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
from collections import OrderedDict


    

    




Using TensorFlow backend.

In [2]:

    

def format_decimal(arr, places=6):
    return [round(x * 10**places) / 10**places for x in arr]


    

In [3]:

    

DATA = OrderedDict()


    

In [4]:

    

random_seed = 1000
data_in_shape = (17, 17, 2)

layers = [
    Conv2D(5, (3,3), activation='relu', padding='same', strides=(2, 2), data_format='channels_last', use_bias=True),
    Conv2D(4, (3,3), activation='linear', padding='same', strides=(1, 1), data_format='channels_last', use_bias=True),
    Conv2D(2, (3,3), activation='relu', padding='valid', strides=(1, 1), data_format='channels_last', use_bias=True),
    Conv2D(3, (5,5), activation='relu', padding='valid', strides=(1, 1), data_format='channels_last', use_bias=True),
    Conv2D(2, (3,3), activation='linear', padding='valid', strides=(1, 1), data_format='channels_last', use_bias=True)
]

input_layer = Input(shape=data_in_shape)
x = layers[0](input_layer)
for layer in layers[1:-1]:
    x = layer(x)
output_layer = layers[-1](x)
model = Model(inputs=input_layer, outputs=output_layer)

np.random.seed(random_seed)
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(random_seed + i)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)

result = model.predict(np.array([data_in]))
data_out_shape = result[0].shape
data_in_formatted = format_decimal(data_in.ravel().tolist())
data_out_formatted = format_decimal(result[0].ravel().tolist())

DATA['pipeline_01'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}


    

In [5]:

    

import os

filename = '../../test/data/pipeline/01.json'
if not os.path.exists(os.path.dirname(filename)):
    os.makedirs(os.path.dirname(filename))
with open(filename, 'w') as f:
    json.dump(DATA, f)


    

In [6]:

    

print(json.dumps(DATA))


    

    




{"pipeline_01": {"input": {"data": [0.307179, -0.769986, 0.900566, -0.035617, 0.744949, -0.575335, -0.918581, -0.205611, -0.533736, 0.683481, -0.585835, 0.484939, -0.215692, -0.635487, 0.487079, -0.860836, 0.770674, 0.905289, 0.862287, -0.169138, -0.942037, 0.964055, -0.320725, 0.413374, -0.276246, -0.929788, 0.710117, 0.314507, 0.531366, 0.108174, 0.770186, 0.808395, -0.979157, -0.850887, -0.510742, -0.73339, 0.39585, -0.20359, 0.766244, -0.637985, -0.135002, -0.963714, 0.382876, -0.060619, -0.743556, 0.782674, 0.836407, -0.853758, -0.909104, -0.122854, 0.203442, -0.379546, 0.363816, -0.581974, 0.039209, 0.131978, -0.117665, -0.724888, -0.572914, -0.733256, -0.355407, -0.532226, 0.054996, 0.131942, -0.123549, -0.356255, 0.119282, 0.730691, 0.694566, -0.784366, -0.367361, -0.181043, 0.374178, 0.404471, -0.107604, -0.159356, 0.605261, 0.077235, 0.847001, -0.876185, -0.264833, 0.940798, 0.398208, 0.781951, -0.492895, 0.451054, -0.593011, 0.075024, -0.526114, -0.127016, 0.596049, -0.383182, 0.243033, -0.120704, 0.826647, 0.317372, 0.307263, -0.283084, 0.045883, -0.909825, -0.811381, 0.843224, -0.853911, 0.858835, 0.43403, 0.244621, 0.509979, -0.71666, 0.587644, 0.450806, 0.082879, -0.034831, -0.047045, -0.107934, 0.63214, -0.451297, -0.876076, 0.920593, -0.083764, 0.515784, 0.362317, 0.083556, -0.734335, -0.493589, -0.112289, 0.1175, 0.627729, -0.364343, -0.20591, 0.780336, 0.650832, -0.786626, -0.680448, 0.302439, 0.138128, 0.968167, 0.498318, 0.604799, -0.54338, 0.037427, 0.260217, -0.995629, 0.02643, -0.10162, -0.301287, -0.452637, 0.804166, -0.938051, -0.351662, -0.426815, 0.914272, -0.966934, -0.085353, -0.350128, 0.392927, 0.3855, 0.365292, 0.784261, 0.891156, -0.836385, -0.766222, -0.926262, 0.84758, 0.963802, -0.246749, -0.873048, -0.744287, 0.759778, -0.091904, -0.601018, 0.252064, 0.552859, -0.070904, -0.919266, -0.252198, 0.733837, -0.793589, -0.774978, 0.560268, -0.729902, -0.742883, -0.518994, -0.416378, -0.333527, 0.584328, -0.511306, -0.78923, 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In [ ]: