Example: Training the perceptron

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# Create a new graph
Graph().as_default()
X = placeholder()
c = placeholder()
# Initialize weights randomly
W = Variable(np.random.randn(2, 2))
b = Variable(np.random.randn(2))
# Build perceptron
p = softmax(add(matmul(X, W), b))
# Build cross-entropy loss
J = negative(reduce_sum(reduce_sum(multiply(c, log(p)), axis=1)))
# Build minimization op
minimization_op = GradientDescentOptimizer(learning_rate=0.01).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(100):
    J_value = session.run(J, feed_dict)
    if step % 10 == 0:
        print("Step:", step, " Loss:", J_value)
    session.run(minimization_op, feed_dict)
# Print final result
W_value = session.run(W)
print("Weight matrix:\n", W_value)
b_value = session.run(b)
print("Bias:\n", b_value)
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