Beta
Course Notes
Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! The datasets used in this course are available in the datasets folder.
# Import constant from TensorFlow
from tensorflow import constant
# Convert the credit_numpy array into a tensorflow constant
credit_constant = constant(credit_numpy)
# Print constant datatype
print('\n The datatype is:', credit_constant.dtype)
# Print constant shape
print('\n The shape is:', credit_constant.shape)
# Define the 1-dimensional variable A1
A1 = Variable([1, 2, 3, 4])
# Print the variable A1
print('\n A1: ', A1)
# Convert A1 to a numpy array and assign it to B1
B1 = A1.numpy()
# Print B1
print('\n B1: ', B1)
# Define tensors A1 and A23 as constants
A1 = constant([1, 2, 3, 4])
A23 = constant([[1, 2, 3], [1, 6, 4]])
# Define B1 and B23 to have the correct shape
B1 = ones_like(A1)
B23 = ones_like(A23)
# Perform element-wise multiplication
C1 = A1*B1
C23 = A23*B23
# Print the tensors C1 and C23
print('\n C1: {}'.format(C1.numpy()))
print('\n C23: {}'.format(C23.numpy()))
# Define features, params, and bill as constants
features = constant([[2, 24], [2, 26], [2, 57], [1, 37]])
params = constant([[1000], [150]])
bill = constant([[3913], [2682], [8617], [64400]])
# Compute billpred using features and params
billpred = matmul(features,params)
# Compute and print the error
error = bill - billpred
print(error.numpy())
Take Notes
Add notes here about the concepts you've learned and code cells with code you want to keep.
Add your notes here
# Add your code snippets here