In [1]:
import cntk as C
from cntk import load_model
import numpy as np
import PIL
from IPython.display import Image
model_file_path = r'E:\local\cntk-models\AlexNet_ImageNet_CNTK.model'
#model_file_path = r'C:\Users\hojohnl\Source\Repos\CNTK\Tutorials\cifar10-resnet.model'
# from https://docs.microsoft.com/en-us/cognitive-toolkit/How-do-I-Read-Things-in-Python
loaded_model = load_model(model_file_path)
# all CNTK models constructed after early 2017 are v2 non-BrainScript
#if is_BrainScript:
# loaded_model = combine([loaded_model.outputs[0]])
In [ ]:
parameters = loaded_model.parameters
#for parameter in parameters:
# print(parameter.name, parameter.shape, "\n", parameter.value)
In [2]:
dir(loaded_model)
Out[2]:
['_ProgressCollector',
'__abs__',
'__add__',
'__call__',
'__class__',
'__del__',
'__delattr__',
'__dict__',
'__dir__',
'__disown__',
'__div__',
'__doc__',
'__eq__',
'__format__',
'__ge__',
'__getattr__',
'__getattribute__',
'__getitem__',
'__gt__',
'__hash__',
'__init__',
'__le__',
'__lshift__',
'__lt__',
'__matmul__',
'__module__',
'__mul__',
'__ne__',
'__neg__',
'__new__',
'__radd__',
'__rdiv__',
'__reduce__',
'__reduce_ex__',
'__repr__',
'__rmatmul__',
'__rmul__',
'__rshift__',
'__rsub__',
'__rtruediv__',
'__setattr__',
'__sizeof__',
'__str__',
'__sub__',
'__subclasshook__',
'__swig_destroy__',
'__swig_getmethods__',
'__swig_setmethods__',
'__truediv__',
'__weakref__',
'_backward',
'_deserialize',
'_deserializer',
'_forward',
'_infer_outputs',
'_placeholders_under_construction',
'_replace_args_type_check',
'_s',
'_serialize_impl',
'_to_Function',
'_udf_callback_map',
'argument_map',
'arguments',
'as_string',
'attributes',
'backward',
'block_arguments_mapping',
'block_root',
'clone',
'constants',
'current_version',
'declare_args',
'deserialize_method_name',
'eval',
'evaluate',
'find_all_with_name',
'find_by_name',
'forward',
'grad',
'gradients',
'inputs',
'is_block',
'is_composite',
'is_primitive',
'load',
'load_from_buffer',
'max_num_outputs',
'module_name',
'name',
'native_user_function',
'op_name',
'output',
'outputs',
'parameters',
'placeholders',
'register_native_user_function',
'register_udf_deserialize_callback',
'replace_placeholder',
'replace_placeholders',
'restore',
'restore_from_checkpoint',
'root_function',
'save',
'serialize',
'set_attribute',
'set_name',
'set_native',
'signature',
'test',
'this',
'train',
'type',
'uid',
'update_signature',
'with_signature']
In [4]:
parameters = loaded_model.parameters
len(parameters)
Out[4]:
16
In [5]:
parameters
Out[5]:
(Parameter('model.arrayOfFunctions[19].W', [], [4096 x 1000]),
Parameter('model.arrayOfFunctions[17].arrayOfFunctions[0].W', [], [4096 x 4096]),
Parameter('model.arrayOfFunctions[15].arrayOfFunctions[0].W', [], [256 x 6 x 6 x 4096]),
