 7.9.0 py36h39e3cac_0 ipython_genutils 0.2.0 py36h3c5d0ee_0 isort 4.3.21 py36_0 jedi 0.15.1 py36_0 jinja2 2.10.3 py_0 jpeg 9b hb83a4c4_2 jsonschema 3.1.1 py36_0 jupyter_client 5.3.4 py36_0 jupyter_core 4.6.1 py36_0 keras-applications 1.0.8 py_0 keras-base 2.2.4 py36_0 keras-gpu 2.2.4 0 keras-preprocessing 1.1.0 py_1 keyring 18.0.0 py36_0 lazy-object-proxy 1.4.3 py36he774522_0 libpng 1.6.37 h2a8f88b_0 libprotobuf 3.9.2 h7bd577a_0 libsodium 1.0.16 h9d3ae62_0 markdown 3.1.1 py36_0 markupsafe 1.1.1 py36he774522_0 mccabe 0.6.1 py36_1 mistune 0.8.4 py36he774522_0 mkl 2019.4 245 mkl-service 2.3.0 py36hb782905_0 mkl_fft 1.0.15 py36h14836fe_0 mkl_random 1.1.0 py36h675688f_0 more-itertools 7.2.0 py36_0 nbconvert 5.6.1 py36_0 nbformat 4.4.0 py36h3a5bc1b_0 numpy 1.17.3 py36h4ceb530_0 numpy-base 1.17.3 py36hc3f5095_0 numpydoc 0.9.1 py_0 openssl 1.1.1d he774522_3 packaging 19.2 py_0 pandoc 2.2.3.2 0 pandocfilters 1.4.2 py36_1 parso 0.5.1 py_0 pickleshare 0.7.5 py36_0 pillow 6.2.1 pypi_0 pypi pip 19.3.1 py36_0 prompt_toolkit 2.0.10 py_0 protobuf 3.10.0 pypi_0 pypi psutil 5.6.3 py36he774522_0 pycodestyle 2.5.0 py36_0 pycparser 2.19 py36_0 pyflakes 2.1.1 py36_0 pygments 2.4.2 py_0 pylint 2.4.3 py36_0 pyopenssl 19.0.0 py36_0 pyparsing 2.4.2 py_0 pyqt 5.9.2 py36h6538335_2 pyreadline 2.1 py36_1 pyrsistent 0.15.4 py36he774522_0 pysocks 1.7.1 py36_0 python 3.6.9 h5500b2f_0 python-dateutil 2.8.1 py_0 pytz 2019.3 py_0 pywin32 223 py36hfa6e2cd_1 pyyaml 5.1.2 py36he774522_0 pyzmq 18.1.0 py36ha925a31_0 qt 5.9.7 vc14h73c81de_0 qtawesome 0.6.0 py_0 qtconsole 4.5.5 py_0 qtpy 1.9.0 py_0 requests 2.22.0 py36_0 rope 0.14.0 py_0 scipy 1.3.1 py36h29ff71c_0 setuptools 39.1.0 pypi_0 pypi sip 4.19.8 py36h6538335_0 six 1.13.0 pypi_0 pypi snowballstemmer 2.0.0 py_0 sphinx 2.2.1 py_0 sphinxcontrib-applehelp 1.0.1 py_0 sphinxcontrib-devhelp 1.0.1 py_0 sphinxcontrib-htmlhelp 1.0.2 py_0 sphinxcontrib-jsmath 1.0.1 py_0 sphinxcontrib-qthelp 1.0.2 py_0 sphinxcontrib-serializinghtml 1.1.3 py_0 spyder 3.3.6 py36_0 spyder-kernels 0.5.2 py36_0 sqlite 3.30.1 he774522_0 sqlparse 0.3.0 pypi_0 pypi tensorboard 1.10.0 py36he025d50_0 tensorflow 1.10.0 gpu_py36h3514669_0 tensorflow-base 1.10.0 gpu_py36h6e53903_0 tensorflow-gpu 1.10.0 pypi_0 pypi termcolor 1.1.0 pypi_0 pypi testpath 0.4.2 py36_0 tornado 6.0.3 py36he774522_0 traitlets 4.3.3 py36_0 typed-ast 1.4.0 py36he774522_0 urllib3 1.25.6 pypi_0 pypi vc 14.1 h0510ff6_4 vs2015_runtime 14.16.27012 hf0eaf9b_0 wcwidth 0.1.7 py36h3d5aa90_0 webencodings 0.5.1 py36_1 werkzeug 0.16.0 py_0 wheel 0.33.6 py36_0 win_inet_pton 1.1.0 py36_0 wincertstore 0.2 py36h7fe50ca_0 wrapt 1.11.2 py36he774522_0 yaml 0.1.7 hc54c509_2 zeromq 4.3.1 h33f27b4_3 zipp 0.6.0 py_0 zlib 1.2.11 h62dcd97_3 ``` 2*RTX2080Ti driver 4.19.67
装了tensorflow以后spyder的代码提示就没有了
我的环境是win10,anaconda3.5,python3.6.3,tensorflow1.4 以前的情况非常好,现在装了tensorflow以后spyder的代码编辑区就没有代码提示了,我全部重装以后出现了同样的情况,请问这是什么原因??
