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Edit and run 





In [1]:



    


import numpy as np
from keras import activations
from keras import backend as K
import json
from collections import OrderedDict



    


















    






Using TensorFlow backend.

















In [2]:



    


def format_decimal(arr):
    return [round(x * 1e6) / 1e6 for x in arr]



    











In [3]:



    


DATA = OrderedDict()

softmax

[activations.softmax.0] 1D





In [4]:



    


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 = activations.softmax(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.softmax.0'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                 'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6,)
out: [0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965]

[activations.softmax.1] 2D





In [5]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 6)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.softmax(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.softmax.1'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                 'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
in shape: (2, 6)
out shape: (2, 6)
out: [0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965, 0.107768, 0.149902, 0.11105, 0.247147, 0.082268, 0.301865]

[activations.softmax.2] 1D





In [6]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 90]
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 = activations.softmax(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.softmax.2'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                 'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 90]
in shape: (6,)
out shape: (6,)
out: [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]

elu

[activations.elu.0] 1D





In [7]:



    


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 = activations.elu(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.elu.0'] = {'input': {'data': data_in, 'shape': data_in_shape},
                             'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6,)
out: [0.0, 0.2, 0.5, -0.095163, 1.0, 2.0]

[activations.elu.1] 2D





In [8]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 6)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.elu(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.elu.1'] = {'input': {'data': data_in, 'shape': data_in_shape},
                             'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
in shape: (2, 6)
out shape: (2, 6)
out: [0.0, 0.2, 0.5, -0.095163, 1.0, 2.0, -0.029555, 0.3, 0.0, 0.8, -0.259182, 1.0]

[activations.elu.2] 3D





In [9]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.elu(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.elu.2'] = {'input': {'data': data_in, 'shape': data_in_shape},
                             'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.0, 0.2, -0.393469, -0.095163, 1.0, 2.0, -0.029555, 2.3, 0.0, 0.8, -0.259182, 1.0]

[activations.elu.3] 3D, alpha=0.5





In [10]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.elu(K.variable(np.array([arr_in])), alpha=0.5)

arr_out = K.eval(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['activations.elu.3'] = {'input': {'data': data_in, 'shape': data_in_shape},
                             'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.0, 0.2, -0.196735, -0.047581, 1.0, 2.0, -0.014777, 2.3, 0.0, 0.8, -0.129591, 1.0]

softplus

[activations.softplus.0] 1D





In [11]:



    


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 = activations.softplus(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.softplus.0'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                  'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6,)
out: [0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928]

[activations.softplus.1] 2D





In [12]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 6)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.softplus(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.softplus.1'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                  'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
in shape: (2, 6)
out shape: (2, 6)
out: [0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928, 0.67826, 0.854355, 0.693147, 1.171101, 0.554355, 1.313262]

[activations.softplus.2] 3D





In [13]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.softplus(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.softplus.2'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                  'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.693147, 0.798139, 0.474077, 0.644397, 1.313262, 2.126928, 0.67826, 2.395545, 0.693147, 1.171101, 0.554355, 1.313262]

softsign

[activations.softsign.0] 1D





In [14]:



    


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 = activations.softsign(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.softsign.0'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                  'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6,)
out: [0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667]

[activations.softsign.1] 2D





In [15]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 6)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.softsign(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.softsign.1'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                  'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
in shape: (2, 6)
out shape: (2, 6)
out: [0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.230769, 0.0, 0.444444, -0.230769, 0.5]

[activations.softsign.2] 3D





In [16]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.softsign(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.softsign.2'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                  'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.0, 0.166667, -0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.69697, 0.0, 0.444444, -0.230769, 0.5]

ReLU

[activations.relu.0] 1D





In [17]:



    


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 = activations.relu(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.relu.0'] = {'input': {'data': data_in, 'shape': data_in_shape},
                              'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6,)
out: [0.0, 0.2, 0.5, 0.0, 1.0, 2.0]

[activations.relu.1] 2D





In [18]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 6)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.relu(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.relu.1'] = {'input': {'data': data_in, 'shape': data_in_shape},
                              'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
in shape: (2, 6)
out shape: (2, 6)
out: [0.0, 0.2, 0.5, 0.0, 1.0, 2.0, 0.0, 0.3, 0.0, 0.8, 0.0, 1.0]

[activations.relu.2] 3D





In [19]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.relu(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.relu.2'] = {'input': {'data': data_in, 'shape': data_in_shape},
                              'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.0, 0.2, 0.0, 0.0, 1.0, 2.0, 0.0, 2.3, 0.0, 0.8, 0.0, 1.0]

