linux - anaconda env 的 TensorFlow 问题

Question

我用 TensorFlow 制作了一个 CNN，以便在大学集群中使用它。 CNN 在我的 Mac 上运行良好，我有一个带 TensorFlow 1.10 的 Anaconda 环境，这里是我在工作环境中使用的所有包:

name: CNN_env
channels:
  - conda-forge
  - defaults
dependencies:
  - _ipyw_jlab_nb_ext_conf=0.1.0=py36h2fc01ae_0
  - absl-py=0.7.0=py36_0
  - alabaster=0.7.12=py36_0
  - anaconda=custom=py36ha4fed55_0
  - anaconda-client=1.6.14=py36_0
  - anaconda-navigator=1.9.6=py36_0
  - anaconda-project=0.8.2=py36h9ee5d53_0
  - appnope=0.1.0=py36hf537a9a_0
  - appscript=1.0.1=py36h9e71e49_1
  - asn1crypto=0.24.0=py36_0
  - astor=0.7.1=py_0
  - astroid=1.6.3=py36_0
  - astropy=3.0.2=py36h917ab60_1
  - attrs=18.1.0=py36_0
  - autopep8=1.4.3=py36_0
  - babel=2.5.3=py36_0
  - backcall=0.1.0=py36_0
  - backports=1.0=py36ha3c1827_1
  - backports.shutil_get_terminal_size=1.0.0=py36hd7a2ee4_2
  - beautifulsoup4=4.6.0=py36h72d3c9f_1
  - bitarray=0.8.1=py36h1de35cc_1
  - bkcharts=0.2=py36h073222e_0
  - blas=1.0=mkl
  - blaze=0.11.3=py36h02e7a37_0
  - bleach=2.1.3=py36_0
  - blosc=1.14.3=hd9629dc_0
  - bokeh=0.12.16=py36_0
  - boto=2.48.0=py36hdbc59ac_1
  - bottleneck=1.2.1=py36hbd380ad_0
  - bzip2=1.0.6=h1de35cc_5
  - c-ares=1.14.0=h470a237_0
  - ca-certificates=2019.1.23=0
  - certifi=2019.3.9=py36_0
  - cffi=1.11.5=py36h342bebf_0
  - chardet=3.0.4=py36h96c241c_1
  - click=6.7=py36hec950be_0
  - cloudpickle=0.5.3=py36_0
  - clyent=1.2.2=py36hae3ad88_0
  - colorama=0.3.9=py36hd29a30c_0
  - conda=4.6.8=py36_0
  - conda-build=3.10.5=py36_0
  - conda-env=2.6.0=h36134e3_0
  - conda-verify=2.0.0=py36he837df3_0
  - contextlib2=0.5.5=py36hd66e5e7_0
  - cryptography=2.6.1=py36ha12b0ac_0
  - curl=7.64.0=ha441bb4_2
  - cycler=0.10.0=py36hfc81398_0
  - cython=0.28.2=py36h1de35cc_0
  - cytoolz=0.9.0.1=py36h1de35cc_0
  - dask=0.17.5=py36_0
  - dask-core=0.17.5=py36_0
  - datashape=0.5.4=py36hfb22df8_0
  - dbus=1.13.2=h760590f_1
  - decorator=4.3.0=py36_0
  - distributed=1.21.8=py36_0
  - docutils=0.14=py36hbfde631_0
  - entrypoints=0.2.3=py36hd81d71f_2
  - et_xmlfile=1.0.1=py36h1315bdc_0
  - expat=2.2.5=hb8e80ba_0
  - fastcache=1.0.2=py36h1de35cc_2
  - filelock=3.0.4=py36_0
  - flask=1.0.2=py36_1
  - flask-cors=3.0.4=py36_0
  - freetype=2.8=h12048fb_1
  - gast=0.2.0=py_0
  - get_terminal_size=1.0.0=h7520d66_0
  - gettext=0.19.8.1=h15daf44_3
  - gevent=1.3.0=py36h1de35cc_0
  - glib=2.56.1=h35bc53a_0
  - glob2=0.6=py36h94c9186_0
  - gmp=6.1.2=hb37e062_1
  - gmpy2=2.0.8=py36hf9c35bd_2
  - greenlet=0.4.13=py36h1de35cc_0
  - grpcio=1.12.1=py36hd9629dc_0
  - h5py=2.7.1=py36ha8ecd60_2
  - hdf5=1.10.2=hfa1e0ec_1
  - heapdict=1.0.0=py36_2
  - html5lib=1.0.1=py36h2f9c1c0_0
  - icu=58.2=h4b95b61_1
  - idna=2.6=py36h8628d0a_1
  - imageio=2.3.0=py36_0
  - imagesize=1.0.0=py36_0
  - intel-openmp=2018.0.0=8
  - ipykernel=4.8.2=py36_0
