<gh_stars>100-1000
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(N_input)

weights_in_hidden = np.random.normal(0, scale=0.1, size=(N_input, N_hidden))
weights_hidden_out = np.random.normal(0, scale=0.1, size=(N_hidden, N_output))

# make a forward pass through the network
hidden_layer_in = np.dot(X, weights_in_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_out)
output_layer_out = sigmoid(output_layer_in)

print('Output-layer Output:')
print(output_layer_out)

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(N_input)

weights_in_hidden = np.random.normal(0, scale=0.1, size=(N_input, N_hidden))
weights_hidden_out = np.random.normal(0, scale=0.1, size=(N_hidden, N_output))

# make a forward pass through the network
hidden_layer_in = np.dot(X, weights_in_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_out)
output_layer_out = sigmoid(output_layer_in)

print('Output-layer Output:')
print(output_layer_out)
Calculate sigmoid # network size # make some fake data # make a forward pass through the network