tensorflow 里loss 出现nan问题 新手问题
大家好, 新手刚刚学,IDE:spyder 预测一个停车场车辆的驶出率, 输入(时间和车辆驶入率)二维。 训练的数据就是,驶出率car/min,时间(时间:1代表一天,从凌晨10秒=10/24/3600的时候开始到晚上23点多),驶入率car/min, 只建了一层的hidden layer,然后print loss是都是nan... 不知道哪里出了问题,是因为层太简单了么?还是激活函数有问题呢? 看网上说排除零的影响,我把输入数和输出数都+1,变得非零了也还是nan... 代码如下: ``` import tensorflow as tf import numpy as np import pandas as pd data=pd.read_csv('0831new.csv') date=data['date'] erate=data['erate'] x=pd.concat([date,erate],axis=1) drate=data['drate'] y=np.array(drate) x=np.array(x) y=y.reshape([7112,1]) x=x+1 y=y+1 z=[] def add_layer(inputs, in_size, out_size, activation_function=None): # add one more layer and return the output of this layer Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.01) Wx_plus_b = tf.matmul(inputs, Weights) + biases if activation_function is None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) return outputs xs = tf.placeholder(tf.float32, [None, 2]) ys = tf.placeholder(tf.float32, [None, 1]) l1 = add_layer(xs, 2, 5, activation_function=tf.nn.tanh) prediction = add_layer(l1,5, 1, activation_function=None) loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1,0])) train_step= tf.train.GradientDescentOptimizer(0.001).minimize(loss) if int((tf.__version__).split('.')[1]) < 12: init = tf.initialize_all_variables() else: init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) for i in range(1000): # training sess.run(train_step, feed_dict={xs: x, ys: y}) if i % 25 == 0: # to see the step improvement print('loss:',sess.run(loss, feed_dict={xs:x, ys:y})) z.append(loss) ``` ![图片说明](https://img-ask.csdn.net/upload/201909/20/1568984044_999083.png) 帮忙给件建议吧~ 谢谢
为什么同一个安装包在IDLE和Spyder中一个找得到一个找不到?
首先我安装了pymongo包 ![图片说明](https://img-ask.csdn.net/upload/201910/02/1569989537_284608.jpg) 然后在IDLE中能够import ![图片说明](https://img-ask.csdn.net/upload/201910/02/1569989619_96687.jpg) 但是在Spyder中显示查找不到 ![图片说明](https://img-ask.csdn.net/upload/201910/02/1569989681_346854.jpg) 而且我发现我安装的requests包在IDLE和Spyder里面显示的版本不同,IDLE中显示的是2.22.0,Spyder中显示的是2.21.0。 我是初学者,不太明白,还请大家指点,谢谢。
用spyder库pymysql调用mysql数据库时出现的ProgrammingError: (1007, 'Unknown error 1007')如何解决?
大一学生, 今天第一次使用pymysql ``` import pymysql conn = pymysql.connect(host='localhost',user='root',passwd='*******',charset='utf8') cursor = conn.cursor() dbName='test' sql = 'show databases' cursor.execute(sql) dbs = cursor.fetchall() for db in dbs: if dbName in db: cursor.execute('drop database '+dbName) break cursor.execute('create database ' +dbName) conn.select_db(dbName) ``` 用vscode调试到这里就运行不下去了 报错 ProgrammingError: (1007, 'Unknown error 1007') 有高手能帮帮忙吗?
spyder中python的缓存机制怎么不起作用?
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