  - ipython=6.4.0=py36_0
  - ipython_genutils=0.2.0=py36h241746c_0
  - ipywidgets=7.2.1=py36_0
  - isort=4.3.4=py36_0
  - itsdangerous=0.24=py36h49fbb8d_1
  - jbig=2.1=h4d881f8_0
  - jdcal=1.4=py36_0
  - jedi=0.12.0=py36_1
  - jinja2=2.10=py36hd36f9c5_0
  - jpeg=9b=he5867d9_2
  - jsonschema=2.6.0=py36hb385e00_0
  - jupyter=1.0.0=py36_4
  - jupyter_client=5.2.3=py36_0
  - jupyter_console=5.2.0=py36hccf5b1c_1
  - jupyter_core=4.4.0=py36h79cf704_0
  - jupyterlab=0.32.1=py36_0
  - jupyterlab_launcher=0.10.5=py36_0
  - kiwisolver=1.0.1=py36h792292d_0
  - krb5=1.16.1=hddcf347_7
  - lazy-object-proxy=1.3.1=py36h2fbbe47_0
  - libcurl=7.64.0=h051b688_2
  - libcxx=4.0.1=h579ed51_0
  - libcxxabi=4.0.1=hebd6815_0
  - libedit=3.1.20170329=hb402a30_2
  - libffi=3.2.1=h475c297_4
  - libgfortran=3.0.1=h93005f0_2
  - libiconv=1.15=hdd342a3_7
  - libpng=1.6.34=he12f830_0
  - libprotobuf=3.6.0=hd28b015_0
  - libsodium=1.0.16=h3efe00b_0
  - libssh2=1.8.0=ha12b0ac_4
  - libtiff=4.0.9=hcb84e12_1
  - libxml2=2.9.8=hab757c2_1
  - libxslt=1.1.32=hb819dd2_0
  - llvmlite=0.23.1=py36hc454e04_0
  - locket=0.2.0=py36hca03003_1
  - lxml=4.2.1=py36h7166777_0
  - lzo=2.10=h362108e_2
  - markdown=2.6.11=py_0
  - markupsafe=1.0=py36h3a1e703_1
  - matplotlib=2.2.2=py36ha7267d0_0
  - mccabe=0.6.1=py36hdaeb55d_0
  - mistune=0.8.3=py36h1de35cc_1
  - mkl=2018.0.2=1
  - mkl-service=1.1.2=py36h7ea6df4_4
  - mkl_fft=1.0.1=py36h917ab60_0
  - mkl_random=1.0.1=py36h78cc56f_0
  - more-itertools=4.1.0=py36_0
  - mpc=1.0.3=h7a72875_5
  - mpfr=3.1.5=h711e7fd_2
  - mpmath=1.0.0=py36hf1b8295_2
  - msgpack-python=0.5.6=py36h04f5b5a_0
  - multipledispatch=0.5.0=py36_0
  - navigator-updater=0.2.1=py36_0
  - nbconvert=5.3.1=py36h810822e_0
  - nbformat=4.4.0=py36h827af21_0
  - ncurses=6.1=h0a44026_0
  - networkx=2.1=py36_0
  - nltk=3.3.0=py36_0
  - nose=1.3.7=py36h73fae2b_2
  - notebook=5.5.0=py36_0
  - numba=0.38.0=py36h1702cab_0
  - numexpr=2.6.5=py36h057f876_0
  - numpy=1.14.3=py36h9bb19eb_1
  - numpy-base=1.14.3=py36h479e554_1
  - numpydoc=0.8.0=py36_0
  - odo=0.5.1=py36hc1af34a_0
  - olefile=0.45.1=py36_0
  - openpyxl=2.5.3=py36_0
  - openssl=1.1.1b=h1de35cc_1
  - packaging=17.1=py36_0
  - pandas=0.23.0=py36h1702cab_0
  - pandoc=1.19.2.1=ha5e8f32_1
  - pandocfilters=1.4.2=py36h3b0b094_1
  - parso=0.2.0=py36_0
  - partd=0.3.8=py36hf5c4cb8_0
  - path.py=11.0.1=py36_0
  - pathlib2=2.3.2=py36_0
  - patsy=0.5.0=py36_0
  - pcre=8.42=h378b8a2_0
  - pep8=1.7.1=py36_0
  - pexpect=4.5.0=py36_0
  - pickleshare=0.7.4=py36hf512f8e_0
  - pillow=5.1.0=py36hfcce615_0
  - pkginfo=1.4.2=py36_1
  - pluggy=0.6.0=py36hb1d0581_0
  - ply=3.11=py36_0
  - prompt_toolkit=1.0.15=py36haeda067_0
  - protobuf=3.6.0=py36hfc679d8_0
  - psutil=5.4.5=py36h1de35cc_0
  - ptyprocess=0.5.2=py36he6521c3_0
  - py=1.5.3=py36_0
