<jupyter_start><jupyter_text>Cropping3D **[convolutional.Cropping3D.0] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, dim_ordering=tf**<jupyter_code>data_in_shape = (3, 5, 3, 3)
L = Cropping3D(cropping=((1,1), (1,1), (1,1)), dim_ordering='tf')

layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(input=layer_0, output=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(260)
data_in = 2 * np.random.random(data_in_shape) - 1
print('')
print('in shape:', data_in_shape)
print('in:', format_decimal(data_in.ravel().tolist()))
result = model.predict(np.array([data_in]))
print('out shape:', result[0].shape)
print('out:', format_decimal(result[0].ravel().tolist()))<jupyter_output>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, -0.9522[...]<jupyter_text>**[convolutional.Cropping3D.1] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, dim_ordering=th**<jupyter_code>data_in_shape = (3, 5, 3, 3)
L = Cropping3D(cropping=((1,1), (1,1), (1,1)), dim_ordering='th')

layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(input=layer_0, output=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(260)
data_in = 2 * np.random.random(data_in_shape) - 1
print('')
print('in shape:', data_in_shape)
print('in:', format_decimal(data_in.ravel().tolist()))
result = model.predict(np.array([data_in]))
print('out shape:', result[0].shape)
print('out:', format_decimal(result[0].ravel().tolist()))<jupyter_output>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, -0.9522[...]<jupyter_text>**[convolutional.Cropping3D.2] cropping (3,2), (2,1), (2,3)) on 7x6x6x6 input, dim_ordering=tf**<jupyter_code>data_in_shape = (7, 6, 6, 6)
L = Cropping3D(cropping=((3,2), (2,1), (2,3)), dim_ordering='tf')

layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(input=layer_0, output=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(262)
data_in = 2 * np.random.random(data_in_shape) - 1
print('')
print('in shape:', data_in_shape)
print('in:', format_decimal(data_in.ravel().tolist()))
result = model.predict(np.array([data_in]))
print('out shape:', result[0].shape)
print('out:', format_decimal(result[0].ravel().tolist()))<jupyter_output>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, 0.09729[...]<jupyter_text>**[convolutional.Cropping3D.3] cropping (3,2), (2,1), (2,3)) on 7x6x6x6 input, dim_ordering=th**<jupyter_code>data_in_shape = (7, 6, 6, 6)
L = Cropping3D(cropping=((3,2), (2,1), (2,3)), dim_ordering='th')


layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(input=layer_0, output=layer_1)

# set weights to random (use seed for reproducibility)
np.random.seed(262)
data_in = 2 * np.random.random(data_in_shape) - 1
print('')
print('in shape:', data_in_shape)
print('in:', format_decimal(data_in.ravel().tolist()))
result = model.predict(np.array([data_in]))
print('out shape:', result[0].shape)
print('out:', format_decimal(result[0].ravel().tolist()))<jupyter_output>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, 0.09729[...]
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