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 [19]:

    

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


    

In [28]:

    

data_in_shape = (17, 17, 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='same', subsample=(1, 1), dim_ordering='tf', bias=True)
conv_2 = Conv2D(2, 3, 3, activation='relu', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True)
conv_3 = Conv2D(3, 5, 5, activation='relu', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True)
conv_4 = 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)
output_layer = conv_4(x)
model = Model(input=input_layer, output=output_layer)

np.random.seed(2000)
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(2000 + 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)}
})


    

    




{'expected': {'data': [-46.36700821, -87.19824219], 'shape': [1, 1, 2]}, 'input': {'data': [0.14103457, 0.12905751, -0.02311633, -0.32704449, -0.24826364, 0.06407174, -0.86378742, 0.16905812, -0.52420447, -0.67848684, -0.6957617, -0.74586156, -0.34511767, 0.38830775, -0.28206706, 0.78273131, -0.59624024, -0.77879499, 0.05511352, 0.73531066, -0.47625124, -0.00120976, -0.1428712, 0.06000778, 0.14789364, -0.2162887, -0.84097177, 0.73456202, -0.67099284, 0.60696324, -0.4241435, -0.46285765, 0.43495603, 0.76281131, 0.98424006, -0.08329993, 0.57025854, 0.47738787, -0.05283404, -0.03033059, 0.8660102, 0.50530768, -0.68142234, -0.73037907, -0.17864565, 0.51307252, -0.57497396, -0.3719412, -0.59745313, 0.87685041, 0.00882968, 0.2074629, 0.67509717, 0.22036492, 0.47114553, -0.18046774, -0.02071998, 0.01784904, 0.01296462, 0.23668214, 0.66920963, 0.17313075, -0.95738522, 0.47124672, 0.84126731, 0.51135444, -0.43048844, 0.89919803, 0.6797657, 0.62990001, 0.48735555, 0.82973875, 0.79246797, 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