  - pycodestyle=2.4.0=py36_0
  - pycosat=0.6.3=py36hee92d8f_0
  - pycparser=2.18=py36h724b2fc_1
  - pycrypto=2.6.1=py36h1de35cc_8
  - pycurl=7.43.0.2=py36ha12b0ac_0
  - pyflakes=1.6.0=py36hea45e83_0
  - pygments=2.2.0=py36h240cd3f_0
  - pylint=1.8.4=py36_0
  - pyodbc=4.0.23=py36h0a44026_0
  - pyopenssl=18.0.0=py36_0
  - pyparsing=2.2.0=py36hb281f35_0
  - pyqt=5.9.2=py36h11d3b92_0
  - pysocks=1.6.8=py36_0
  - pytables=3.4.3=py36h5ca999c_2
  - pytest=3.5.1=py36_0
  - pytest-arraydiff=0.2=py36_0
  - pytest-astropy=0.3.0=py36_0
  - pytest-doctestplus=0.1.3=py36_0
  - pytest-openfiles=0.3.0=py36_0
  - pytest-remotedata=0.2.1=py36_0
  - python=3.6.8=haf84260_0
  - python-dateutil=2.7.3=py36_0
  - python.app=2=py36_8
  - pytz=2018.4=py36_0
  - pywavelets=0.5.2=py36h2710a04_0
  - pyyaml=3.12=py36h2ba1e63_1
  - pyzmq=17.0.0=py36h1de35cc_1
  - qt=5.9.5=h02808f3_0
  - qtawesome=0.4.4=py36h468c6fb_0
  - qtconsole=4.3.1=py36hd96c0ff_0
  - qtpy=1.4.1=py36_0
  - readline=7.0=hc1231fa_4
  - requests=2.18.4=py36h4516966_1
  - rope=0.10.7=py36h68959ac_0
  - ruamel_yaml=0.15.35=py36h1de35cc_1
  - scikit-image=0.13.1=py36h1de35cc_1
  - scikit-learn=0.19.1=py36hffbff8c_0
  - scipy=1.1.0=py36hcaad992_0
  - seaborn=0.8.1=py36h595ecd9_0
  - send2trash=1.5.0=py36_0
  - setuptools=39.1.0=py36_0
  - simplegeneric=0.8.1=py36_2
  - singledispatch=3.4.0.3=py36hf20db9d_0
  - sip=4.19.8=py36h0a44026_0
  - six=1.11.0=py36h0e22d5e_1
  - snappy=1.1.7=he62c110_3
  - snowballstemmer=1.2.1=py36h6c7b616_0
  - sortedcollections=0.6.1=py36_0
  - sortedcontainers=1.5.10=py36_0
  - sphinx=1.7.4=py36_0
  - sphinxcontrib=1.0=py36h9364dc8_1
  - sphinxcontrib-websupport=1.0.1=py36h92f4a7a_1
  - spyder=3.2.8=py36_0
  - sqlalchemy=1.2.7=py36hb402a30_0
  - sqlite=3.26.0=ha441bb4_0
  - statsmodels=0.9.0=py36h917ab60_0
  - sympy=1.1.1=py36h7f3cf04_0
  - tblib=1.3.2=py36hda67792_0
  - tensorboard=1.10.0=py36_0
  - tensorflow=1.10.0=py36_0
  - termcolor=1.1.0=py_2
  - terminado=0.8.1=py36_1
  - testpath=0.3.1=py36h625a49b_0
  - tk=8.6.8=ha441bb4_0
  - toolz=0.9.0=py36_0
  - tornado=5.0.2=py36_0
  - traitlets=4.3.2=py36h65bd3ce_0
  - typing=3.6.4=py36_0
  - unicodecsv=0.14.1=py36he531d66_0
  - unixodbc=2.3.6=h3efe00b_0
  - urllib3=1.22=py36h68b9469_0
  - wcwidth=0.1.7=py36h8c6ec74_0
  - webencodings=0.5.1=py36h3b9701d_1
  - werkzeug=0.14.1=py36_0
  - wheel=0.31.1=py36_0
  - widgetsnbextension=3.2.1=py36_0
  - wrapt=1.10.11=py36hc29e774_0
  - xlrd=1.1.0=py36h336f4a2_1
  - xlsxwriter=1.0.4=py36_0
  - xlwings=0.11.8=py36_0
  - xlwt=1.2.0=py36h5ad1178_0
  - xz=5.2.4=h1de35cc_4
  - yaml=0.1.7=hc338f04_2
  - zeromq=4.2.5=h378b8a2_0
  - zict=0.1.3=py36h71da714_0
  - zlib=1.2.11=hf3cbc9b_2
  - pip:
    - enum34==1.1.6
    - pip==18.1
    - prettytensor==0.7.4
prefix: /Users/matteo/anaconda3