Parameter('model.arrayOfFunctions[12].W', [], [256 x 384 x 3 x 3]),
Parameter('model.arrayOfFunctions[10].W', [], [384 x 384 x 3 x 3]),
Parameter('model.arrayOfFunctions[8].W', [], [384 x 256 x 3 x 3]),
Parameter('model.arrayOfFunctions[4].W', [], [256 x 96 x 5 x 5]),
Parameter('model.arrayOfFunctions[0].W', [], [96 x 3 x 11 x 11]),
Parameter('model.arrayOfFunctions[0].b', [], [96 x 1 x 1]),
Parameter('model.arrayOfFunctions[4].b', [], [256 x 1 x 1]),
Parameter('model.arrayOfFunctions[8].b', [], [384 x 1 x 1]),
Parameter('model.arrayOfFunctions[10].b', [], [384 x 1 x 1]),
Parameter('model.arrayOfFunctions[12].b', [], [256 x 1 x 1]),
Parameter('model.arrayOfFunctions[15].arrayOfFunctions[0].b', [], [4096]),
Parameter('model.arrayOfFunctions[17].arrayOfFunctions[0].b', [], [4096]),
Parameter('model.arrayOfFunctions[19].b', [], [1000]))
In [6]:
loaded_model.root_function
Out[6]:
Combine: Output('ce', [], []), Output('errs', [], []), Output('top5Errs', [], []), Output('z', [#, ], [1000]) -> Output('ce', [], []), Output('errs', [], []), Output('top5Errs', [], []), Output('z', [#, ], [1000])
In [8]:
loaded_model.inputs
Out[8]:
(Parameter('model.arrayOfFunctions[12].W', [], [256 x 384 x 3 x 3]),
Parameter('model.arrayOfFunctions[10].W', [], [384 x 384 x 3 x 3]),
Parameter('model.arrayOfFunctions[8].W', [], [384 x 256 x 3 x 3]),
Parameter('model.arrayOfFunctions[4].W', [], [256 x 96 x 5 x 5]),
Parameter('model.arrayOfFunctions[0].W', [], [96 x 3 x 11 x 11]),
Input('features', [#, ], [3 x 227 x 227]),
Constant('featNorm.MinusArgs[1]', [], [1 x 1]),
Parameter('model.arrayOfFunctions[0].b', [], [96 x 1 x 1]),
Constant('_z.x._.x._.x.x._.x._.x._.x.x.x._.x.x.den._', [], [1]),
Constant('_z.x._.x._.x.x._.x._.x._.x.x.x._.x.x.den._.ElementTimesArgs[1]._', [], [1]),
Constant('z.x._.x._.x.x._.x._.x._.x.x.x._.x.x.W', [], [1 x 5 x 1 x 1]),
Parameter('model.arrayOfFunctions[4].b', [], [256 x 1 x 1]),
Constant('_z.x._.x._.x.x._.x._.x._.x.x.den._', [], [1]),
Constant('_z.x._.x._.x.x._.x._.x._.x.x.den._.ElementTimesArgs[1]._', [], [1]),
Constant('z.x._.x._.x.x._.x._.x._.x.x.W', [], [1 x 5 x 1 x 1]),
Parameter('model.arrayOfFunctions[8].b', [], [384 x 1 x 1]),
Parameter('model.arrayOfFunctions[10].b', [], [384 x 1 x 1]),
Parameter('model.arrayOfFunctions[12].b', [], [256 x 1 x 1]),
Parameter('model.arrayOfFunctions[15].arrayOfFunctions[0].W', [], [256 x 6 x 6 x 4096]),
Parameter('model.arrayOfFunctions[15].arrayOfFunctions[0].b', [], [4096]),
Parameter('model.arrayOfFunctions[17].arrayOfFunctions[0].W', [], [4096 x 4096]),
Parameter('model.arrayOfFunctions[17].arrayOfFunctions[0].b', [], [4096]),
Parameter('model.arrayOfFunctions[19].W', [], [4096 x 1000]),
Parameter('model.arrayOfFunctions[19].b', [], [1000]),
