In [1]:

    

import numpy as np
import json
from keras.models import Model
from keras.layers import Input
from keras.layers.convolutional import Conv2D
from keras import backend as K


    

    




Using TensorFlow backend.

In [2]:

    

def format_decimal(arr, places=8):
    return [round(x * 10**places) / 10**places for x in arr]


    

In [4]:

    

data_in_shape = (16, 16, 2)

conv_0 = Conv2D(5, 3, 3, activation='relu', border_mode='same', subsample=(2, 2), dim_ordering='tf', bias=True)
conv_1 = Conv2D(4, 3, 3, activation='linear', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True)
conv_2 = Conv2D(2, 1, 1, activation='relu', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True)
conv_3 = Conv2D(3, 5, 5, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True)
conv_4 = Conv2D(2, 3, 3, activation='linear', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True)
conv_5 = Conv2D(4, 1, 1, activation='relu', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True)
conv_6 = Conv2D(2, 3, 3, activation='linear', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True)

input_layer = Input(shape=data_in_shape)
x = conv_0(input_layer)
x = conv_1(x)
x = conv_2(x)
x = conv_3(x)
x = conv_4(x)
x = conv_5(x)
output_layer = conv_6(x)
model = Model(input=input_layer, output=output_layer)

np.random.seed(3000)
data_in = 2 * np.random.random(data_in_shape) - 1

# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
    np.random.seed(3000 + i)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)

result = model.predict(np.array([data_in]))

print({
    'input': {'data': format_decimal(data_in.ravel().tolist()), 'shape': list(data_in_shape)},
    'weights': [{'data': format_decimal(weights[i].ravel().tolist()), 'shape': list(weights[i].shape)} for i in range(len(weights))],
    'expected': {'data': format_decimal(result[0].ravel().tolist()), 'shape': list(result[0].shape)}
})


    

    




{'input': {'data': [0.02158712, 0.17120789, 0.42253968, -0.34874392, 0.04062585, 0.47916386, 0.684427, -0.69498906, 0.06485479, 0.05675407, -0.07646271, 0.18419409, -0.79339215, 0.62722166, -0.85550321, -0.36241177, 0.85582755, 0.68180441, -0.32636201, -0.21848271, 0.30283706, 0.32128667, -0.05237423, 0.06603501, 0.80225016, 0.99431247, 0.82962931, -0.75036757, 0.311414, 0.45841489, 0.51261021, 0.02276411, -0.82829494, 0.92948606, 0.47251296, -0.65135147, -0.39246035, -0.26382678, -0.29026049, 0.31517715, -0.82461071, -0.2283719, 0.69612321, -0.83480008, 0.96956959, -0.04238643, -0.39236961, -0.27375073, 0.31817232, -0.26229144, -0.41973438, 0.82439776, -0.96024608, 0.88288849, 0.22662472, 0.42280432, -0.06176646, -0.27379721, -0.74020505, -0.78652315, 0.34077241, -0.4854114, -0.94292376, -0.69879077, -0.45936408, 0.07486736, 0.12277373, 0.44663933, -0.2374805, 0.97628004, 0.8363243, 0.93503332, 0.29394737, -0.68209785, 0.95477247, -0.07327856, 0.50103735, 0.91777335, 0.6739928, -0.98499775, 0.61051427, 0.39329428, -0.12069, -0.44325231, -0.81229626, 0.2400612, -0.6014919, 0.3950817, 0.9199326, 0.73138346, 0.48931682, -0.13941714, 0.66200411, -0.56300023, -0.74614386, -0.5024158, 0.09216436, -0.74613148, -0.10437111, -0.98804565, -0.38953607, -0.75173589, 0.10544912, 0.2122157, 0.36553923, -0.14460125, 0.75693161, 0.02540242, -0.80083957, -0.45071968, -0.22808028, -0.13830507, 0.75961451, 0.21697387, 0.19385144, -0.81843405, -0.47749522, -0.47903825, -0.76566692, -0.82896292, -0.51813879, 0.37840462, -0.36735574, -0.07504603, -0.81150799, -0.1402414, -0.86849378, 0.33481197, -0.51653972, -0.66723411, -0.89837662, -0.97345217, -0.38707172, -0.08084839, -0.79660717, 0.77982, 0.27035192, 0.25028201, -0.97524904, -0.92697161, 0.63661278, 0.42884333, 0.36808762, 0.52321376, 0.63645692, 0.37568512, -0.32183286, 0.65335452, -0.00800893, 0.07030834, -0.2509481, 0.34497345, -0.11285549, -0.75627752, -0.87781018, -0.7973496, 0.82729233, 0.51446942, -0.12950208, 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-0.54113574, 0.3569098, -0.63639918, -0.78602205, 0.60640877, 0.26678617, 0.48439871, 0.60489156, -0.50640138, 0.42960609, 0.6026213, 0.72232948, -0.5999677, 0.5974766, -0.11468093, -0.91200886, -0.59372174, -0.75228349, 0.3525877, 0.42311969, 0.50357755, 0.8768361, -0.33447034, 0.5805673, -0.089548, -0.62483196, -0.88824033, 0.95515643, 0.86431987, -0.1635778, 0.72856758, 0.15155153, -0.72370583, -0.06956691, -0.16810678, 0.72833225, 0.2174608, -0.96397124, -0.05708696, 0.10087297, 0.03018523, -0.79591462, -0.81829274, 0.94442449, -0.73824392, 0.38574104, 0.02880237, 0.52033325, -0.19511934, 0.84133026, -0.58710165, -0.23015826, 0.63350329, 0.02299491, 0.97226814, 0.34246344, 0.64616127, -0.46018167, -0.03561687, 0.24592514, -0.85117151, -0.51333591, -0.81505806, -0.41295655, 0.51997057, 0.38011854, -0.3333831, -0.1279661, 0.6052381, -0.71539978, -0.42342126, -0.61309864, -0.95769165, -0.08200324, -0.11478403, -0.66970664, -0.28086432, -0.54861811, -0.4065818, -0.35742788, 0.39742654, 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