[activations.relu.3] 3D, max_value=0.5





In [20]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.relu(K.variable(np.array([arr_in])), max_value=0.5)

arr_out = K.eval(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['activations.relu.3'] = {'input': {'data': data_in, 'shape': data_in_shape},
                              'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.0, 0.2, 0.0, 0.0, 0.5, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5]

[activations.relu.4] 3D, alpha=0.3





In [21]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.relu(K.variable(np.array([arr_in])), alpha=0.3)

arr_out = K.eval(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['activations.relu.4'] = {'input': {'data': data_in, 'shape': data_in_shape},
                              'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.0, 0.2, -0.15, -0.03, 1.0, 2.0, -0.009, 2.3, 0.0, 0.8, -0.09, 1.0]

tanh

[activations.tanh.0] 1D





In [22]:



    


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 = activations.tanh(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.tanh.0'] = {'input': {'data': data_in, 'shape': data_in_shape},
                              'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6,)
out: [0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028]

[activations.tanh.1] 2D





In [23]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 6)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.tanh(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.tanh.1'] = {'input': {'data': data_in, 'shape': data_in_shape},
                              'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
in shape: (2, 6)
out shape: (2, 6)
out: [0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.291313, 0.0, 0.664037, -0.291313, 0.761594]

[activations.tanh.2] 3D





In [24]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.tanh(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.tanh.2'] = {'input': {'data': data_in, 'shape': data_in_shape},
                              'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.0, 0.197375, -0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.980096, 0.0, 0.664037, -0.291313, 0.761594]

sigmoid

[activations.sigmoid.0] 1D





In [25]:



    


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 = activations.sigmoid(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.sigmoid.0'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                 'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6,)
out: [0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797]

[activations.sigmoid.1] 2D





In [26]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 6)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.sigmoid(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.sigmoid.1'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                 'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
in shape: (2, 6)
out shape: (2, 6)
out: [0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797, 0.492501, 0.574443, 0.5, 0.689974, 0.425557, 0.731059]

[activations.sigmoid.2] 3D





In [27]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.sigmoid(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.sigmoid.2'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                 'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.5, 0.549834, 0.377541, 0.475021, 0.731059, 0.880797, 0.492501, 0.908877, 0.5, 0.689974, 0.425557, 0.731059]

hard_sigmoid

[activations.hard_sigmoid.0] 1D





In [28]:



    


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 = activations.hard_sigmoid(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.hard_sigmoid.0'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                      'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6,)
out: [0.5, 0.54, 0.6, 0.48, 0.7, 0.9]

[activations.hard_sigmoid.1] 2D





In [29]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 6)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.hard_sigmoid(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.hard_sigmoid.1'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                      'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
in shape: (2, 6)
out shape: (2, 6)
out: [0.5, 0.54, 0.6, 0.48, 0.7, 0.9, 0.494, 0.56, 0.5, 0.66, 0.44, 0.7]

[activations.hard_sigmoid.2] 3D





In [30]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.hard_sigmoid(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.hard_sigmoid.2'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                      'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.5, 0.54, 0.4, 0.48, 0.7, 0.9, 0.494, 0.96, 0.5, 0.66, 0.44, 0.7]

linear

[activations.linear.0] 1D





In [31]:



    


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 = activations.linear(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.linear.0'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2]
in shape: (6,)
out shape: (6,)
out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0]

[activations.linear.1] 2D





In [32]:



    


data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 6)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.linear(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.linear.1'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]
in shape: (2, 6)
out shape: (2, 6)
out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0, -0.03, 0.3, 0.0, 0.8, -0.3, 1.0]

[activations.linear.2] 3D





In [33]:



    


data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
data_in_shape = (2, 2, 3)
print('in:', data_in)
print('in shape:', data_in_shape)
arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)

result = activations.linear(K.variable(np.array([arr_in])))

arr_out = K.eval(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['activations.linear.2'] = {'input': {'data': data_in, 'shape': data_in_shape},
                                'expected': {'data': data_out, 'shape': data_out_shape}}



    


















    






in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]
in shape: (2, 2, 3)
out shape: (2, 2, 3)
out: [0.0, 0.2, -0.5, -0.1, 1.0, 2.0, -0.03, 2.3, 0.0, 0.8, -0.3, 1.0]

export for Keras.js tests





In [34]:



    


print(json.dumps(DATA))



    


















    






