notebook.community

Edit and run 





In [1]:



    


import numpy as np
from keras.models import Model
from keras.layers import Input, Dense, RepeatVector
from keras.layers.merge import Add, Multiply, Average, Maximum, Concatenate, Dot
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()

merge.Add

[merge.Add.0]





In [4]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_2 = Add()([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Add.0'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    '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: [4.849999, 4.27]

merge.Multiply

[merge.Multiply.0]





In [5]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_2 = Multiply()([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Multiply.0'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    '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: [-17.885, -0.9408]

merge.Average

[merge.Average.0]





In [6]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_2 = Average()([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Average.0'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    '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: [2.425, 2.135]

merge.Maximum

[merge.Maximum.0]





In [7]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_2 = Maximum()([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Maximum.0'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    '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, 4.48]

merge.Concatenate

[merge.Concatenate.0] 1D inputs, axis=-1





In [8]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_2 = Concatenate(axis=-1)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Concatenate.0'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (4,)
out: [7.3, -0.21, -2.45, 4.48]

[merge.Concatenate.1] 2D inputs, axis=-1





In [9]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1a = RepeatVector(3)(layer_1a)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_1b = RepeatVector(3)(layer_1b)
layer_2 = Concatenate(axis=-1)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Concatenate.1'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (3, 4)
out: [7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48]

[merge.Concatenate.2] 2D inputs, axis=-2





In [10]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1a = RepeatVector(3)(layer_1a)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_1b = RepeatVector(3)(layer_1b)
layer_2 = Concatenate(axis=-2)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Concatenate.2'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6, 2)
out: [7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48]

[merge.Concatenate.3] 2D inputs, axis=1





In [11]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1a = RepeatVector(3)(layer_1a)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_1b = RepeatVector(3)(layer_1b)
layer_2 = Concatenate(axis=1)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Concatenate.3'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6, 2)
out: [7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48]

[merge.Concatenate.4] 2D inputs, axis=2





In [12]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1a = RepeatVector(3)(layer_1a)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_1b = RepeatVector(3)(layer_1b)
layer_2 = Concatenate(axis=2)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Concatenate.4'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (3, 4)
out: [7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48]

merge.Dot

[merge.Dot.0] 2D x 2D inputs, axes=1, normalize=False





In [13]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1a = RepeatVector(3)(layer_1a)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_1b = RepeatVector(3)(layer_1b)
layer_2 = Dot(axes=1, normalize=False)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Dot.0'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (2, 2)
out: [-53.654999, 98.112, 1.5435, -2.822401]

[merge.Dot.1] 2D x 2D inputs, axes=2, normalize=False





In [14]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1a = RepeatVector(3)(layer_1a)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_1b = RepeatVector(3)(layer_1b)
layer_2 = Dot(axes=2, normalize=False)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Dot.1'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (3, 3)
out: [-18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258]

[merge.Dot.2] 2D x 2D inputs, axes=1, normalize=True





In [15]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1a = RepeatVector(3)(layer_1a)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_1b = RepeatVector(3)(layer_1b)
layer_2 = Dot(axes=1, normalize=True)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Dot.2'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (2, 2)
out: [-1.0, 1.0, 1.0, -1.0]

[merge.Dot.3] 2D x 2D inputs, axes=2, normalize=True





In [16]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1a = RepeatVector(3)(layer_1a)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_1b = RepeatVector(3)(layer_1b)
layer_2 = Dot(axes=2, normalize=True)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Dot.3'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (3, 3)
out: [-0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843]

[merge.Dot.4] 2D x 2D inputs, axes=(2,2), normalize=True





In [17]:



    


layer_0 = Input(shape=(6,))
layer_1a = Dense(2, activation='linear')(layer_0)
layer_1a = RepeatVector(3)(layer_1a)
layer_1b = Dense(2, activation='linear')(layer_0)
layer_1b = RepeatVector(3)(layer_1b)
layer_2 = Dot(axes=(2,2), normalize=True)([layer_1a, layer_1b])
model = Model(inputs=layer_0, outputs=layer_2)

W_1a = 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_1a = np.array([0.5, 0.7])
W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))
b_1b = np.array([0.1, -0.2])
model.set_weights([W_1a, b_1a, W_1b, b_1b])

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['merge.Dot.4'] = {
    'input': {'data': data_in, 'shape': data_in_shape},
    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in [W_1a, b_1a, W_1b, b_1b]],
    'expected': {'data': data_out, 'shape': data_out_shape}
}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (3, 3)
out: [-0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843]

export for Keras.js tests





In [18]:



    


print(json.dumps(DATA))



    


















    






{"merge.Add.0": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [4.849999, 4.27], "shape": [2]}}, "merge.Multiply.0": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [-17.885, -0.9408], "shape": [2]}}, "merge.Average.0": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [2.425, 2.135], "shape": [2]}}, "merge.Maximum.0": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [7.3, 4.48], "shape": [2]}}, "merge.Concatenate.0": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [7.3, -0.21, -2.45, 4.48], "shape": [4]}}, "merge.Concatenate.1": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48], "shape": [3, 4]}}, "merge.Concatenate.2": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48], "shape": [6, 2]}}, "merge.Concatenate.3": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48], "shape": [6, 2]}}, "merge.Concatenate.4": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48], "shape": [3, 4]}}, "merge.Dot.0": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [-53.654999, 98.112, 1.5435, -2.822401], "shape": [2, 2]}}, "merge.Dot.1": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [-18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258], "shape": [3, 3]}}, "merge.Dot.2": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [-1.0, 1.0, 1.0, -1.0], "shape": [2, 2]}}, "merge.Dot.3": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [-0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843], "shape": [3, 3]}}, "merge.Dot.4": {"input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}, "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]}, {"data": [1.0, 0.0, -0.9, 0.6, -0.7, 0.0, 0.2, 0.4, 0.0, 0.0, -1.0, 2.3], "shape": [6, 2]}, {"data": [0.1, -0.2], "shape": [2]}], "expected": {"data": [-0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843], "shape": [3, 3]}}}

Content source: qinwf-nuan/keras-js

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