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.layers.pooling import MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
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()

pipeline 6


In [4]:
random_seed = 1006
data_in_shape = (24, 24, 2)

layers = [
    Conv2D(5, (3,3), activation='relu', padding='valid', strides=(2,2), data_format='channels_last', use_bias=True),
    BatchNormalization(epsilon=1e-03, axis=-1, center=True, scale=True),
    Conv2D(4, (3,3), activation='relu', padding='same', strides=(1,1), data_format='channels_last', use_bias=True),
    BatchNormalization(epsilon=1e-03, axis=-1, center=True, scale=True),
    Conv2D(3, (3,3), activation='relu', padding='same', strides=(1,1), data_format='channels_last', use_bias=True),
    BatchNormalization(epsilon=1e-03, axis=-1, center=True, scale=True),
    MaxPooling2D(pool_size=(2,2), strides=None, padding='valid', data_format='channels_last'),
    Conv2D(4, (3,3), activation='linear', padding='valid', strides=(1,1), data_format='channels_last', use_bias=True),
    BatchNormalization(epsilon=1e-03, axis=-1, center=True, scale=True),
    Conv2D(2, (3,3), activation='relu', padding='same', strides=(1,1), data_format='channels_last', use_bias=True),
    BatchNormalization(epsilon=1e-03, axis=-1, center=True, scale=True),
    MaxPooling2D(pool_size=(2,2), strides=None, padding='valid', data_format='channels_last')
]

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)
    if i % 6 == 5:
        # std should be positive
        weights.append(0.5 * np.random.random(w.shape))
    else:
        weights.append(np.random.random(w.shape) - 0.5)
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_06'] = {
    '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}
}

