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"""

============================================================

Tensor Operations in Python Examples

============================================================

The following demonstrates naive tensor operations in Python

"""

import numpy as np

class TensorOpsInPython:

def __init__(self):

print(__doc__)

self.array_1d_y1 = [0, 1, 2, 3, 4, 5]

self.array_1d_x1 = [9, 7, 5, 3, 1, 0]

self.array_2d_x = [[0, 1, 2], [3, 4, 5]]

self.array_2d_y = [[2, 4, 6], [8, 10, 12]]

self.array_2d_x1 = [[0, 1, 2, 3, 4, 5],

[1, 2, 3, 4, 5, 6],

[2, 3, 4, 5, 6, 7]]

self.array_2d_y1 = [[3, 2, 1],

[4, 2, 0],

[0, 3, 1],

[0, 1, 0],

[2, 4, 1],

[4, 4, 1]]

self.tensor_1d_x1 = np.array(self.array_1d_x1)

self.tensor_1d_y1 = np.array(self.array_1d_y1)

self.tensor_2d_x = np.array(self.array_2d_x)

self.tensor_2d_y = np.array(self.array_2d_y)

self.tensor_2d_x1 = np.array(self.array_2d_x1)

self.tensor_2d_y1 = np.array(self.array_2d_y1)

# Naive Relu

print('\n--- Naive Relu')

print('Input')

print(self.tensor_2d_x)

x = self.naive_relu(self.tensor_2d_x)

print('\nOutput')

print(x)

print('\nNumpy')

x = np.maximum(self.tensor_2d_x,0)

print(x)

# Naive Add

print('\n--- Naive Add')

print('Input')

print(self.tensor_2d_x)

print(self.tensor_2d_y)

x = self.naive_add(self.tensor_2d_x, self.tensor_2d_y)

print('\nOutput')

print(x)

print('\nNumpy')

x = self.tensor_2d_x + self.tensor_2d_y

print(x)

# Naive Subtract

print('\n--- Naive Subtract')

print('Input')

print(self.tensor_2d_x)

print(self.tensor_2d_y)

x = self.naive_subtract(self.tensor_2d_x, self.tensor_2d_y)

print('\nOutput')

print(x)

print('\nNumpy')

x = self.tensor_2d_x - self.tensor_2d_y

print(x)

# Naive Multiply

print('\n--- Naive Multiply')

print('Input')

print(self.tensor_2d_x)

print(self.tensor_2d_y)

x = self.naive_multiply(self.tensor_2d_x, self.tensor_2d_y)

print('\nOutput')

print(x)

print('\nNumpy')

x = self.tensor_2d_x * self.tensor_2d_y

print(x)

# Naive Add Matrix and Vector

print('\n--- Naive Add Matrix and Vector')

print('Input')

print(self.tensor_2d_x1)

print(self.tensor_1d_y1)

x = self.naive_add_matrix_and_vector(self.tensor_2d_x1, self.tensor_1d_y1)

print('\nOutput')

print(x)

# Naive Vector Dot (product)

print('\n--- Naive Vector Dot (product)')

print('Input')

print(self.tensor_1d_x1)

print(self.tensor_1d_y1)

x = self.naive_vector_dot(self.tensor_1d_x1, self.tensor_1d_y1)

print('\nOutput')

print(x)

# Naive Matrix Vector Dot (product)

print('\n--- Naive Vector Dot (product)')

print('Input')

print(self.tensor_2d_x1)

print(self.tensor_1d_y1)

x = self.naive_matrix_vector_dot(self.tensor_2d_x1, self.tensor_1d_y1)

print('\nOutput')

print(x)

# Naive Matrix Vector Dot v2 reusing vector dot

print('\n--- Naive Vector Dot V2 reusing vector dot(product)')

print('Input')

print(self.tensor_2d_x1)

print(self.tensor_1d_y1)

x = self.naive_matrix_vector_dot_v2(self.tensor_2d_x1, self.tensor_1d_y1)

print('\nOutput')

print(x)

# Naive Matrix Dot

print('\n--- Naive Matrix Dot')

print('Input')

print(self.tensor_2d_x1)

print(self.tensor_2d_y1)

x = self.naive_matrix_dot(self.tensor_2d_x1, self.tensor_2d_y1)

print('\nOutput')

print(x)

# Reshape

print('\n--- Reshape')

print('Input')

print(self.tensor_2d_x)

x = self.tensor_2d_x.reshape((3, 2))

print('\nOutput after reshape to 3, 2')

print(x)

# Transpose

print('\n--- Transpose (exchanging rows and columns)')

print('Input')

print(self.tensor_2d_x)

x = np.transpose(self.tensor_2d_x)

print('\nOutput after transpose')

print(x)

@staticmethod

def naive_relu(x):

assert len(x.shape) == 2

x = x.copy()

for i in range(x.shape[0]):

for j in range(x.shape[1]):

x[i, j] = max(x[i, j], 0)

return x

@staticmethod

def naive_add(x, y):

assert len(x.shape) == 2

assert x.shape == y.shape

x = x.copy()

for i in range(x.shape[0]):

for j in range(x.shape[1]):

x[i, j] += y[i, j]

return x

@staticmethod

def naive_subtract(x, y):

assert len(x.shape) == 2

assert x.shape == y.shape

x = x.copy()

for i in range(x.shape[0]):

for j in range(x.shape[1]):

x[i, j] -= y[i, j]

return x

@staticmethod

def naive_multiply(x, y):

assert len(x.shape) == 2

assert x.shape == y.shape

x = x.copy()

for i in range(x.shape[0]):

for j in range(x.shape[1]):

x[i, j] *= y[i, j]

return x

# Example broadcasting axes to smaller tensor to match ndim of larger tensor,

# so smaller tensor is repeated along new axes to match full shape of larger tensor

@staticmethod

def naive_add_matrix_and_vector(x, y):

assert len(x.shape) == 2

assert len(y.shape) == 1

assert x.shape[1] == y.shape[0]

x = x.copy()

for i in range(x.shape[0]):

for j in range(x.shape[1]):

x[i, j] += y[j]

return x

# Only works with vectors having same number elements

@staticmethod

def naive_vector_dot(x, y):

assert len(x.shape) == 1

assert len(y.shape) == 1

assert x.shape[0] == y.shape[0]

z = 0

for i in range(x.shape[0]):

z += x[i] * y[i]

return z

# Only works with vectors having same number elements

@staticmethod

def naive_matrix_vector_dot(x, y):

assert len(x.shape) == 2 # Numpy matrix

assert len(y.shape) == 1 # Numpy vector

assert x.shape[1] == y.shape[0]

z = np.zeros(x.shape[0])

for i in range(x.shape[0]):

for j in range(x.shape[1]):

z[i] += x[i, j] * y[j]

return z

def naive_matrix_vector_dot_v2(self, x, y):

z = np.zeros(x.shape[0])

for i in range(x.shape[0]):

z[i] = self.naive_vector_dot(x[i, :], y)

return z

# Dot product between 2 matrices. Result is matrix with shale x.shape[0], y.shape[1],

# where coefficients are the vector products between rows of x and columns of y

def naive_matrix_dot(self, x, y):

assert len(x.shape) == 2 # Numpy matrix

assert len(y.shape) == 2 # Numpy matrix

assert x.shape[1] == y.shape[0]

z = np.zeros((x.shape[0], y.shape[1]))

for i in range(x.shape[0]):

for j in range(y.shape[1]):

row_x = x[i, :]

column_y = y[:, j]

z[i, j] = self.naive_vector_dot(row_x, column_y)

return z

tp = TensorOpsInPython()