但如果我尝试在集群上创建相同的环境并运行相同的代码，它就不起作用，它给了我这个错误:

2019-03-22 13:48:57.392457: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-03-22 13:48:57.397469: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.

TRAINING MODE
2019-03-22 13:49:04.088494: E tensorflow/core/common_runtime/executor.cc:630] Executor failed to create kernel. Not found: No registered '_MklConv2DWithBias' OpKernel for CPU devices compatible with node {{node Conv1/conv2d/BiasAdd}} = _MklConv2DWithBias[T=DT_DOUBLE, _kernel="MklOp", data_format="NHWC", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_layer/Reshape, conv2d/kernel/read, conv2d/bias/read, DMT/_0, DMT/_1, DMT/_2)
     (OpKernel was found, but attributes didn't match)
    .  Registered:  device='CPU'; label='MklOp'; T in [DT_FLOAT]

     [[{{node Conv1/conv2d/BiasAdd}} = _MklConv2DWithBias[T=DT_DOUBLE, _kernel="MklOp", data_format="NHWC", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_layer/Reshape, conv2d/kernel/read, conv2d/bias/read, DMT/_0, DMT/_1, DMT/_2)]]
Traceback (most recent call last):
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1292, in _do_call
    return fn(*args)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1277, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1367, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.NotFoundError: No registered '_MklConv2DWithBias' OpKernel for CPU devices compatible with node {{node Conv1/conv2d/BiasAdd}} = _MklConv2DWithBias[T=DT_DOUBLE, _kernel="MklOp", data_format="NHWC", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_layer/Reshape, conv2d/kernel/read, conv2d/bias/read, DMT/_0, DMT/_1, DMT/_2)
     (OpKernel was found, but attributes didn't match)
    .  Registered:  device='CPU'; label='MklOp'; T in [DT_FLOAT]

     [[{{node Conv1/conv2d/BiasAdd}} = _MklConv2DWithBias[T=DT_DOUBLE, _kernel="MklOp", data_format="NHWC", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_layer/Reshape, conv2d/kernel/read, conv2d/bias/read, DMT/_0, DMT/_1, DMT/_2)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "cnn_mnist_beta5.py", line 146, in <module>
    sess.run(train_op)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 887, in run
    run_metadata_ptr)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1110, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1286, in _do_run
    run_metadata)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1308, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: No registered '_MklConv2DWithBias' OpKernel for CPU devices compatible with node {{node Conv1/conv2d/BiasAdd}} = _MklConv2DWithBias[T=DT_DOUBLE, _kernel="MklOp", data_format="NHWC", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_layer/Reshape, conv2d/kernel/read, conv2d/bias/read, DMT/_0, DMT/_1, DMT/_2)
     (OpKernel was found, but attributes didn't match)
    .  Registered:  device='CPU'; label='MklOp'; T in [DT_FLOAT]