Input('labels', [#, ], [1000]),
Constant('inputs.inputs[2]', [], [1 x 1]))
In [9]:
loaded_model.forward
Out[9]:
<bound method Function.forward of Composite(Combine): Input('features', [#, ], [3 x 227 x 227]), Input('labels', [#, ], [1000]) -> Output('ce', [], []), Output('errs', [], []), Output('top5Errs', [], []), Output('z', [#, ], [1000])>
In [11]:
loaded_model.argument_map
Out[11]:
<bound method Function.argument_map of Composite(Combine): Input('features', [#, ], [3 x 227 x 227]), Input('labels', [#, ], [1000]) -> Output('ce', [], []), Output('errs', [], []), Output('top5Errs', [], []), Output('z', [#, ], [1000])>
In [12]:
loaded_model.signature
Out[12]:
(Input('features', [#, ], [3 x 227 x 227]), Input('labels', [#, ], [1000]))
In [21]:
loaded_model.constants
Out[21]:
(Constant('featNorm.MinusArgs[1]', [], [1 x 1]),
Constant('_z.x._.x._.x.x._.x._.x._.x.x.x._.x.x.den._', [], [1]),
Constant('_z.x._.x._.x.x._.x._.x._.x.x.x._.x.x.den._.ElementTimesArgs[1]._', [], [1]),
Constant('z.x._.x._.x.x._.x._.x._.x.x.x._.x.x.W', [], [1 x 5 x 1 x 1]),
Constant('_z.x._.x._.x.x._.x._.x._.x.x.den._', [], [1]),
Constant('_z.x._.x._.x.x._.x._.x._.x.x.den._.ElementTimesArgs[1]._', [], [1]),
Constant('z.x._.x._.x.x._.x._.x._.x.x.W', [], [1 x 5 x 1 x 1]),
Constant('inputs.inputs[2]', [], [1 x 1]))
In [22]:
loaded_model.as_string()
Out[22]:
"Composite(Combine): Input('features', [#, ], [3 x 227 x 227]), Input('labels', [#, ], [1000]) -> Output('ce', [], []), Output('errs', [], []), Output('top5Errs', [], []), Output('z', [#, ], [1000])"
In [18]:
loaded_model.module_name
Out[18]:
<bound method Function.module_name of Composite(Combine): Input('features', [#, ], [3 x 227 x 227]), Input('labels', [#, ], [1000]) -> Output('ce', [], []), Output('errs', [], []), Output('top5Errs', [], []), Output('z', [#, ], [1000])>
In [102]:
#node_in_graph = loaded_model.find_by_name('z.x._._.PlusArgs[0]')
#node_in_graph = loaded_model.find_by_name('model.arrayOfFunctions[19].b')
#node_in_graph = loaded_model.find_by_name('z.x._.x')
#node_in_graph = loaded_model.find_by_name('z.x._.x._.x')
node_in_graph = loaded_model.find_by_name('z.x._.x.b')
In [103]:
print(node_in_graph)
None
In [39]:
parameters
Out[39]:
(Parameter('model.arrayOfFunctions[19].W', [], [4096 x 1000]),
Parameter('model.arrayOfFunctions[17].arrayOfFunctions[0].W', [], [4096 x 4096]),
Parameter('model.arrayOfFunctions[15].arrayOfFunctions[0].W', [], [256 x 6 x 6 x 4096]),
Parameter('model.arrayOfFunctions[12].W', [], [256 x 384 x 3 x 3]),
Parameter('model.arrayOfFunctions[10].W', [], [384 x 384 x 3 x 3]),
Parameter('model.arrayOfFunctions[8].W', [], [384 x 256 x 3 x 3]),
Parameter('model.arrayOfFunctions[4].W', [], [256 x 96 x 5 x 5]),
