Learn practical skills, build real-world projects, and advance your career
Updated 4 years ago
a=[ 0.49671415, -0.1382643 , 0.64768854]
import numpy
numpy.array(a,ndmin=3).T
array([[[ 0.49671415]],
[[-0.1382643 ]],
[[ 0.64768854]]])
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]