     [[{{node Conv1/conv2d/BiasAdd}} = _MklConv2DWithBias[T=DT_DOUBLE, _kernel="MklOp", data_format="NHWC", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_layer/Reshape, conv2d/kernel/read, conv2d/bias/read, DMT/_0, DMT/_1, DMT/_2)]]

Caused by op 'Conv1/conv2d/BiasAdd', defined at:
  File "cnn_mnist_beta5.py", line 100, in <module>
    logits = cnn_model_fn(X_batch, MODE)
  File "/home/mdonato/cnn_genetic_beta5/cnn_model_fn_v2.py", line 28, in cnn_model_fn
    padding="valid",
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/layers/convolutional.py", line 417, in conv2d
    return layer.apply(inputs)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 828, in apply
    return self.__call__(inputs, *args, **kwargs)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 364, in __call__
    outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 769, in __call__
    outputs = self.call(inputs, *args, **kwargs)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/keras/layers/convolutional.py", line 210, in call
    outputs = nn.bias_add(outputs, self.bias, data_format='NHWC')
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 1507, in bias_add
    return gen_nn_ops.bias_add(value, bias, data_format=data_format, name=name)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 687, in bias_add
    "BiasAdd", value=value, bias=bias, data_format=data_format, name=name)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
    return func(*args, **kwargs)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3272, in create_op
    op_def=op_def)
  File "/home/mdonato/.conda/envs/cnn_genetic/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1768, in __init__
    self._traceback = tf_stack.extract_stack()

NotFoundError (see above for traceback): No registered '_MklConv2DWithBias' OpKernel for CPU devices compatible with node {{node Conv1/conv2d/BiasAdd}} = _MklConv2DWithBias[T=DT_DOUBLE, _kernel="MklOp", data_format="NHWC", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_layer/Reshape, conv2d/kernel/read, conv2d/bias/read, DMT/_0, DMT/_1, DMT/_2)
     (OpKernel was found, but attributes didn't match)
    .  Registered:  device='CPU'; label='MklOp'; T in [DT_FLOAT]

     [[{{node Conv1/conv2d/BiasAdd}} = _MklConv2DWithBias[T=DT_DOUBLE, _kernel="MklOp", data_format="NHWC", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_layer/Reshape, conv2d/kernel/read, conv2d/bias/read, DMT/_0, DMT/_1, DMT/_2)]]

这是集群环境的规范:

