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In [1]:
a=[ 0.49671415, -0.1382643 ,  0.64768854]
In [2]:
import numpy
In [10]:
numpy.array(a,ndmin=3).T
Out[10]:
array([[[ 0.49671415]],

       [[-0.1382643 ]],

       [[ 0.64768854]]])
In [11]:
import numpy as np

def sigmoid(x):
    """
    Calculate sigmoid
    """
    return 1/(1+np.exp(-x))

# Network size
N_input = 4
N_hidden = 3
N_output = 2

np.random.seed(42)
# Make some fake data
X = np.random.randn(4)

weights_input_to_hidden = np.random.normal(0, scale=0.1, size=(N_input, N_hidden))
weights_hidden_to_output = np.random.normal(0, scale=0.1, size=(N_hidden, N_output))


# TODO: Make a forward pass through the network

hidden_layer_in = np.dot(X, weights_input_to_hidden)
hidden_layer_out = sigmoid(hidden_layer_in)

print('Hidden-layer Output:')
print(hidden_layer_out)

output_layer_in = np.dot(hidden_layer_out, weights_hidden_to_output)
output_layer_out = sigmoid(output_layer_in)

print('Output-layer Output:')
print(output_layer_out)
Hidden-layer Output: [0.41492192 0.42604313 0.5002434 ] Output-layer Output: [0.49815196 0.48539772]
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