from session import Session
import numpy as np
# Create a new graph
Graph().as_default()
X = Placeholder()
c = Placeholder()
# Create a weight matrix for 2 outout classes:
# One with a weight vector (1, 1) for blue and one with a
# weight vector (-1, -1) for red
W = Variable([[1, -1], [1, -1]])
b = Variable([0, 0])
p = softmax(add(matmul(X, W), b))
# Cross-entropy loss
J = negative(reduce_sum(reduce_sum(multiply(c, log(p)), axis=1)))
# Create red points centered at (-2, -2)
red_points = np.random.randn(50, 2) - 2 * np.ones((50, 2))
# Create blue points centered at (2, 2)
blue_points = np.random.randn(50, 2) + 2 * np.ones((50, 2))
session = Session()
print(
session.run(
J, {
X: np.concatenate((blue_points, red_points)),
c: [[1, 0]] * len(blue_points) + [[0, 1]] * len(red_points)
#!/usr/bin/env python # -*- coding: utf-8 -*- """ An example that builds the graph which performs the following transformation: (1 0) (1) z = (0 -1)* x + (1) """ from graph import Graph from graph import Variable from graph import Placeholder from operations import matmul from operations import add # Create a new graph Graph().as_default() # Create variables A = Variable([[1, 0], [0, -1]]) b = Variable([1, 1]) # Create placeholder x = Placeholder() # Create hidden node y y = matmul(A, x) # Create output node z z = add(y, b)
#!/usr/bin/env python
#! -*- coding: utf-8 -*-
""" Build a perceptron that classifies a point between two sets
which are divided by a line between (4,0) and (0,4).
"""
from graph import Graph
from graph import Variable
from graph import Placeholder
from operations import matmul
from operations import add
from operations import sigmoid
from session import Session
# Create a new graph
Graph().as_default()
x = Placeholder()
w = Variable([1, 1])
b = Variable(0)
p = sigmoid(add(matmul(w, x), b))
# Let's try it to calculate the probability for point (3,2)T
# being a blue point (being over the line or p(wTx+b) > 0.5)
session = Session()
print(session.run(p, {x: [3, 2]}))