import NumPy as np # Converting NumPy array to byte format byte_output = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).tobytes() # Converting byte format back to NumPy array array_format = np.frombuffer(byte_output)
import tensorflow as tf import matplotlib.pyplot as plt ##sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True)) ## ##x = tf.constant([t for t in range(100)], shape=(1,100), dtype=tf.float32) ##y = tf.constant(4, dtype=tf.float32) ## ##out = tf.scalar_mul(y,x) ## ## ## ##sess.run(tf.global_variables_initializer()) # Creates a graph. ##with tf.device("/device:GPU:0"): ## a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') ## b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') ## c = tf.matmul(a, b) ### Creates a session with log_device_placement set to True. ##sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) ### Runs the op. ##print(sess.run(c)) a = np.array([1, 2, 3, 4, 5, 6, -34]) b = np.divide(a, 2) print(a, b) a = np.array([1, 2, 3, 4, 5, 6, -34]) b = np.divide(a, 3) print(a, b)
from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf import NumPy as np import logging logger = tf.get_logger() logger.setLevel(logging.ERROR) celsius_q = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float) farheneit_a = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float) for i, c in enumerate(celsius_q): print("{} degrees Celsius = {} degrees farenheit".format(c, farheneit_a[i])) slope = 1 intercept = 1 learning_rate = 0.1 ''' for i in range(epochs): curr_values = [] for c in celsius_q: curr_values.append(slope*celsisu+slope) for i in range(len(curr_values)): error = curr_values[i]-celsius_q[i] ''' # creating the underlying neural network to identify the relationships IO = tf.keras.layers.Dense(units=1,input_shape=1)
print(sum(range(5),-1)) from numpy import * print(sum(range(5),-1)) # Exercise 25 # Consider an integer vector Z, which of these expressions are legal? Z ** Z XXXXXXXXXXXXXXXXXXXX 2 << Z >> 2 XXXXXXXXXXXXXXXXXXXX Z <- Z XXXXXXXXXXXXXXXXXXXX 1j * Z XXXXXXXXXXXXXXXXXXXX Z / 1 / 1 XXXXXXXXXXXXXXXXXXXX Z <Z> Z # Question 26 # What are the results of the following expressions? np.array(0) // np.array(0) -- an integer np.array(0) // np.aray(0.) -- a floating point number np.array(0) / np.array(0) -- a floating point number np.array(0) / np.array(0.) -- a floating point number # Question 27 # How to round away from zero in a float array? Z = np.random.uniform(-10,+10,10) print(np.trunc(Z + np.copysign(0.5,Z))) # Question 28 # Extract the integer part of a random array using 5 different methods Z = np.random.uniform(0,10,10) print(Z - Z%1) print(np.floor(Z))