{"activations.softmax.0": {"expected": {"data": [0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}}, "activations.softmax.1": {"expected": {"data": [0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965, 0.107768, 0.149902, 0.11105, 0.247147, 0.082268, 0.301865], "shape": [2, 6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], "shape": [2, 6]}}, "activations.softmax.2": {"expected": {"data": [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 90], "shape": [6]}}, "activations.elu.0": {"expected": {"data": [0.0, 0.2, 0.5, -0.095163, 1.0, 2.0], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}}, "activations.elu.1": {"expected": {"data": [0.0, 0.2, 0.5, -0.095163, 1.0, 2.0, -0.029555, 0.3, 0.0, 0.8, -0.259182, 1.0], "shape": [2, 6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], "shape": [2, 6]}}, "activations.elu.2": {"expected": {"data": [0.0, 0.2, -0.393469, -0.095163, 1.0, 2.0, -0.029555, 2.3, 0.0, 0.8, -0.259182, 1.0], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.elu.3": {"expected": {"data": [0.0, 0.2, -0.196735, -0.047581, 1.0, 2.0, -0.014777, 2.3, 0.0, 0.8, -0.129591, 1.0], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.softplus.0": {"expected": {"data": [0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}}, "activations.softplus.1": {"expected": {"data": [0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928, 0.67826, 0.854355, 0.693147, 1.171101, 0.554355, 1.313262], "shape": [2, 6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], "shape": [2, 6]}}, "activations.softplus.2": {"expected": {"data": [0.693147, 0.798139, 0.474077, 0.644397, 1.313262, 2.126928, 0.67826, 2.395545, 0.693147, 1.171101, 0.554355, 1.313262], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.softsign.0": {"expected": {"data": [0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}}, "activations.softsign.1": {"expected": {"data": [0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.230769, 0.0, 0.444444, -0.230769, 0.5], "shape": [2, 6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], "shape": [2, 6]}}, "activations.softsign.2": {"expected": {"data": [0.0, 0.166667, -0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.69697, 0.0, 0.444444, -0.230769, 0.5], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.relu.0": {"expected": {"data": [0.0, 0.2, 0.5, 0.0, 1.0, 2.0], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}}, "activations.relu.1": {"expected": {"data": [0.0, 0.2, 0.5, 0.0, 1.0, 2.0, 0.0, 0.3, 0.0, 0.8, 0.0, 1.0], "shape": [2, 6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], "shape": [2, 6]}}, "activations.relu.2": {"expected": {"data": [0.0, 0.2, 0.0, 0.0, 1.0, 2.0, 0.0, 2.3, 0.0, 0.8, 0.0, 1.0], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.relu.3": {"expected": {"data": [0.0, 0.2, 0.0, 0.0, 0.5, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.relu.4": {"expected": {"data": [0.0, 0.2, -0.15, -0.03, 1.0, 2.0, -0.009, 2.3, 0.0, 0.8, -0.09, 1.0], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.tanh.0": {"expected": {"data": [0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}}, "activations.tanh.1": {"expected": {"data": [0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.291313, 0.0, 0.664037, -0.291313, 0.761594], "shape": [2, 6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], "shape": [2, 6]}}, "activations.tanh.2": {"expected": {"data": [0.0, 0.197375, -0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.980096, 0.0, 0.664037, -0.291313, 0.761594], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.sigmoid.0": {"expected": {"data": [0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}}, "activations.sigmoid.1": {"expected": {"data": [0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797, 0.492501, 0.574443, 0.5, 0.689974, 0.425557, 0.731059], "shape": [2, 6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], "shape": [2, 6]}}, "activations.sigmoid.2": {"expected": {"data": [0.5, 0.549834, 0.377541, 0.475021, 0.731059, 0.880797, 0.492501, 0.908877, 0.5, 0.689974, 0.425557, 0.731059], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.hard_sigmoid.0": {"expected": {"data": [0.5, 0.54, 0.6, 0.48, 0.7, 0.9], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}}, "activations.hard_sigmoid.1": {"expected": {"data": [0.5, 0.54, 0.6, 0.48, 0.7, 0.9, 0.494, 0.56, 0.5, 0.66, 0.44, 0.7], "shape": [2, 6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], "shape": [2, 6]}}, "activations.hard_sigmoid.2": {"expected": {"data": [0.5, 0.54, 0.4, 0.48, 0.7, 0.9, 0.494, 0.96, 0.5, 0.66, 0.44, 0.7], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}, "activations.linear.0": {"expected": {"data": [0.0, 0.2, 0.5, -0.1, 1.0, 2.0], "shape": [6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2], "shape": [6]}}, "activations.linear.1": {"expected": {"data": [0.0, 0.2, 0.5, -0.1, 1.0, 2.0, -0.03, 0.3, 0.0, 0.8, -0.3, 1.0], "shape": [2, 6]}, "input": {"data": [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], "shape": [2, 6]}}, "activations.linear.2": {"expected": {"data": [0.0, 0.2, -0.5, -0.1, 1.0, 2.0, -0.03, 2.3, 0.0, 0.8, -0.3, 1.0], "shape": [2, 2, 3]}, "input": {"data": [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], "shape": [2, 2, 3]}}}

Content source: qinwf-nuan/keras-js

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