Multi-Layer Perceptron
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# Create a new graph
Graph().as_default()
# Create training input placeholder
X = placeholder()
# Create placeholder for the training classes
c = placeholder()
# Build a hidden layer
W_hidden = Variable(np.random.randn(2, 2))
b_hidden = Variable(np.random.randn(2))
p_hidden = sigmoid(add(matmul(X, W_hidden), b_hidden))
# Build the output layer
W_output = Variable(np.random.randn(2, 2))
b_output = Variable(np.random.randn(2))
p_output = softmax(add(matmul(p_hidden, W_output), b_output))
# Build cross-entropy loss
J = negative(reduce_sum(reduce_sum(multiply(c, log(p_output)), axis=1)))
# Build minimization op
minimization_op = GradientDescentOptimizer(learning_rate=0.03).minimize(J)
# Build placeholder inputs
feed_dict = {
X: np.concatenate((blue_points, red_points)),
c:
[[1, 0]] * len(blue_points)
+ [[0, 1]] * len(red_points)
}
# Create session
session = Session()
# Perform 100 gradient descent steps
for step in range(1000):
J_value = session.run(J, feed_dict)
if step % 100 == 0:
print("Step:", step, " Loss:", J_value)
session.run(minimization_op, feed_dict)
# Print final result
W_hidden_value = session.run(W_hidden)
print("Hidden layer weight matrix:\n", W_hidden_value)
b_hidden_value = session.run(b_hidden)
print("Hidden layer bias:\n", b_hidden_value)
W_output_value = session.run(W_output)
print("Output layer weight matrix:\n", W_output_value)
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