export for Keras.js tests


In [5]:
import os

filename = '../../test/data/pipeline/06.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_06": {"input": {"data": [0.486255, -0.547151, 0.285068, 0.764711, 0.481398, 0.442527, -0.409304, 0.051033, -0.652471, 0.623918, 0.698811, -0.48696, -0.525531, -0.083229, -0.54216, -0.595979, 0.965361, 0.961457, 0.469608, -0.18139, 0.237622, -0.841546, -0.201479, 0.210842, -0.099026, -0.017468, -0.270985, -0.421947, 0.990418, 0.633556, -0.46994, -0.283905, 0.339371, 0.851372, -0.963439, 0.672347, -0.592494, 0.115008, 0.77155, -0.629049, -0.284972, 0.08256, -0.526964, -0.579017, 0.048964, 0.296374, -0.503246, -0.95555, -0.759658, -0.148746, 0.527992, 0.419541, -0.601167, -0.246472, 0.611566, -0.47989, -0.796678, 0.845136, -0.929013, 0.081316, -0.965527, 0.064677, 0.687209, -0.781686, 0.556524, -0.294628, 0.343468, -0.693394, -0.864068, 0.522942, -0.854592, 0.954066, 0.352462, 0.404271, 0.935993, 0.006064, -0.614327, -0.951249, -0.974544, -0.981322, -0.524456, -0.353175, -0.283883, 0.270072, 0.336334, -0.529043, 0.880513, -0.663035, -0.709319, -0.69236, 0.233949, 0.90419, -0.721928, 0.580281, -0.149192, -0.246252, 0.099723, 0.986128, -0.979644, 0.242715, 0.433547, 0.199869, -0.572331, -0.152181, 0.329916, -0.071652, -0.580827, 0.88984, -0.857622, -0.926973, -0.444937, -0.183938, 0.72548, -0.238406, -0.651195, -0.770945, -0.97797, -0.666038, 0.253825, 0.001102, -0.769608, -0.364219, 0.653122, -0.845224, -0.900383, 0.916435, 0.562575, 0.577639, -0.655935, 0.683806, -0.955929, 0.271965, 0.670582, -0.874893, -0.671992, -0.124948, 0.354001, -0.289044, -0.880824, -0.505697, 0.975131, -0.404046, 0.345771, -0.013626, -0.077943, 0.837888, -0.371654, 0.453362, 0.331138, -0.360725, 0.125168, -0.964794, -0.826929, 0.794106, -0.982495, -0.861929, 0.451361, -0.023568, 0.112144, 0.738027, -0.664822, 0.441946, -0.163688, 0.893386, 0.808835, -0.80181, 0.418756, 0.622756, 0.034079, 0.345499, 0.181397, -0.723234, 0.696562, -0.961237, -0.03916, 0.261942, -0.234349, 0.962729, -0.185936, 0.185842, 0.763643, 0.167632, -0.850653, -0.155428, -0.983086, 0.018397, -0.211233, 0.864641, -0.751372, 0.463791, -0.823289, -0.290048, -0.073149, 0.533656, 0.163079, 0.49321, 0.864586, 0.339837, 0.992513, -0.057005, -0.363075, 0.789408, -0.949479, 0.327304, 0.457404, -0.623596, -0.602841, -0.674863, -0.566123, -0.04016, -0.527756, -0.988547, -0.581931, 0.854939, -0.076242, -0.818141, 0.402988, 0.959873, -0.603879, 0.533945, 0.806037, 0.563679, 0.309068, -0.794625, 0.517614, -0.495755, 0.508661, -0.767147, 0.606852, -0.246746, -0.40705, 0.052749, -0.467531, 0.686256, -0.88221, -0.176934, 0.447751, 0.053891, 0.565448, 0.439325, 0.056684, 0.727521, -0.551452, -0.25285, -0.239782, -0.678919, 0.941918, -0.552664, -0.866533, -0.833687, 0.578477, -0.236515, -0.645378, 0.051962, 0.493989, 0.828093, -0.302125, -0.440988, 0.026237, 0.982092, -0.048101, -0.755464, -0.37717, 0.47509, 0.653256, 0.832432, 0.855754, 0.360381, -0.17015, -0.728107, -0.784632, -0.631152, 0.924434, -0.703005, -0.06247, 0.266859, -0.41264, 0.647442, -0.850714, 0.610146, -0.925309, -0.192222, 0.85114, -0.787633, -0.27648, 0.389089, -0.549745, -0.375167, -0.8305, -0.175722, -0.703476, 0.689417, -0.45344, -0.077539, 0.51277, -0.137774, 0.509996, -0.43267, 0.093598, -0.232406, -0.083906, -0.485305, -0.361813, 0.144066, -0.043592, 0.424642, 0.986991, -0.575422, 0.108056, 0.130273, -0.295803, 0.963312, -0.708053, 0.7834, -0.878736, -0.826228, 0.795529, -0.042012, 0.68836, 0.513852, 0.454591, -0.727192, 0.28589, 0.394249, 0.719319, -0.53121, 0.203057, -0.74984, -0.435546, 0.46915, 0.176435, 0.303732, 0.801332, 0.827697, -0.183831, -0.1239, 0.990167, -0.007123, -0.090204, -0.802137, 0.63637, 0.752294, -0.682622, 0.571574, -0.821026, -0.44556, -0.737739, 0.833381, -0.700659, 0.407301, -0.108541, 0.863125, -0.221152, -0.790393, -0.958177, 0.020887, 0.816695, -0.457642, -0.004841, -0.631066, -0.073435, 0.147722, -0.325494, -0.174399, -0.496922, -0.779064, 0.689366, -0.713829, 0.126345, 0.888437, -0.823531, 0.075602, 0.316403, 0.653177, 0.733758, 0.403407, -0.296278, -0.994762, -0.945548, 