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 = 1002
data_in_shape = (16, 16, 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='valid', strides=(1,1), data_format='channels_last', use_bias=True),
    Conv2D(2, (1,1), activation='relu', padding='valid', strides=(1,1), data_format='channels_last', use_bias=True),
    Conv2D(3, (5,5), activation='relu', padding='same', strides=(1,1), data_format='channels_last', use_bias=True),
    Conv2D(2, (3,3), activation='linear', padding='same', strides=(1,1), data_format='channels_last', use_bias=True),
    Conv2D(4, (1,1), 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_02'] = {
    '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/02.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))


    

    




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-0.430779, -0.779167, -0.089611, -0.010344, 0.381565, 0.888566, -0.799082, -0.282598, -0.993037, 0.171011, 0.591692, -0.474342, -0.184681, 0.403464, -0.61967, -0.178805, 0.435739, -0.719391, -0.722968, 0.462124, -0.268135, -0.63873, -0.061148, 0.014527, -0.605344, -0.178504, -0.863282, -0.033531, -0.1711, -0.729127, 0.002226, 0.474697, 0.376, 0.234174, -0.900878, 0.779224, 0.188183], "shape": [16, 16, 2]}, "weights": [{"data": [-0.742023, -0.077688, -0.167692, 0.205448, -0.633864, -0.164175, -0.731823, 0.313236, 0.613465, -0.723716, -0.299231, 0.229032, 0.102561, 0.384949, -0.90948, -0.294898, -0.916217, -0.699031, -0.323329, -0.673445, 0.521949, -0.306796, -0.476018, -0.628623, 0.808028, -0.585043, -0.307429, -0.234868, -0.897584, 0.741743, 0.320785, 0.709132, -0.978084, 0.601894, -0.228816, -0.069558, -0.522066, -0.399597, -0.916222, 0.161549, -0.211915, 0.823372, -0.6549, -0.30403, 0.677588, -0.431259, 0.219659, -0.091937, -0.101636, -0.595218, -0.815428, 0.502932, 0.775249, 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In [ ]: