In [1]:

    

import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers.core import Dense, Dropout
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()


    

In [4]:

    

layer_0 = Input(shape=(6,))
layer_1 = Dense(2)(layer_0)
layer_2 = Dropout(0.5)(layer_1)
model = Model(inputs=layer_0, outputs=layer_2)

W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))
b = np.array([0.5, 0.7])
model.set_weights([W, b])

data_in = [0, 0.2, 0.5, -0.1, 1, 2]
data_in_shape = (6,)
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]
data_out_shape = arr_out.shape
print('out shape:', data_out_shape)
data_out = format_decimal(arr_out.ravel().tolist())
print('out:', data_out)

DATA['core.Dropout.0'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W, b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}


    

    




in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (2,)
out: [7.3, -0.21]

In [5]:

    

print(json.dumps(DATA))


    

    




{"core.Dropout.0": {"weights": [{"data": [0.1, 0.4, 0.5, 0.1, 1.0, -2.0, 0.0, 0.3, 0.2, 0.1, 3.0, 0.0], "shape": [6, 2]}, {"data": [0.5, 0.7], "shape": [2]}], "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "expected": {"data": [7.3, -0.21], "shape": [2]}}}

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