In [1]:

    

import numpy as np
import json
from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, BatchNormalization, merge
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 [16]:

    

data_in_shape = (8, 8, 2)

input_layer_0 = Input(shape=data_in_shape)
branch_0 = Conv2D(4, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_0-0')(input_layer_0)

input_layer_1 = Input(shape=data_in_shape)
branch_1 = Conv2D(2, 1, 1, activation='relu', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_1-0')(input_layer_1)
branch_1 = Conv2D(4, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_1-1')(branch_1)
branch_1 = Conv2D(2, 1, 1, activation='relu', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_1-2')(branch_1)

input_layer_2 = Input(shape=data_in_shape)
branch_2 = Conv2D(5, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_2-0')(input_layer_2)
branch_2 = Conv2D(3, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_2-1')(branch_2)

output_layer = merge([branch_0, branch_1, branch_2], mode='concat')
model = Model(input=[input_layer_0, input_layer_1, input_layer_2], output=output_layer)

data_in = []
for i in range(3):
    np.random.seed(19000 + i)
    data_in.append(np.expand_dims(2 * np.random.random(data_in_shape) - 1, axis=0))

# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
    np.random.seed(19000 + i)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)

result = model.predict(data_in)

print({
    'inputs': [{'data': format_decimal(data_in[i].ravel().tolist()), 'shape': list(data_in_shape)} for i in range(3)],
    '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)}
})


    

    




{'weights': [{'shape': [1, 1, 2, 2], 'data': [-0.71596766, 0.83019575, 0.12233745, 0.92591957]}, {'shape': [2], 'data': [-0.24747862, 0.16567801]}, {'shape': [3, 3, 2, 4], 'data': [-0.94835971, -0.09864511, -0.96668335, -0.11678862, -0.0684701, 0.35297961, 0.68186505, -0.64464683, 0.60428433, -0.12843896, -0.66738358, -0.96252509, 0.11343417, -0.26810103, 0.62478478, -0.92468562, 0.12092665, -0.03926093, -0.48419303, -0.2015424, -0.59762146, 0.48302104, 0.46979752, -0.33142447, 0.6379101, 0.29292009, -0.08235647, 0.58146891, -0.95709137, 0.91108344, -0.54017656, 0.7763715, -0.0947622, 0.44968087, -0.1750356, -0.16006343, -0.98221201, 0.16236873, 0.02533719, -0.7193739, -0.83571764, -0.09914948, -0.05324984, -0.58456162, 0.81909889, -0.49005946, -0.37903645, 0.7714574, 0.16240276, 0.77738959, -0.60000374, -0.4806184, -0.84023324, -0.36447888, 0.18264885, -0.32894268, 0.36280974, -0.85252119, 0.61026127, 0.04979669, -0.15516699, -0.67961233, -0.36392683, -0.8904356, 0.90778897, 0.93174974, -0.00675755, -0.19315719, 0.34133025, 0.21591398, 0.91391035, -0.50014449]}, {'shape': [4], 'data': [-0.22277998, 0.83024662, 0.28250145, 0.80130791]}, {'shape': [3, 3, 2, 5], 'data': [-0.27431101, 0.41639419, 0.28978002, 0.01022304, 0.34676476, 0.92145893, 0.54710693, -0.00129638, 0.19458927, -0.82426229, 0.2493499, 0.75655543, -0.12101202, 0.18093875, -0.06614411, -0.29852196, -0.38870871, 0.3282147, -0.58642101, -0.56520792, -0.62072695, 0.25214419, 0.39526129, -0.70663187, -0.50887732, -0.94197461, -0.86376215, -0.19962013, 0.38461478, -0.16522164, -0.51690063, -0.41235393, 0.54507372, 0.50765358, 0.8467451, 0.04629424, -0.15811579, -0.06779732, 0.54153266, 0.79279675, 0.12512371, -0.23521379, 0.99233648, -0.96873134, 0.38834065, 0.81294911, 0.42531214, 0.70748795, 0.37904506, -0.06639092, 0.46828664, 0.26222765, -0.14356817, -0.657842, -0.33931757, 0.67349513, -0.51238876, 0.12673645, -0.36282379, -0.7854849, -0.53469439, -0.51389275, 0.44516035, 0.96014876, 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-0.67241477, -0.96960123, 0.52578098, -0.71196724, 0.33794471, -0.77846838, -0.35593011, 0.93151092, -0.00757161, -0.51565083, -0.21402329, -0.45856714, -0.31730791, -0.04441553, 0.88208495, -0.85420179, 0.91867899, 0.07909438, -0.66512917, 0.82040738, 0.04453403, 0.21720405, 0.76404934, 0.3051056, -0.95407415, 0.05532792, -0.83626565, -0.54109659]}, {'shape': [4], 'data': [0.77041048, 0.36454704, -0.49954674, -0.66859321]}, {'shape': [1, 1, 4, 2], 'data': [0.20403495, -0.77340124, -0.46550048, -0.72451726, 0.93998706, -0.22566734, -0.90885358, 0.97179257]}, {'shape': [2], 'data': [0.35004329, 0.7476916]}, {'shape': [3, 3, 5, 3], 'data': [0.66324328, 0.19081955, -0.59506243, 0.1471951, 0.25965958, -0.93468423, 0.09420988, -0.9912427, -0.92985548, 0.72284093, 0.51787613, -0.34516821, -0.22759756, 0.57881034, 0.74886838, 0.02095912, 0.30531405, -0.89652203, -0.99999733, -0.73958489, -0.17262839, -0.75704349, -0.94976901, -0.10138756, 0.17502399, 0.00341713, 0.84848194, -0.43352148, 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In [17]:

    

import json
config = json.loads(model.to_json())
print([x['config']['name'] for x in config['config']['layers']])


    

    




['input_5', 'conv_1-0', 'input_6', 'input_4', 'conv_1-1', 'conv_2-0', 'conv_0-0', 'conv_1-2', 'conv_2-1', 'merge_2']

In [ ]: