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In [1]:



    


import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers.convolutional import Cropping3D
from keras import backend as K
import json
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()

Cropping3D

[convolutional.Cropping3D.0] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, data_format='channels_last'





In [4]:



    


data_in_shape = (3, 5, 3, 3)
L = Cropping3D(cropping=((1,1), (1,1), (1,1)), data_format='channels_last')

layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(260)
data_in = 2 * np.random.random(data_in_shape) - 1
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())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)

DATA['convolutional.Cropping3D.0'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}



    


















    






in shape: (3, 5, 3, 3)
in: [-0.806777, -0.564841, -0.481331, 0.559626, 0.274958, -0.659222, -0.178541, 0.689453, -0.028873, 0.053859, -0.446394, -0.53406, 0.776897, -0.700858, -0.802179, -0.616515, 0.718677, 0.303042, -0.080606, -0.850593, -0.795971, 0.860487, -0.90685, 0.89858, 0.617251, 0.334305, -0.351137, -0.642574, 0.108974, -0.993964, 0.051085, -0.372012, 0.843766, 0.088025, -0.598662, 0.789035, -0.971233, -0.917609, 0.10035, -0.993908, 0.001303, 0.151006, -0.222302, -0.462642, 0.318882, 0.910461, -0.951606, -0.628088, 0.858834, 0.18648, 0.005839, -0.976863, -0.602595, -0.907466, 0.537085, 0.254335, -0.624879, -0.26462, -0.597274, 0.717515, 0.933281, -0.928165, -0.664793, 0.350419, 0.604354, 0.134613, -0.170544, 0.287505, -0.342583, -0.908204, -0.055996, 0.686364, -0.877763, 0.666511, -0.333493, -0.283194, 0.764997, 0.702478, -0.495511, 0.451364, 0.945924, -0.238126, 0.146568, -0.281422, -0.379007, 0.914172, 0.337272, 0.175728, -0.945958, -0.727577, -0.133378, -0.069779, -0.952209, 0.237581, -0.244335, 0.838199, -0.187442, 0.255861, 0.28158, -0.60797, 0.992258, 0.16382, 0.240562, 0.353587, 0.300174, 0.216722, -0.422345, -0.239941, 0.354771, 0.567014, -0.085289, 0.751301, -0.255041, -0.656808, -0.220736, -0.275667, 0.568007, 0.251274, 0.888831, 0.976139, 0.040987, 0.474905, 0.181267, 0.707542, -0.570195, 0.897656, -0.91601, -0.419443, -0.346422, -0.770603, 0.883902, -0.490007, 0.470248, -0.217687, 0.365399]
out shape: (1, 3, 1, 3)
out: [-0.26462, -0.597274, 0.717515, -0.170544, 0.287505, -0.342583, -0.283194, 0.764997, 0.702478]

[convolutional.Cropping3D.1] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, data_format='channels_first'





In [5]:



    


data_in_shape = (3, 5, 3, 3)
L = Cropping3D(cropping=((1,1), (1,1), (1,1)), data_format='channels_first')

layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(261)
data_in = 2 * np.random.random(data_in_shape) - 1
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())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)

DATA['convolutional.Cropping3D.1'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}



    


















    






in shape: (3, 5, 3, 3)
in: [0.601872, -0.028379, 0.654213, 0.217731, -0.864161, 0.422013, 0.888312, -0.714141, -0.184753, 0.224845, -0.221123, -0.847943, -0.511334, -0.871723, -0.597589, -0.889034, -0.544887, -0.004798, 0.406639, -0.35285, 0.648562, 0.325102, -0.691014, -0.77342, -0.290393, -0.724779, 0.282119, 0.149588, 0.189558, -0.216989, -0.959622, -0.6309, -0.935622, -0.799247, -0.434939, -0.46504, -0.354178, 0.299364, -0.141294, 0.7451, 0.479026, -0.401207, -0.100649, 0.210378, 0.274347, 0.18857, -0.806871, -0.319347, -0.340714, 0.158351, -0.628744, -0.831363, -0.438345, 0.476498, 0.078437, 0.440929, -0.104442, -0.298102, -0.71911, -0.639047, -0.105697, -0.206569, 0.488046, 0.537381, -0.932015, -0.026858, -0.180384, 0.689, -0.211902, -0.748449, -0.998768, 0.240241, 0.981617, -0.543443, -0.402752, -0.155179, -0.786053, -0.6814, 0.856198, 0.5014, -0.030769, 0.537266, -0.51634, -0.651155, 0.746369, -0.479882, 0.830541, 0.809405, -0.088337, 0.323661, -0.837717, 0.205022, 0.64613, 0.53008, -0.524773, -0.465825, -0.737998, 0.002655, -0.409154, 0.386546, 0.732114, 0.943808, 0.972295, 0.887986, -0.903027, -0.068767, 0.325606, -0.292748, 0.987261, -0.459732, 0.560632, 0.305507, 0.950267, 0.412453, 0.230538, 0.040849, 0.241941, 0.785678, -0.743142, -0.162099, -0.713202, 0.830204, 0.672253, 0.27976, -0.860542, 0.628966, 0.884616, -0.200211, 0.258164, -0.222392, 0.68802, 0.51867, -0.14398, -0.236862, 0.045431]
out shape: (3, 3, 1, 1)
out: [-0.871723, -0.691014, -0.6309, -0.71911, 0.689, -0.786053, 0.887986, 0.950267, 0.830204]

[convolutional.Cropping3D.2] cropping ((3,2), (2,1), (2,3)) on 7x6x6x6 input, data_format='channels_last'





In [6]:



    


data_in_shape = (7, 6, 6, 6)
L = Cropping3D(cropping=((3,2), (2,1), (2,3)), data_format='channels_last')

layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(262)
data_in = 2 * np.random.random(data_in_shape) - 1
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())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)

DATA['convolutional.Cropping3D.2'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}



    


















    






in shape: (7, 6, 6, 6)
in: [-0.215694, 0.441215, 0.116911, 0.53299, 0.883562, -0.535525, -0.869764, -0.596287, 0.576428, -0.689083, -0.132924, -0.129935, -0.17672, -0.29097, 0.590914, 0.992098, 0.908965, -0.170202, 0.640203, 0.178644, 0.866749, -0.545566, -0.827072, 0.420342, -0.076191, 0.207686, -0.908472, -0.795307, -0.948319, 0.683682, -0.563278, -0.82135, 0.391356, 0.873557, 0.101774, 0.552487, -0.805471, -0.158387, 0.737777, 0.407663, 0.024323, 0.931444, -0.813418, -0.054911, -0.8488, -0.528392, 0.31073, -0.518975, -0.334317, 0.984459, 0.876118, 0.939272, -0.286813, -0.499695, 0.741409, 0.979405, 0.384731, -0.861441, 0.956879, 0.371096, 0.395767, -0.182768, -0.04864, 0.943523, 0.252702, -0.922957, 0.084044, -0.151514, -0.104476, -0.519114, 0.170185, -0.304221, 0.648458, 0.975786, -0.026504, 0.806763, -0.551306, 0.363744, -0.253539, 0.520681, -0.555914, 0.164078, 0.069617, 0.61564, -0.814065, 0.834879, 0.448904, 0.572477, -0.634663, -0.962277, -0.511182, 0.28331, 0.456662, 0.097291, -0.612782, 0.780788, 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0.275683, 0.124566, 0.551632, -0.010923, 0.678319, 0.628851, -0.458944, 0.800552, -0.397508, 0.98454, -0.965652, -0.20582, 0.820265, -0.132816, -0.278788, -0.856228, 0.139643, 0.703275, -0.322692, -0.338363, 0.378641, -0.28617, 0.101698, 0.433472, 0.068613, -0.148242, -0.466705, 0.738394, -0.410927, 0.984962, 0.405669, 0.125866, -0.982088, 0.955039, 0.845436, -0.745603, 0.446631, 0.78486, 0.369507, -0.380944, 0.883958, 0.268992, 0.106985, 0.97518, -0.222182, 0.831121, 0.946638, -0.357391, -0.347985, 0.787543, 0.209383, 0.476927, -0.374685, 0.693189, 0.60696, -0.978613, 0.289488, 0.943956, 0.035094, -0.89281, 0.105184, -0.666919, 0.304481, -0.955062, 0.925374, 0.157778, -0.468151, -0.31179, -0.012953, 0.164448, -0.656307, 0.343733]
out shape: (2, 3, 1, 6)
out: [0.795219, -0.16835, -0.51469, 0.478591, -0.749477, 0.155347, 0.195573, 0.58147, 0.06667, 0.854165, 0.416721, 0.034649, 0.480717, 0.050545, -0.517454, 0.646335, -0.520894, 0.298304, 0.691759, -0.628298, 0.697236, 0.670352, 0.110078, -0.137484, 0.042634, -0.939601, -0.949383, -0.383965, -0.42231, -0.374712, -0.267755, -0.299397, 0.475516, 0.224848, -0.613151, 0.853631]