Parameter('model.arrayOfFunctions[0].W', [], [96 x 3 x 11 x 11]),
Parameter('model.arrayOfFunctions[0].b', [], [96 x 1 x 1]),
Parameter('model.arrayOfFunctions[4].b', [], [256 x 1 x 1]),
Parameter('model.arrayOfFunctions[8].b', [], [384 x 1 x 1]),
Parameter('model.arrayOfFunctions[10].b', [], [384 x 1 x 1]),
Parameter('model.arrayOfFunctions[12].b', [], [256 x 1 x 1]),
Parameter('model.arrayOfFunctions[15].arrayOfFunctions[0].b', [], [4096]),
Parameter('model.arrayOfFunctions[17].arrayOfFunctions[0].b', [], [4096]),
Parameter('model.arrayOfFunctions[19].b', [], [1000]))
In [40]:
parameters[0]
Out[40]:
Parameter('model.arrayOfFunctions[19].W', [], [4096 x 1000])
In [46]:
parameters[7].value[0]
Out[46]:
array([[[ 3.50630446e-03, -1.38956951e-02, -2.07328796e-02,
-2.07189303e-02, -1.34732276e-02, -5.86241530e-03,
3.01717198e-03, 2.78823730e-02, 3.41331623e-02,
2.10740771e-02, 2.60362830e-02],
[ 1.80671215e-02, -1.60608062e-04, -1.28373094e-02,
-2.58281119e-02, -4.40702997e-02, -4.21921872e-02,
-3.72415818e-02, -2.06697378e-02, 2.17856672e-02,
2.97645628e-02, 2.02181898e-02],
[ 2.80588791e-02, 2.29507443e-02, 2.01876909e-02,
1.79119688e-02, 2.27448461e-03, -3.64888832e-02,
-6.66335523e-02, -8.06568563e-02, -6.45741224e-02,
-1.34916203e-02, 2.00375840e-02],
[ 2.63227765e-02, 2.10754313e-02, 2.15190761e-02,
2.93923430e-02, 4.89946306e-02, 4.26985621e-02,
4.32141451e-03, -3.81312482e-02, -9.16949585e-02,
-8.08384791e-02, -3.78490165e-02],
[ 7.29457336e-03, 2.35204641e-02, 2.43157148e-02,
8.67151562e-03, -1.55515724e-03, 3.45121659e-02,
6.87839016e-02, 7.18631148e-02, 2.56474502e-02,
-4.66021560e-02, -7.05356002e-02],
[ -2.02720705e-02, 3.03806667e-03, 2.62349341e-02,
3.90781015e-02, 9.89390444e-03, -2.27464903e-02,
-8.84139910e-03, 3.48515324e-02, 5.77488057e-02,
3.36729959e-02, -1.01607647e-02],
[ -3.19806151e-02, -3.56811695e-02, -1.29497563e-02,
4.09118347e-02, 8.05644915e-02, 6.25853240e-02,
1.29225431e-02, -2.11890507e-02, -7.82141741e-03,
1.01888143e-02, 1.86636839e-02],
[ -1.44501003e-02, -3.04901209e-02, -5.29653803e-02,
-4.14560549e-02, 6.26058318e-03, 5.30958809e-02,
7.23051801e-02, 4.89179753e-02, 2.14141347e-02,
1.19928624e-02, 2.09243856e-02],
[ -7.55948201e-03, -7.68836867e-03, -2.51051784e-02,
-5.11993431e-02, -6.95234612e-02, -4.87841144e-02,
8.68050731e-04, 3.63034941e-02, 4.26917262e-02,
3.12642306e-02, 3.34120616e-02],
[ -2.45727540e-04, -2.00031418e-03, 7.20829377e-03,
-6.57307729e-03, -3.00610289e-02, -4.87693883e-02,
-4.55099829e-02, -2.49798484e-02, -6.70335395e-03,
1.27052581e-02, 2.51725595e-02],
[ 1.87909324e-02, 8.66514351e-03, 1.15985665e-02,
1.41251432e-02, 6.09156722e-03, -5.17470110e-03,
-1.37421843e-02, -2.63284426e-02, -2.86639687e-02,
-1.51513154e-02, 8.22509173e-03]],