name: cnn_genetic
channels:
  - defaults
dependencies:
  - _tflow_select=2.3.0=mkl
  - absl-py=0.7.0=py36_0
  - alabaster=0.7.12=py36_0
  - anaconda-client=1.7.2=py36_0
  - anaconda=custom=py36hbbc8b67_0
  - anaconda-project=0.8.2=py36_0
  - asn1crypto=0.24.0=py36_0
  - astor=0.7.1=py36_0
  - astroid=2.2.5=py36_0
  - astropy=3.1.2=py36h7b6447c_0
  - atomicwrites=1.3.0=py_0
  - attrs=19.1.0=py_0
  - babel=2.6.0=py36_0
  - backcall=0.1.0=py36_0
  - backports=1.0=py36_1
  - backports.os=0.1.1=py36_0
  - backports.shutil_get_terminal_size=1.0.0=py36_2
  - beautifulsoup4=4.7.1=py36_1
  - bitarray=0.8.3=py36h14c3975_0
  - bkcharts=0.2=py36_0
  - blas=1.0=mkl
  - blaze=0.11.3=py36_0
  - bleach=3.1.0=py36_0
  - blosc=1.15.0=hd408876_0
  - bokeh=1.0.4=py36_0
  - boto=2.49.0=py36_0
  - bottleneck=1.2.1=py36h035aef0_1
  - bzip2=1.0.6=h14c3975_5
  - c-ares=1.15.0=h7b6447c_1
  - ca-certificates=2019.1.23=0
  - cairo=1.14.12=h8948797_3
  - certifi=2019.3.9=py36_0
  - cffi=1.12.2=py36h2e261b9_1
  - chardet=3.0.4=py36_1
  - click=7.0=py36_0
  - cloudpickle=0.8.0=py36_0
  - clyent=1.2.2=py36_1
  - colorama=0.4.1=py36_0
  - contextlib2=0.5.5=py36_0
  - cryptography=2.6.1=py36h1ba5d50_0
  - curl=7.64.0=hbc83047_2
  - cycler=0.10.0=py36_0
  - cython=0.29.6=py36he6710b0_0
  - cytoolz=0.9.0.1=py36h14c3975_1
  - dask=1.1.4=py_0
  - dask-core=1.1.4=py_0
  - datashape=0.5.4=py36_1
  - dbus=1.13.6=h746ee38_0
  - decorator=4.4.0=py_0
  - defusedxml=0.5.0=py36_1
  - distributed=1.26.0=py36_1
  - docutils=0.14=py36_0
  - entrypoints=0.3=py36_0
  - et_xmlfile=1.0.1=py36_0
  - expat=2.2.6=he6710b0_0
  - fastcache=1.0.2=py36h14c3975_2
  - filelock=3.0.10=py36_0
  - flask=1.0.2=py36_1
  - flask-cors=3.0.7=py36_0
  - fontconfig=2.13.0=h9420a91_0
  - freetype=2.9.1=h8a8886c_1
  - fribidi=1.0.5=h7b6447c_0
  - gast=0.2.2=py36_0
  - get_terminal_size=1.0.0=haa9412d_0
  - gevent=1.4.0=py36h7b6447c_0
  - glib=2.56.2=hd408876_0
  - glob2=0.6=py36_1
  - gmp=6.1.2=h6c8ec71_1
  - gmpy2=2.0.8=py36h10f8cd9_2
  - graphite2=1.3.13=h23475e2_0
  - greenlet=0.4.15=py36h7b6447c_0
  - grpcio=1.16.1=py36hf8bcb03_1
  - gst-plugins-base=1.14.0=hbbd80ab_1
  - gstreamer=1.14.0=hb453b48_1
  - h5py=2.9.0=py36h7918eee_0
  - harfbuzz=1.8.8=hffaf4a1_0
  - hdf5=1.10.4=hb1b8bf9_0
  - heapdict=1.0.0=py36_2
  - html5lib=1.0.1=py36_0
  - icu=58.2=h9c2bf20_1
  - idna=2.8=py36_0
  - imageio=2.5.0=py36_0
  - imagesize=1.1.0=py36_0
  - importlib_metadata=0.8=py36_0
  - intel-openmp=2019.3=199
  - ipykernel=5.1.0=py36h39e3cac_0
  - ipython=7.3.0=py36h39e3cac_0
  - ipython_genutils=0.2.0=py36_0
  - ipywidgets=7.4.2=py36_0
  - isort=4.3.15=py36_0
  - itsdangerous=1.1.0=py36_0
  - jbig=2.1=hdba287a_0
  - jdcal=1.4=py36_0
  - jedi=0.13.3=py36_0
  - jeepney=0.4=py36_0
  - jinja2=2.10=py36_0
  - jpeg=9b=h024ee3a_2
  - jsonschema=3.0.1=py36_0
  - jupyter=1.0.0=py36_7
  - jupyter_client=5.2.4=py36_0
