Post your solutions to the Oct 25 In-Class Exercise Here.

Best, Chris

import tensorflow as tf

def perceptron(weights, inputs, biases, activation): nodes = weights * inputs + biases return activation(nodes)

def step(nodes): return tf.ceil(tf.clip_by_value(nodes, 0, 1))

x = tf.placeholder(tf.float32, shape=(3))

W = tf.Variable([[2, 2, 2], [-2/3, -2/3, -2/3], [2/5, 2/5, 2/5]], dtype=tf.float32)

bias = tf.zeros((3), dtype=tf.float32)

layer1 = perceptron(W, x, bias, step) layer2 = perceptron(W, layer1, bias, step)

session = tf.Session()

TODO

import tensorflow as tf import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

def perceptron(weights, inputs, biases, activation): #print(tf.shape(weights)) #print(tf.shape(inputs)) nodes = tf.matmul(weights, inputs) + biases return activation(nodes)

def step(nodes): return tf.ceil(tf.clip_by_value(nodes, 0, 1))

x = tf.placeholder(tf.float32, shape=(3, 1)) W1 = tf.Variable([[2, 2, 2], [-2 / 3, -2 / 3, -2 / 3], [2 / 5, 2 / 5, 2 / 5]], dtype=tf.float32) b1 = tf.Variable([[-1], [1], [-1]], dtype=tf.float32)

W2 = tf.Variable([[1/2, 1/2, 1]], dtype=tf.float32) b2 = tf.Variable(-1, dtype=tf.float32)

[[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] my_layer1 = perceptron(W1, x, b1, step)

my_layer2 = perceptron(W2, my_layer1, b2, step)

session = tf.Session() init = tf.global_variables_initializer() session.run(init) l1 = session.run(my_layer1, {x: [[1.0], [1.0], [1.0]]}) print(tf.shape(l1)) print("L1 layer") print(l1)

print("L2 layer") print(session.run(my_layer2, {x: l1}))

outputs

array([[ 1., 1.],

[ 1., 1.]], dtype=float32)

Name : Kunal Deshmukh

import tensorflow as tf

W1 = tf.Variable([[2,2,2],[-1.5,-1.5,-1.5],[0.5,0.5,1]], dtype=tf.float32) b1 = tf.Variable([[-1,-1,-1]], dtype=tf.float32) W2 = tf.Variable([[2,2,1]], dtype=tf.float32) b2 = tf.Variable([[1]], dtype=tf.float32) session = tf.Session() init = tf.global_variables_initializer() session.run(init) session.run(W) session.run(b)

def perceptron(weights, inputs, biases, activation): nodes = weights * inputs + biases return activation(nodes)

def step(nodes): return tf.ceil(tf.clip_by_value(nodes, 0, 1))

x = tf.placeholder(tf.float32, shape=(2)) my_layer1 = perceptron(W1, x, b1, step) my_layer2 = perceptron(W2,my_layer1,b2,step) session = tf.Session() init = tf.global_variables_initializer() session.run(init) session.run(my_layer1, {x:[1,1,1]}) session.run(my_layer2, {x:[1,1,1]})

import tensorflow as tf import os

def perceptron(weights, inputs, biases, activation): nodes = weights * inputs + biases return activation(nodes)

def step(nodes): return tf.ceil(tf.clip_by_value(nodes, 0, 1))

x = tf.placeholder(tf.float32, shape=(3))

W1 = tf.Variable([[2, 2, 2], [-2 / 3, -2 / 3, -2 / 3], [2 / 5, 2 / 5, 2 / 5]], dtype=tf.float32)

b1 = tf.Variable([0.0, 0.0, 0.0], dtype=tf.float32)

W2 = tf.Variable([1 / 2, 1 / 2, 1], dtype=tf.float32) b2 = tf.Variable(0.0, dtype=tf.float32)

layer1 = perceptron(W1, x, b1, step)

layer2 = perceptron(W2, my_layer1, b2, step)

session = tf.Session()

init = tf.global_variables_initializer()

session.run(init)

l1= session.run(my_layer1, {x: [0.0, 1.0, 0.0]})

print(l1) print(session.run(layer2, {x:l1[0]}))

import tensorflow as tf

def perceptron(weights, inputs, biases, activation): nodes = tf.add(tf.matmul(weights,inputs), biases) return activation(nodes)

def step(nodes): return tf.ceil(tf.clip_by_value(nodes, 0, 1))

uniform_init = tf.random_uniform_initializer(0, 1)

x = tf.Variable([1,0,0], dtype=tf.float32)

x = tf.placeholder(tf.float32, shape=(3,1))

W1 = tf.Variable( ([[2,2,2], [-2/3, -2/3, -2/3], [1/2.5, 1/2.5, 1/2.5]]), dtype=tf.float32) b1 = tf.Variable([[-1],[1],[-1]], dtype=tf.float32)

W2 = tf.Variable( ([[1/2, 1/2, 1]]), dtype=tf.float32) b2 = tf.Variable([-1], dtype=tf.float32)

G1 checks x+y+z > 1/2

W1 = tf.Variable([2,2,2], dtype=tf.float32)

G2 checks x+y+z < 3/2

W2 = tf.Variable([-2/3,-2/3,-2/3], dtype=tf.float32)

G2 checks x+y+z > 2.5

W3 = tf.Variable([1/2.5,1/2.5,1/2.5], dtype=tf.float32)

b1 = tf.placeholder(tf.float32, shape=(3,3))