[[ -6.51691854e-03, 8.35410669e-04, 3.03569250e-03,
1.22411018e-02, 6.46493724e-03, 1.93543662e-03,
-3.81553284e-04, 7.00137950e-03, 8.91112909e-03,
-9.72796604e-03, -9.37007461e-03],
[ -6.07031118e-03, -3.62819061e-03, 5.87025657e-03,
1.12034995e-02, 3.61549784e-03, 5.87973185e-03,
-4.40799166e-03, -6.82329666e-03, 2.13351659e-02,
2.07668953e-02, 1.05928164e-03],
[ -1.59018617e-02, -8.22426472e-03, 4.83972719e-03,
2.71418840e-02, 4.80524004e-02, 3.16202864e-02,
-3.62487335e-04, -2.73514390e-02, -4.02195640e-02,
-7.35693658e-03, 2.11120192e-02],
[ -1.60473958e-02, -3.43205035e-02, -4.79264185e-02,
-3.39893550e-02, 2.48605795e-02, 7.50247166e-02,
8.96216929e-02, 6.28679097e-02, -1.23926178e-02,
-4.28397767e-02, -1.64997149e-02],
[ 1.64219411e-03, -7.64352083e-03, -4.42382134e-02,
-1.00406721e-01, -1.25073999e-01, -5.86030670e-02,
5.12378030e-02, 1.26524702e-01, 1.24248981e-01,
4.78706360e-02, -3.41694104e-03],
[ 1.24264657e-02, 3.12853754e-02, 3.31232920e-02,
-5.11009991e-03, -9.45159346e-02, -1.76511124e-01,
-1.64674684e-01, -6.08621798e-02, 4.68653254e-02,
8.24379325e-02, 5.71292080e-02],
[ 1.49996793e-02, 1.48672415e-02, 4.32567000e-02,
9.14747715e-02, 9.19204056e-02, 3.53609934e-03,
-1.12201266e-01, -1.67615414e-01, -1.23278238e-01,
-4.61526029e-02, 1.72199160e-02],
[ 1.97455287e-02, 4.09696531e-03, -6.82325149e-03,
1.24549270e-02, 6.86807260e-02, 9.82866064e-02,
6.89457655e-02, -5.55709656e-03, -7.13253245e-02,
-7.43103772e-02, -4.31654081e-02],
[ 1.06687127e-02, 1.50524974e-02, 9.73688555e-04,
-1.87695380e-02, -1.99060477e-02, 1.34892622e-02,
5.17734550e-02, 6.20026812e-02, 2.40334105e-02,
-1.44004738e-02, -2.88838688e-02],
[ -1.08632470e-04, 7.34055741e-03, 1.43454624e-02,
9.89818759e-03, -3.30395275e-03, -1.57752950e-02,
-6.73701009e-03, 1.45804547e-02, 2.19062455e-02,
9.26206354e-03, -1.13179311e-02],
[ 5.79719478e-03, -2.56665424e-03, -5.49237709e-03,
5.00047207e-03, 6.75040577e-03, 5.94909489e-03,
4.73267073e-03, 1.66302896e-03, 4.89258859e-03,
3.19065922e-03, -5.66056045e-03]],
[[ 1.96824386e-03, 2.33540628e-02, 3.06071322e-02,
3.61001156e-02, 2.19905004e-02, -3.34414304e-03,
-2.68158391e-02, -3.09753548e-02, -3.55234146e-02,
-5.17534688e-02, -4.95414883e-02],
[ -2.36925222e-02, 3.27261840e-03, 2.40222421e-02,
3.94467562e-02, 3.72356400e-02, 3.32086757e-02,
9.20768175e-03, -1.44693116e-02, -4.67946939e-03,
-1.15460558e-02, -3.08931824e-02],
[ -4.64302897e-02, -2.40870863e-02, 1.45210116e-03,
4.82531786e-02, 8.27911347e-02, 8.24187025e-02,
4.85862195e-02, 2.95237289e-03, -2.90984903e-02,
-1.28441164e-02, 3.83370579e-03],
[ -3.94097529e-02, -6.10531494e-02, -7.46096522e-02,
-4.91598174e-02, 3.00845429e-02, 1.02839932e-01,
1.36641204e-01, 1.14649706e-01, 2.97650732e-02,
-1.78315435e-02, -4.66430234e-03],