  - jupyter_console=6.0.0=py36_0
  - jupyter_core=4.4.0=py36_0
  - jupyterlab=0.35.4=py36hf63ae98_0
  - jupyterlab_server=0.2.0=py36_0
  - keras-applications=1.0.7=py_0
  - keras-preprocessing=1.0.9=py_0
  - keyring=18.0.0=py36_0
  - kiwisolver=1.0.1=py36hf484d3e_0
  - krb5=1.16.1=h173b8e3_7
  - lazy-object-proxy=1.3.1=py36h14c3975_2
  - libarchive=3.3.3=h5d8350f_5
  - libcurl=7.64.0=h20c2e04_2
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hd88cf55_4
  - libgcc-ng=8.2.0=hdf63c60_1
  - libgfortran-ng=7.3.0=hdf63c60_0
  - liblief=0.9.0=h7725739_2
  - libpng=1.6.36=hbc83047_0
  - libprotobuf=3.6.1=hd408876_0
  - libsodium=1.0.16=h1bed415_0
  - libssh2=1.8.0=h1ba5d50_4
  - libstdcxx-ng=8.2.0=hdf63c60_1
  - libtiff=4.0.10=h2733197_2
  - libtool=2.4.6=h7b6447c_5
  - libuuid=1.0.3=h1bed415_2
  - libxcb=1.13=h1bed415_1
  - libxml2=2.9.9=he19cac6_0
  - libxslt=1.1.33=h7d1a2b0_0
  - llvmlite=0.28.0=py36hd408876_0
  - locket=0.2.0=py36_1
  - lxml=4.3.2=py36hefd8a0e_0
  - lz4-c=1.8.1.2=h14c3975_0
  - lzo=2.10=h49e0be7_2
  - markdown=3.0.1=py36_0
  - markupsafe=1.1.1=py36h7b6447c_0
  - matplotlib=3.0.3=py36h5429711_0
  - mccabe=0.6.1=py36_1
  - mistune=0.8.4=py36h7b6447c_0
  - mkl=2019.3=199
  - mkl-service=1.1.2=py36he904b0f_5
  - mkl_fft=1.0.10=py36ha843d7b_0
  - mkl_random=1.0.2=py36hd81dba3_0
  - more-itertools=6.0.0=py36_0
  - mpc=1.1.0=h10f8cd9_1
  - mpfr=4.0.1=hdf1c602_3
  - mpmath=1.1.0=py36_0
  - msgpack-python=0.6.1=py36hfd86e86_1
  - multipledispatch=0.6.0=py36_0
  - nbconvert=5.4.1=py_2
  - nbformat=4.4.0=py36_0
  - ncurses=6.1=he6710b0_1
  - networkx=2.2=py36_1
  - nltk=3.4=py36_1
  - nose=1.3.7=py36_2
  - notebook=5.7.6=py36_0
  - numba=0.43.0=py36h962f231_0
  - numexpr=2.6.9=py36h9e4a6bb_0
  - numpy=1.16.2=py36h7e9f1db_0
  - numpy-base=1.16.2=py36hde5b4d6_0
  - numpydoc=0.8.0=py36_0
  - odo=0.5.1=py36_0
  - olefile=0.46=py36_0
  - openpyxl=2.6.1=py_0
  - openssl=1.1.1b=h7b6447c_1
  - packaging=19.0=py36_0
  - pandas=0.24.2=py36he6710b0_0
  - pandoc=2.2.3.2=0
  - pandocfilters=1.4.2=py36_1
  - pango=1.42.4=h049681c_0
  - parso=0.3.4=py36_0
  - partd=0.3.10=py_0
  - patchelf=0.9=he6710b0_3
  - path.py=11.5.0=py36_0
  - pathlib2=2.3.3=py36_0
  - patsy=0.5.1=py36_0
  - pcre=8.43=he6710b0_0
  - pep8=1.7.1=py36_0
  - pexpect=4.6.0=py36_0
  - pickleshare=0.7.5=py36_0
  - pillow=5.4.1=py36h34e0f95_0
  - pip=19.0.3=py36_0
  - pixman=0.38.0=h7b6447c_0
  - pkginfo=1.5.0.1=py36_0
  - pluggy=0.9.0=py36_0
  - ply=3.11=py36_0
  - prometheus_client=0.6.0=py36_0
  - prompt_toolkit=2.0.9=py36_0
  - protobuf=3.6.1=py36he6710b0_0
  - psutil=5.6.1=py36h7b6447c_0
  - ptyprocess=0.6.0=py36_0
  - py=1.8.0=py36_0
  - py-lief=0.9.0=py36h7725739_2
  - pycodestyle=2.5.0=py36_0
  - pycosat=0.6.3=py36h14c3975_0
  - pycparser=2.19=py36_0
  - pycrypto=2.6.1=py36h14c3975_9
  - pycurl=7.43.0.2=py36h1ba5d50_0
  - pyflakes=2.1.1=py36_0
  - pygments=2.3.1=py36_0