[convolutional.Cropping3D.3] cropping ((3,2), (2,1), (2,3)) on 7x6x6x6 input, data_format='channels_first'





In [7]:



    


data_in_shape = (7, 6, 6, 6)
L = Cropping3D(cropping=((3,2), (2,1), (2,3)), data_format='channels_first')


layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(263)
data_in = 2 * np.random.random(data_in_shape) - 1
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())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)

DATA['convolutional.Cropping3D.3'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}



    


















    






in shape: (7, 6, 6, 6)
in: [0.146568, 0.515731, 0.134536, 0.043205, 0.83382, -0.586258, 0.983022, -0.20072, 0.957428, -0.534388, 0.086582, 0.736441, 0.70589, 0.631127, -0.554697, -0.905576, 0.339993, 0.392332, -0.964003, -0.634941, 0.026777, 0.974543, 0.873564, 0.002092, -0.851041, 0.956249, 0.644129, 0.507722, -0.483208, 0.909996, -0.706016, -0.222652, -0.888891, -0.784948, -0.114098, -0.145975, -0.951592, -0.499689, -0.371706, 0.254782, 0.091264, -0.847246, 0.811012, -0.307217, 0.661773, 0.296799, 0.568937, -0.74828, 0.961128, 0.983882, -0.6555, -0.430288, 0.155451, -0.54652, 0.574678, 0.997315, -0.155701, -0.707259, 0.013075, -0.031526, 0.246938, -0.598347, -0.839284, 0.322527, -0.479531, -0.403987, -0.130843, -0.794677, -0.525219, -0.427038, -0.886764, -0.380128, -0.248709, -0.634349, 0.635426, -0.422687, 0.105558, -0.000606, 0.414652, 0.530061, 0.617177, 0.539295, -0.683667, -0.917674, -0.305118, 0.554795, 0.332407, -0.593635, 0.931082, -0.054595, -0.977532, -0.869297, -0.79651, 0.823621, -0.818625, 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out shape: (7, 1, 3, 1)
out: [-0.63443, 0.057, 0.915434, 0.774556, 0.042716, -0.481838, -0.294721, 0.381696, -0.791953, -0.192332, 0.827751, -0.999257, -0.564349, -0.094318, 0.040976, -0.325463, 0.936938, 0.469365, -0.276566, -0.262442, -0.438434]

[convolutional.Cropping3D.4] cropping (1, 2, 3) on 7x6x8x3 input, data_format='channels_last'





In [8]:



    


data_in_shape = (7, 6, 8, 3)
L = Cropping3D(cropping=(1, 2, 3), data_format='channels_last')


layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(264)
data_in = 2 * np.random.random(data_in_shape) - 1
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())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)

DATA['convolutional.Cropping3D.4'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}



    


