W1 = tf.get_variable("W1", shape=[2, 2], initializer=uniform_init)

b1 = tf.get_variable("b1", initializer=tf.zeros_initializer)

my_layer1 = perceptron(W1, x, b1, step)

session = tf.Session() init = tf.global_variables_initializer()

l1_out = tf.placeholder(tf.float32)

#OutG1+OutG2+2OutG3≥2

my_layer2 = perceptron(W2, my_layer1,b2, step)

print(session.run(init))

print(session.run(my_layer1, {x: [[0], [1], [1]]}))

print(session.run(my_layer2, {x: [[0], [1], [1]]})) print(session.run(my_layer2, {x: [[0], [1], [1]]}))

print(session.run(my_layer2, {x: [[1], [1], [1]]}))

print(session.run(my_layer2, {x: [[1], [0], [1]]}))

print(session.run(my_layer2, {x: [[0], [0], [1]]}))

print(session.run(my_layer2, {x: [[0], [0], [0]]}))

import tensorflow as tf

import os

def perceptron(weights, inputs, biases, activation): nodes = weights * inputs + biases return activation(nodes)

def step(nodes): return tf.ceil(tf.clip_by_value(nodes, 0, 1))

x = tf.placeholder(tf.float32, shape=(3))

W1 = tf.Variable([[2, 2, 2], [-2 / 3, -2 / 3, -2 / 3], [2 / 5, 2 / 5, 2 / 5]], dtype=tf.float32)

b1 = tf.Variable([0.0, 0.0, 0.0], dtype=tf.float32)

W2 = tf.Variable([1 / 2, 1 / 2, 1], dtype=tf.float32) b2 = tf.Variable(0.0, dtype=tf.float32)

layer1 = perceptron(W1, x, b1, step)

layer2 = perceptron(W2, my_layer1, b2, step)

session = tf.Session()

init = tf.global_variables_initializer()

session.run(init)

l1= session.run(my_layer1, {x: [0.0, 1.0, 0.0]})

print(l1) print(session.run(layer2, {x:l1[0]}))

*import tensorflow as tf *def perceptron(weights, inputs, activation):

• nodes = weights * inputs
  • return activation(nodes)

*def step(nodes):

• return tf.ceil(tf.clip_by_value(nodes, 0, 1))

*x = tf.placeholder(tf.float32, shape=(3)) *W1 = tf.constant([[2,2,2],[-2/3,-2/3,-2/3],[1/2.5,1/2.5,1/25]],tf.float32) *my_layer1 = perceptron(W1, x, step) *W2 = tf.constant([1/2,1/2,1],tf.float32) *my_layer2 = perceptron(W2, my_layer1, step) *session = tf.Session() *init = tf.global_variables_initializer() *session.run(init) *session.run(my_layer1, {x:[1,0,1]}) *print session.run(my_layer2, {x:[1,0,1]})

Did not install tensorflow before class. Hence, discussed with my classmate and wrote this: import tensorflow as tf import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' def perceptron(weights, inputs, biases, activation): #print(tf.shape(weights)) #print(tf.shape(inputs)) nodes = tf.matmul(weights, inputs) + biases return activation(nodes) def step(nodes): return tf.ceil(tf.clip_by_value(nodes, 0, 1)) x = tf.placeholder(tf.float32, shape=(3, 1)) W1 = tf.Variable([[2, 2, 2], [-2 / 3, -2 / 3, -2 / 3], [2 / 5, 2 / 5, 2 / 5]], dtype=tf.float32) b1 = tf.Variable([[-1], [1], [-1]], dtype=tf.float32) W2 = tf.Variable(1/2, 1/2, 1 , dtype=tf.float32) b2 = tf.Variable(-1, dtype=tf.float32) [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] my_layer1 = perceptron(W1, x, b1, step) my_layer2 = perceptron(W2, my_layer1, b2, step) session = tf.Session() init = tf.global_variables_initializer() session.run(init) l1 = session.run(my_layer1, {x: [[1.0], [1.0], [1.0]]}) print(tf.shape(l1)) print("L1 layer") print(l1) print("L2 layer") print(session.run(my_layer2, {x: l1})) outputs array([[ 1., 1.], [ 1., 1.]], dtype=float32)

import tensorflow as tf

def perceptron(p, activation): node = tf.reduce_sum(p) return activation(node)

point = tf.placeholder(tf.float32, shape=(None, 3))

def g1(node): return tf.cond( tf.greater_equal(tf.cast(node, tf.float32), tf.constant(0.5)), lambda: 1, lambda: 0)

def g2(node): return tf.cond( tf.greater_equal(tf.cast(node, tf.float32), tf.constant(1.5)), lambda: 1, lambda: 0)

def g3(node): return tf.cond( tf.greater_equal(tf.cast(node, tf.float32), tf.constant(2.5)), lambda: 1, lambda: 0)

def g4(node): return tf.cond( tf.greater_equal(tf.cast(node, tf.float32), tf.constant(2.0)), lambda: 1, lambda: 0)

p1 = perceptron(point, g1) p2 = perceptron(point, g2) p3 = perceptron(point, g3) p4 = perceptron([p1, p2, p3], g4)

input_data = [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [0.0, 1.0, 1.0], [1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 0.0], [1.0, 1.0, 1.0]]

init = tf.global_variables_initializer() session = tf.Session() session.run(init) result = session.run(p4, feed_dict={point: input_data}) print 'Result', result session.close()