  - pylint=2.3.1=py36_0
  - pyodbc=4.0.26=py36he6710b0_0
  - pyopenssl=19.0.0=py36_0
  - pyparsing=2.3.1=py36_0
  - pyqt=5.9.2=py36h05f1152_2
  - pyrsistent=0.14.11=py36h7b6447c_0
  - pysocks=1.6.8=py36_0
  - pytables=3.5.1=py36h71ec239_0
  - pytest=4.3.1=py36_0
  - pytest-arraydiff=0.3=py36h39e3cac_0
  - pytest-astropy=0.5.0=py36_0
  - pytest-doctestplus=0.3.0=py36_0
  - pytest-openfiles=0.3.2=py36_0
  - pytest-remotedata=0.3.1=py36_0
  - python=3.6.8=h0371630_0
  - python-dateutil=2.8.0=py36_0
  - python-libarchive-c=2.8=py36_6
  - pytz=2018.9=py36_0
  - pywavelets=1.0.2=py36hdd07704_0
  - pyyaml=5.1=py36h7b6447c_0
  - pyzmq=18.0.0=py36he6710b0_0
  - qt=5.9.7=h5867ecd_1
  - qtawesome=0.5.7=py_0
  - qtconsole=4.4.3=py36_0
  - qtpy=1.7.0=py_0
  - readline=7.0=h7b6447c_5
  - requests=2.21.0=py36_0
  - rope=0.12.0=py36_0
  - ruamel_yaml=0.15.46=py36h14c3975_0
  - scikit-image=0.14.2=py36he6710b0_0
  - scikit-learn=0.20.3=py36hd81dba3_0
  - scipy=1.2.1=py36h7c811a0_0
  - seaborn=0.9.0=py36_0
  - secretstorage=3.1.1=py36_0
  - send2trash=1.5.0=py36_0
  - setuptools=40.8.0=py36_0
  - simplegeneric=0.8.1=py36_2
  - singledispatch=3.4.0.3=py36_0
  - sip=4.19.8=py36hf484d3e_0
  - six=1.12.0=py36_0
  - snappy=1.1.7=hbae5bb6_3
  - snowballstemmer=1.2.1=py36_0
  - sortedcollections=1.1.2=py36_0
  - sortedcontainers=2.1.0=py36_0
  - soupsieve=1.8=py36_0
  - sphinx=1.8.5=py36_0
  - sphinxcontrib=1.0=py36_1
  - sphinxcontrib-websupport=1.1.0=py36_1
  - spyder=3.3.3=py36_0
  - spyder-kernels=0.4.2=py36_0
  - sqlalchemy=1.3.1=py36h7b6447c_0
  - sqlite=3.27.2=h7b6447c_0
  - statsmodels=0.9.0=py36h035aef0_0
  - sympy=1.3=py36_0
  - tblib=1.3.2=py36_0
  - tensorboard=1.11.0=py36hf484d3e_0
  - tensorflow=1.11.0=mkl_py36ha6f0bda_0
  - tensorflow-base=1.11.0=mkl_py36h3c3e929_0
  - termcolor=1.1.0=py36_1
  - terminado=0.8.1=py36_1
  - testpath=0.4.2=py36_0
  - tk=8.6.8=hbc83047_0
  - toolz=0.9.0=py36_0
  - tornado=6.0.1=py36h7b6447c_0
  - tqdm=4.31.1=py_0
  - traitlets=4.3.2=py36_0
  - typed-ast=1.3.1=py36h7b6447c_0
  - unicodecsv=0.14.1=py36_0
  - unixodbc=2.3.7=h14c3975_0
  - urllib3=1.24.1=py36_0
  - wcwidth=0.1.7=py36_0
  - webencodings=0.5.1=py36_1
  - werkzeug=0.14.1=py36_0
  - wheel=0.33.1=py36_0
  - widgetsnbextension=3.4.2=py36_0
  - wrapt=1.11.1=py36h7b6447c_0
  - wurlitzer=1.0.2=py36_0
  - xlrd=1.2.0=py36_0
  - xlsxwriter=1.1.5=py36_0
  - xlwt=1.3.0=py36_0
  - xz=5.2.4=h14c3975_4
  - yaml=0.1.7=had09818_2
  - zeromq=4.3.1=he6710b0_3
  - zict=0.1.4=py36_0
  - zipp=0.3.3=py_0
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.3.7=h0b5b093_0
  - pip:
    - libarchive-c==2.8
    - lief==0.9.0
    - msgpack==0.6.1
    - tables==3.5.1
prefix: /home/mdonato/.conda/envs/cnn_genetic

如果有人能告诉我为什么它不起作用，我将不胜感激。感谢您的宝贵时间。

Answer 1

我解决了安装 Keras 问题

conda install -c conda-forge keras-applications

该命令自动将 TensorFlow 从 1.13 降级到我需要的 1.10。