    






in shape: (7, 6, 8, 3)
in: [0.552932, -0.391563, 0.145352, -0.913287, 0.239969, -0.797865, 0.113016, -0.015543, 0.485166, 0.419961, -0.83219, 0.756209, 0.514834, -0.140546, 0.811136, -0.274515, 0.706663, 0.022525, -0.051553, 0.990059, -0.706901, -0.618589, 0.042919, 0.472082, 0.222432, 0.543026, -0.527015, 0.189191, 0.005629, 0.502652, 0.1252, -0.290988, -0.306937, -0.126483, 0.469071, -0.232995, -0.156825, -0.533997, 0.165413, 0.032944, -0.123496, -0.151654, -0.911871, 0.011792, -0.715693, -0.352744, 0.748372, 0.735725, 0.239828, -0.253998, -0.630254, 0.633554, 0.799631, -0.750882, 0.751927, 0.85789, 0.335157, -0.03868, -0.906432, -0.007475, -0.109547, 0.728283, -0.364401, 0.064281, 0.744773, -0.436497, -0.945798, -0.069515, 0.928977, 0.683572, -0.8726, -0.289312, 0.087812, 0.227353, -0.831422, -0.67108, 0.500418, 0.492954, 0.89731, -0.146691, 0.444627, -0.505485, -0.375672, -0.715253, 0.701297, 0.695526, -0.360664, 0.538686, 0.378611, 0.370014, -0.035763, -0.918841, -0.248974, -0.802044, 0.378476, 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out shape: (5, 2, 2, 3)
out: [0.57764, 0.132439, 0.910782, -0.636661, -0.108427, 0.512615, -0.761538, -0.253966, -0.591122, 0.252467, 0.517584, 0.562246, 0.959226, 0.264088, -0.24509, -0.525403, 0.130652, 0.710521, 0.459992, -0.548211, -0.129499, -0.512361, 0.953579, 0.8654, -0.358915, 0.925979, 0.656968, -0.385544, 0.864226, -0.375078, 0.567692, 0.367596, -0.925945, 0.041652, -0.768622, -0.563986, -0.188094, 0.252368, 0.468262, -0.29529, -0.159899, 0.067066, -0.977921, -0.118587, -0.058308, 0.243291, -0.993812, 0.439848, -0.208156, -0.344898, -0.055826, -0.797474, -0.898262, -0.566794, 0.372528, -0.204021, 0.442165, -0.678351, -0.162768, 0.719584]

[convolutional.Cropping3D.5] cropping 2 on 7x6x8x3 input, data_format='channels_last'





In [9]:



    


data_in_shape = (7, 6, 8, 3)
L = Cropping3D(cropping=2, data_format='channels_last')


layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(265)
data_in = 2 * np.random.random(data_in_shape) - 1
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())
print('')
print('in shape:', data_in_shape)
print('in:', data_in_formatted)
print('out shape:', data_out_shape)
print('out:', data_out_formatted)

DATA['convolutional.Cropping3D.5'] = {
    'input': {'data': data_in_formatted, 'shape': data_in_shape},
    'expected': {'data': data_out_formatted, 'shape': data_out_shape}
}



    


















    






in shape: (7, 6, 8, 3)
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out shape: (3, 2, 4, 3)
out: [0.368884, -0.282417, -0.217466, -0.926953, 0.424473, -0.47864, -0.452637, -0.576014, 0.481422, 0.976067, -0.631883, 0.995375, 0.1388, 0.417196, -0.503639, 0.354642, -0.503528, 0.808448, 0.517741, -0.032673, 0.539583, 0.039738, 0.964054, 0.545395, 0.567993, -0.175764, -0.155146, 0.041385, 0.065046, -0.648293, -0.010358, 0.538669, 0.594941, -0.523612, -0.504316, -0.375962, -0.358166, -0.082048, 0.547058, 0.517912, -0.471595, 0.704512, 0.124798, -0.305429, 0.105186, -0.467963, 0.389047, 0.08788, 0.781112, 0.379431, -0.84566, -0.663874, 0.15676, -0.62687, 0.70388, -0.924102, 0.918148, 0.319109, -0.660066, 0.096029, 0.212718, -0.570995, -0.556846, -0.898686, -0.077739, 0.029208, 0.146626, -0.661982, 0.75758, 0.404107, -0.113871, 0.828418]

export for Keras.js tests





In [10]:



    


print(json.dumps(DATA))



    


















    






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Content source: qinwf-nuan/keras-js

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