[ -2.70229927e-03, -2.47104522e-02, -7.90898353e-02,
-1.43792689e-01, -1.62956208e-01, -7.20253438e-02,
6.36917651e-02, 1.71295196e-01, 1.71834007e-01,
9.73547101e-02, 4.30306382e-02],
[ 2.55559236e-02, 2.97347102e-02, 1.25759570e-02,
-4.69234586e-02, -1.53728485e-01, -2.35080242e-01,
-2.02051073e-01, -6.78689927e-02, 7.06691742e-02,
1.26860589e-01, 1.20419972e-01],
[ 3.85421328e-02, 3.32597718e-02, 5.49093820e-02,
8.95126462e-02, 6.42906874e-02, -4.20139469e-02,
-1.67246550e-01, -2.17956707e-01, -1.49551824e-01,
-4.14426215e-02, 5.32154776e-02],
[ 3.24370302e-02, 2.03154311e-02, 1.82814114e-02,
4.29460667e-02, 8.80723149e-02, 9.72901583e-02,
4.64284457e-02, -5.36620244e-02, -1.16149902e-01,
-1.03068650e-01, -5.18970564e-02],
[ 4.35745483e-03, 1.45921540e-02, 1.68568343e-02,
9.21446271e-03, 1.30211115e-02, 4.45751995e-02,
7.08355382e-02, 5.47778420e-02, 1.72159378e-03,
-3.94109748e-02, -5.16697541e-02],
[ -1.94593780e-02, -9.13538039e-03, 6.79212390e-03,
1.28429951e-02, 8.74121115e-03, 1.21888584e-02,
2.35506948e-02, 3.16514224e-02, 2.97835339e-02,
-3.52311472e-04, -3.41352522e-02],
[ -2.41024550e-02, -3.30652744e-02, -3.14658396e-02,
-1.22568449e-02, 6.83015853e-04, 6.51137438e-03,
1.81107670e-02, 2.24965494e-02, 3.35680135e-02,
1.99190993e-02, -7.88432918e-03]]], dtype=float32)
In [42]:
parameters[0].shape
Out[42]:
(4096, 1000)
In [43]:
parameters[15].value
Out[43]:
array([ -1.29500637e-02, 2.89597013e-03, -5.24822343e-03,
-8.44752137e-03, 1.77988282e-03, -1.69690349e-03,
1.12689603e-02, -7.35673914e-03, -1.30422693e-02,
-1.79991052e-02, -1.94467716e-02, -1.58345029e-02,
-4.37695533e-04, -4.67045745e-03, -5.18501969e-03,
-8.72209109e-03, -4.81168833e-03, -4.03746590e-03,
-1.16221951e-02, -6.16759667e-03, -6.65784391e-05,
7.86450040e-03, -2.29683658e-03, -1.14905937e-02,
-1.06872376e-02, 3.66675784e-03, -1.26435673e-02,
-8.29939730e-03, 2.75216484e-03, -1.30725594e-03,
3.46196606e-03, -9.37383622e-03, -5.26162237e-03,
-3.54038482e-03, 6.22636359e-03, -1.67948324e-02,
-4.72843088e-03, 1.44107174e-03, -6.16718270e-03,
9.26524308e-03, -1.04761671e-03, 3.28019867e-03,
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1.37712313e-02, -6.13895152e-03, -5.71912341e-03,
2.12832820e-02, -7.68472813e-03, -1.03836656e-02,
1.73703823e-02, 1.12672672e-02, 5.74785424e-03,
9.00700968e-03, -9.66321025e-03, -1.10305939e-02,
7.06498278e-03, -8.56367592e-03, 7.61304330e-03,
6.43027388e-03, -7.78217940e-03, 2.89568352e-03,
1.86963100e-02, -1.74942426e-03, 2.14213179e-03,
6.69030240e-03, -2.07714605e-04, -4.49535344e-03,
1.22886524e-03, -7.44425273e-03, 1.47372615e-02,
1.77836511e-02, -1.15262056e-02, 1.65626500e-02,
-4.49551363e-03, 4.41365596e-03, 1.08250650e-02,
-1.03331264e-02, 1.19542945e-02, -1.90678239e-03,
