In [1]:
import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers.core import Reshape
from keras import backend as K
import json
from collections import OrderedDict
In [2]:
def format_decimal(arr, places=6):
return [round(x * 10**places) / 10**places for x in arr]
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DATA = OrderedDict()
[core.Reshape.0] shape [6] -> [2, 3]
In [4]:
layer_0 = Input(shape=(6,))
layer_1 = Reshape((2, 3))(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
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.Reshape.0'] = {
'input': {'data': data_in, 'shape': data_in_shape},
'expected': {'data': data_out, 'shape': data_out_shape}
}
[core.Reshape.1] shape [3, 2] -> [6]
In [5]:
layer_0 = Input(shape=(3, 2))
layer_1 = Reshape((6,))(layer_0)
model = Model(inputs=layer_0, outputs=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)### export for Keras.js tests
json.dumps(DATA)
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.Reshape.1'] = {
'input': {'data': data_in, 'shape': data_in_shape},
'expected': {'data': data_out, 'shape': data_out_shape}
}
[core.Reshape.2] shape [3, 2, 2] -> [4, 3]
In [6]:
layer_0 = Input(shape=(3, 2, 2))
layer_1 = Reshape((4, 3))(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
data_in = [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]
data_in_shape = (3, 2, 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]
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.Reshape.2'] = {
'input': {'data': data_in, 'shape': data_in_shape},
'expected': {'data': data_out, 'shape': data_out_shape}
}
In [7]:
print(json.dumps(DATA))
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