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-0.645419, -0.266031, 0.76425, 0.585456, 0.928782, 0.607011, 0.270196, 0.905235, -0.858239, -0.195855, 0.305792, 0.584082, -0.210509, -0.173999, 0.824324, -0.74533, 0.696131, -0.641099, 0.707403, -0.568491, 0.64085, -0.577708, -0.780788, -0.737747, -0.339846, -0.431497, -0.907344, -0.298178, 0.669754, 0.715158, 0.890816, 0.877134, 0.419016, 0.022736, 0.997763, -0.258703, 0.509531, -0.102557, 0.409674, 0.019529, -0.36995, -0.985299, 0.327835, 0.981791, -0.207778, 0.48103, -0.125416, 0.3202, 0.958831, -0.516102, -0.572185, 0.679159, -0.764687, -0.955172, -0.469998, -0.557047, -0.532824, 0.703606, -0.579514, -0.015036, 0.147105, 0.028851, -0.347213, -0.019932, 0.15606, 0.993806, 0.894112, -0.99963, -0.620094, 0.215054, 0.496588, 0.084, -0.676355, -0.551419, -0.950723, -0.830096, 0.578957, 0.039835, 0.446344, 0.904247, -0.033941, -0.81856, -0.578186, 0.057856, -0.885668, 0.951545, 0.060842, -0.010294, 0.085369, 0.522728, 0.354027, -0.294758, 0.429752, 0.328914, 0.561625, 0.710191, 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-0.857551, -0.178259, -0.852572, -0.291459, 0.202821, -0.593399, 0.328959, -0.669342, 0.711407, 0.431166, 0.046969, -0.711472, 0.81955, -0.497522, 0.428023, -0.370867, -0.575357, -0.79154, 0.051436, 0.071935, -0.874042, 0.687667, 0.376745, 0.001103, -0.221096, -0.713609, -0.249726, 0.696809, -0.463919, -0.956152, 0.921492, 0.051743, 0.187907, 0.654332, 0.753417, 0.939616, -0.646714, -0.624903, 0.710668, 0.690062, -0.562926, 0.42977, -0.918019, 0.620161, -0.195706, -0.14817, -0.360041, 0.854987, 0.277203, 0.50889, -0.658715, 0.538804, 0.769241, -0.981708, 0.259073, 0.069941, 0.370966, 0.899523, -0.00579, -0.043348, 0.864144, -0.09708, -0.628859, 0.181087, 0.517733, 0.983056, -0.26079, -0.802774, 0.304272, 0.888787, 0.161273, 0.628118, 0.663029, 0.363542, 0.431043, -0.616303, -0.883195, 0.233033, -0.954338, 0.614977, 0.970322, 0.497869, 0.406102, -0.622701, 0.632102, 0.486736, -0.967365, 0.933694, -0.716667, -0.911851, -0.350433, 0.762935, -0.802107, 0.050819, -0.712459, -0.259422, 0.117084, 0.620313, -0.198907, -0.709825, 0.025051, 0.384125, 0.750688, 0.28824, 0.698855, 0.862429, -0.50643, 0.414703, 0.286667, -0.470255, 0.829836, 0.887914, 0.675373, 0.225466, -0.756888, 0.553497, 0.64819, -0.52851, -0.32858, -0.625153, 0.537321, -0.483682, -0.073723, 0.985251, -0.148753, -0.127375, -0.378964, -0.030468, -0.30716, -0.698805, -0.701457, -0.385274, -0.560848, -0.71119, 0.031465, 0.310515, -0.140853, 0.612563, 0.673827, -0.51118, -0.505356, 0.06278, -0.527185, 0.839937, 0.852363, -0.514123, -0.572813, -0.764201, -0.943209, 0.014288, 0.599129, 0.802254, -0.252392, -0.052557, -0.644723, -0.549447, 0.191711, -0.242638, -0.56279, -0.526064, 0.346034, -0.005556, 0.718985, -0.78946, -0.496133, 0.870892, -0.354525, 0.022894, 0.424526, 0.672151, -0.404306, 0.670724, 0.898511, 0.361963, 0.571205, 0.027766, -0.876006, 0.570736, -0.015329, -0.81362, -0.090709, -0.258103, -0.891695, -0.805445, -0.915697, 0.079103, -0.55349, 0.159306, 0.899196, 0.476916, 0.211369, -0.169569, 0.358602, -0.079723, -0.189892, -0.65019, -0.491215, 0.7682, -0.317483, -0.993848, 0.669241, -0.787503, -0.192175, 0.618152, 0.636964, 0.178092, -0.127657, -0.683083, -0.696914, 0.149576, 0.161589, 0.738768, -0.840285, -0.232935, -0.294932, 0.36209, -0.93838, -0.80207, 0.947688, -0.907391, -0.297309, 0.286899, -0.82987, 0.007642, 0.986445, 0.578582, 0.124038, 0.252478, 0.128255, 0.106001, -0.833099, 0.691718, -0.321365, 0.999563, 0.240999, 0.297526, 0.277837, 0.305819, 0.550701, 0.974198, -0.489469, -0.940198, 0.14797, -0.893833, -0.248261, -0.788778, -0.687953, -0.194255, 0.596787, -0.406853, -0.998092, 0.312146, 0.075569, -0.974178, -0.238176, 0.658012, 0.887835, -0.193192, 0.237335, 0.859391, 0.530296, 0.455379, -0.380185, -0.372228, -0.391174, -0.534258, 0.310863, -0.886344, -0.219225, -0.803269, 0.360038, -0.664033, -0.696736, -0.148953, -0.014585, -0.185184, 0.83197, 0.104067, -0.833771, 0.308997, 0.982954, 0.334785, -0.971828, -0.901457, -0.405818, 0.68634, -0.545575, 0.404335, 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In [ ]: