keras-js/notebooks/convolutional/Cropping1D.ipynb

In [10]:

import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers.convolutional import Cropping1D
from keras import backend as K

In [11]:

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

Cropping1D

[convolutional.Cropping1D.0] cropping (1,1) on 6x4 input

In [28]:

data_in_shape = (6, 4)
L = Cropping1D(cropping=(1,1))

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(240)
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()))

in shape: (6, 4)
in: [-0.412987, -0.139246, 0.567466, -0.512174, -0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.752844, 0.218213, 0.37408, 0.09404, -0.786108, -0.1961, -0.031679, -0.188902, 0.846634]
out shape: (4, 4)
out: [-0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.752844, 0.218213, 0.37408, 0.09404, -0.786108]

[convolutional.Cropping1D.1] cropping (2,3) on 6x4 input

In [27]:

data_in_shape = (6, 4)
L = Cropping1D(cropping=(2,3))

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(241)
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()))

in shape: (6, 4)
in: [0.229895, 0.156613, -0.66952, -0.996975, 0.45773, 0.684298, -0.999213, 0.276751, -0.484373, -0.506163, 0.353904, 0.513668, -0.981594, 0.78914, 0.603978, 0.204066, 0.532874, 0.866766, -0.544852, 0.876693, -0.523052, -0.716556, 0.875262, 0.544879]
out shape: (1, 4)
out: [-0.484373, -0.506163, 0.353904, 0.513668]

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