在python中冻结tensorflow图时出现索引错误？

Question

我试图冻结一个玩具图，但我得到以下索引错误。如果说实话，我有点不知所措，有什么建议吗？下面是代码和stacktrace：

import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.python.tools import freeze_graph

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()


model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(784,)))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax, name="output_node"))

model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.fit(train_images, train_labels, epochs=1)

# GRAPH SAVING - '.pbtxt'
tf.train.write_graph(K.get_session().graph_def, 'out', 'my_graph_name_graph.pbtxt')

# GRAPH SAVING - '.chkp'
# KEY: This method saves the graph at it's last checkpoint (hence '.chkp')
tf.train.Saver().save(K.get_session(), 'out/my_graph_name.chkp')

# GRAPH SAVING - '.bytes'
freeze_graph.freeze_graph('out/my_graph_name_graph.pbtxt', None, False,
                          'out/my_graph_name.chkp', "output_node",
                          "save/restore_all", "save/Const:0",
                          'out/frozen_my_graph_name.bytes', True, "")

print("done!")

堆栈跟踪：

^{pr2}$

以下是我的套餐：

(env) C:\env>conda list
# packages in environment at C:\Users\bsmit\AppData\Local\Continuum\anaconda3\envs\env:
#
# Name                    Version                   Build  Channel
_tflow_select             2.3.0                       mkl
absl-py                   0.7.0                    py36_0
astor                     0.7.1                    py36_0
backcall                  0.1.0                    py36_0
blas                      1.0                         mkl
bleach                    3.1.0                    py36_0
ca-certificates           2019.1.23                     0
certifi                   2018.11.29               py36_0
colorama                  0.4.1                    py36_0
decorator                 4.3.2                    py36_0
entrypoints               0.3                      py36_0
gast                      0.2.2                    py36_0
grpcio                    1.16.1           py36h351948d_1
h5py                      2.9.0            py36h5e291fa_0
hdf5                      1.10.4               h7ebc959_0
icc_rt                    2019.0.0             h0cc432a_1
icu                       58.2                 ha66f8fd_1
intel-openmp              2019.1                      144
ipykernel                 5.1.0            py36h39e3cac_0
ipython                   7.3.0            py36h39e3cac_0
ipython_genutils          0.2.0            py36h3c5d0ee_0
ipywidgets                7.4.2                    py36_0
jedi                      0.13.3                   py36_0
jinja2                    2.10                     py36_0
jpeg                      9b                   hb83a4c4_2
jsonschema                2.6.0            py36h7636477_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
keras-applications        1.0.6                    py36_0
keras-base                2.2.4                    py36_0
keras-preprocessing       1.0.5                    py36_0
libmklml                  2019.0.3                      0
libpng                    1.6.36               h2a8f88b_0
libprotobuf               3.6.1                h7bd577a_0
libsodium                 1.0.16               h9d3ae62_0
m2w64-gcc-libgfortran     5.3.0                         6
m2w64-gcc-libs            5.3.0                         7
m2w64-gcc-libs-core       5.3.0                         7
m2w64-gmp                 6.1.0                         2
m2w64-libwinpthread-git   5.0.0.4634.697f757               2
markdown                  3.0.1                    py36_0
markupsafe                1.1.1            py36he774522_0
mistune                   0.8.4            py36he774522_0
mkl                       2019.1                      144
mkl_fft                   1.0.10           py36h14836fe_0
mkl_random                1.0.2            py36h343c172_0
msys2-conda-epoch         20160418                      1
nbconvert                 5.3.1                    py36_0
nbformat                  4.4.0            py36h3a5bc1b_0
notebook                  5.7.4                    py36_0
numpy                     1.16.2           py36h19fb1c0_0
numpy-base                1.16.2           py36hc3f5095_0
openssl                   1.1.1b               he774522_0
pandoc                    2.2.3.2                       0
pandocfilters             1.4.2                    py36_1
parso                     0.3.4                    py36_0
pickleshare               0.7.5                    py36_0
pip                       19.0.3                   py36_0
prometheus_client         0.6.0                    py36_0
prompt_toolkit            2.0.9                    py36_0
protobuf                  3.6.1            py36h33f27b4_0
pygments                  2.3.1                    py36_0
pyqt                      5.9.2            py36h6538335_2
pyreadline                2.1                      py36_1
python                    3.6.8                h9f7ef89_7
python-dateutil           2.8.0                    py36_0
pywinpty                  0.5.5                 py36_1000
pyyaml                    3.13             py36hfa6e2cd_0
pyzmq                     18.0.0           py36ha925a31_0
qt                        5.9.7            vc14h73c81de_0
qtconsole                 4.4.3                    py36_0
scipy                     1.2.1            py36h29ff71c_0
send2trash                1.5.0                    py36_0
setuptools                40.8.0                   py36_0
sip                       4.19.8           py36h6538335_0
six                       1.12.0                   py36_0
sqlite                    3.26.0               he774522_0
tensorboard               1.12.2           py36h33f27b4_0
tensorflow                1.12.0          mkl_py36h4f00353_0
tensorflow-base           1.12.0          mkl_py36h81393da_0
termcolor                 1.1.0                    py36_1
terminado                 0.8.1                    py36_1
testpath                  0.4.2                    py36_0
tornado                   5.1.1            py36hfa6e2cd_0
traitlets                 4.3.2            py36h096827d_0
vc                        14.1                 h0510ff6_4
vs2015_runtime            14.15.26706          h3a45250_0
wcwidth                   0.1.7            py36h3d5aa90_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
wincertstore              0.2              py36h7fe50ca_0
winpty                    0.4.3                         4
yaml                      0.1.7                hc54c509_2
zeromq                    4.3.1                h33f27b4_3
zlib                      1.2.11               h62dcd97_3

看来你的帖子大部分都是代码，请多加一些细节。它看来你的帖子主要是代码，请添加一些详细信息。在

Answer 1

你的模型没有任何输入。keras模型需要某种形式的输入，可以是层，也可以是第一层的参数。在

请看this链接，以获得如何在MNIST上使用keras的清晰示例。它几乎完全相同的代码，但请注意下面的一行

model.add(Dense(512, activation='relu', input_shape=(784,)))

input_shape参数就是您缺少的。在