-1.39002865e-02, -1.91661203e-03, -2.09704065e-03,
4.76882840e-03, 1.62208942e-03, -2.61454354e-03,
-9.67579894e-03, 8.07088707e-03, 2.62096734e-03,
1.84017722e-03, -2.10938533e-03, 1.03234630e-02,
2.52896082e-03, 2.63950936e-02, -5.58545534e-03,
-2.24215374e-03, -1.44066429e-02, -6.65545743e-03,
7.53595261e-03, -1.28965545e-02, -3.74585530e-03,
-1.74480199e-03, -1.07467426e-02, -1.64851616e-03,
5.19106025e-03, 5.10315085e-03, 1.02893426e-03,
1.91423658e-03, 1.51306093e-02, 5.36807813e-04,
1.58838425e-02, -1.83689725e-02, 6.56085508e-03,
-7.53932307e-03, -3.89346015e-03, 1.04584463e-03,
-4.42489702e-03, 1.02243992e-02, -3.53342621e-05,
-1.46955205e-03, -4.82552079e-03, 8.89092311e-03,
4.00606962e-03, 5.55161899e-03, 2.56051472e-03,
3.80361592e-03, 8.74571688e-03, -1.51327737e-02,
-1.40774446e-02, -1.32027827e-02, -1.91964172e-02,
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1.16129322e-02, -2.04728753e-03, -2.54780874e-02,
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1.74846202e-02, 2.00117868e-03, 2.02196911e-02,
4.75109648e-03, -4.91981069e-03, 1.32744424e-02,
-6.08587591e-03, 1.94013827e-02, 2.69878656e-02,
3.76933347e-03, -9.87474993e-03, -2.06873138e-02,
4.46397439e-03, 7.83734024e-03, 1.16070937e-02,
8.38749576e-03, -1.48011511e-02, -5.79253631e-03,
-8.49956367e-03, 2.41103093e-03, 1.42735988e-03,
4.91141900e-03, -4.64020733e-04, -8.50224495e-03,
4.82099596e-03, -9.78186354e-03, -2.46866178e-02,
-4.22752276e-03, -9.04466398e-03, -9.72170196e-03,
-3.85634298e-03, -1.41905043e-02, 6.99427328e-04,
-2.19151634e-03, 1.72647729e-03, -8.58845247e-04,
3.82274645e-03, -1.07983071e-02, -1.05489139e-02,
6.15141587e-03, 7.85595831e-03, 7.01989932e-03,
2.30260543e-03, 3.63296317e-03, 4.14839052e-02,
5.99989714e-03, -2.20737467e-03, -2.14048419e-02,
2.63890158e-03, -1.07462646e-03, 3.52423042e-02,
3.67568433e-03, -5.03078056e-03, -1.36080780e-03,
6.68016961e-03, 4.79865307e-03, 1.61947701e-02,
-4.83226776e-03, 4.58921073e-03, 3.59819643e-03,
3.23600089e-03, 2.72982083e-02, 1.08473646e-02,
-6.60818676e-03, -1.19808856e-02, -1.08733149e-02,
-1.18572088e-02, 7.73320906e-03, -8.92864354e-03,
-1.12458202e-03, -2.65436526e-02, -9.39748064e-03,
-1.01335952e-02, -5.90573996e-03, -1.55094815e-02,
-1.86653098e-03, -5.19529730e-03, -3.16088484e-03,
1.84682161e-02], dtype=float32)
In [73]:
named_node = loaded_model.find_by_name('z.x')
In [74]:
print(named_node)
z.x: Dropout(z.x._: SequenceOver[][Tensor[4096]]) -> SequenceOver[][Tensor[4096]]
In [65]:
node_outputs = loaded_model.outputs
In [66]:
for n in node_outputs : print("{0} {1}".format(n.name, n.shape))
ce ()
errs ()
top5Errs ()
z (1000,)
In [ ]:
Content source: hjl/cntkdemo
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