keras-js/notebooks/core/Permute.ipynb

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import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers.core import Permute
from keras import backend as K

Using TensorFlow backend.

In [3]:

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

Permute

[core.Permute.0] shape [3, 2] -> [2, 3]

In [14]:

layer_0 = Input(shape=(3, 2))
layer_1 = Permute((2, 1))(layer_0)
model = Model(input=layer_0, output=layer_1)

data_in = [0, 0.2, 0.5, -0.1, 1, 2]
data_in_shape = (3, 2)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)
result = model.predict(np.array([arr_in]))
arr_out = result[0]
print('out shape:', arr_out.shape)
data_out = format_decimal(arr_out.ravel().tolist())
print('out:', data_out)

in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (3, 2)
out shape: (2, 3)
out: [0.0, 0.5, 1.0, 0.2, -0.1, 2.0]

[core.Permute.1] shape [2, 3, 4] -> [4, 3, 2]

In [15]:

layer_0 = Input(shape=(2, 3, 4))
layer_1 = Permute((3, 2, 1))(layer_0)
model = Model(input=layer_0, output=layer_1)

data_in = [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]
data_in_shape = (2, 3, 4)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)
result = model.predict(np.array([arr_in]))
arr_out = result[0]
print('out shape:', arr_out.shape)
data_out = format_decimal(arr_out.ravel().tolist())
print('out:', data_out)

in: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]
in shape: (2, 3, 4)
out shape: (4, 3, 2)
out: [0.0, 0.0, 1.0, 1.0, 0.5, 0.5, 0.2, 0.2, 2.0, 2.0, -0.1, -0.1, 0.5, 0.5, 0.0, 0.0, 1.0, 1.0, -0.1, -0.1, 0.2, 0.2, 2.0, 2.0]

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