it-ebooks - Mastering TensorFlow 1.x Code Notes
Here you can read online it-ebooks - Mastering TensorFlow 1.x Code Notes full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2018, publisher: iBooker it-ebooks, genre: Humor. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:
Romance novel
Science fiction
Adventure
Detective
Science
History
Home and family
Prose
Art
Politics
Computer
Non-fiction
Religion
Business
Children
Humor
Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.
Mastering TensorFlow 1.x Code Notes: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "Mastering TensorFlow 1.x Code Notes" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
it-ebooks: author's other books
Who wrote Mastering TensorFlow 1.x Code Notes? Find out the surname, the name of the author of the book and a list of all author's works by series.
Mastering TensorFlow 1.x Code Notes — read online for free the complete book (whole text) full work
Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Mastering TensorFlow 1.x Code Notes" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.
Font size:
Interval:
Bookmark:
import tensorflow as tf import numpy as nptfs = tf.InteractiveSession()hello = tf.constant( "Hello TensorFlow !!" )print(tfs.run(hello))b'Hello TensorFlow !!'c1 = tf.constant(, name= 'x' )c2 = tf.constant( 6.0 , name= 'y' )c3 = tf.constant( 7.0 , tf.float32, name= 'z' )print( 'c1 (x): ' , c1)print( 'c2 (y): ' , c2)print( 'c3 (z): ' , c3)c1 (x): Tensor("x:0", shape=(), dtype=int32)c2 (y): Tensor("y:0", shape=(), dtype=float32)c3 (z): Tensor("z:0", shape=(), dtype=float32)print( 'run([c1,c2,c3]) : ' , tfs.run([c1, c2, c3]))run([c1,c2,c3]) : [5, 6.0, 7.0]op1 = tf.add(c2, c3)op2 = tf.multiply(c2, c3)print( 'op1 : ' , op1)print( 'op2 : ' , op2)op1 : Tensor("Add:0", shape=(), dtype=float32)op2 : Tensor("Mul:0", shape=(), dtype=float32)print( 'run(op1) : ' , tfs.run(op1))print( 'run(op2) : ' , tfs.run(op2))run(op1) : 13.0run(op2) : 42.0p1 = tf.placeholder(tf.float32)p2 = tf.placeholder(tf.float32)print( 'p1 : ' , p1)print( 'p2 : ' , p2)p1 : Tensor("Placeholder:0", dtype=float32)p2 : Tensor("Placeholder_1:0", dtype=float32)op4 = p1 * p2 # shorthand for tf.multiply(p1, p2)print( 'run(op4,{p1:2.0, p2:3.0}) : ' ,tfs.run(op4,{p1: 2.0 , p2: 3.0 }))run(op4,{p1:2.0, p2:3.0}) : 6.0print( 'run(op4,feed_dict = {p1:3.0, p2:4.0}) : ' , tfs.run(op4, feed_dict={p1: 3.0 , p2: 4.0 }))run(op4,feed_dict = {p1:3.0, p2:4.0}) : 12.0print( 'run(op4,feed_dict={p1:[2.0,3.0,4.0], p2:[3.0,4.0,5.0]}):' , tfs.run(op4, feed_dict={p1: [ 2.0 , 3.0 , 4.0 ], p2: [ 3.0 , 4.0 , 5.0 ]}))run(op4,feed_dict = {p1:[2.0,3.0,4.0], p2:[3.0,4.0,5.0]}) : [ 6. 12. 20.]tf_t = tf.convert_to_tensor( 5.0 , dtype=tf.float64)print( 'tf_t : ' , tf_t)print( 'run(tf_t) : \n' , tfs.run(tf_t))tf_t : Tensor("Const_1:0", shape=(), dtype=float64)run(tf_t) : 5.0a1dim = np.array([, , , , 5.99 ])print( "a1dim Shape : " , a1dim.shape)tf_t = tf.convert_to_tensor(a1dim, dtype=tf.float64)print( 'tf_t : ' , tf_t)print( 'tf_t[0] : ' , tf_t[])print( 'tf_t[0] : ' , tf_t[])print( 'run(tf_t) : \n' , tfs.run(tf_t))a1dim Shape : (5,)tf_t : Tensor("Const_2:0", shape=(5,), dtype=float64)tf_t[0] : Tensor("strided_slice:0", shape=(), dtype=float64)tf_t[0] : Tensor("strided_slice_1:0", shape=(), dtype=float64)run(tf_t) : [ 1. 2. 3. 4. 5.99]a2dim = np.array([(, , , , 5.99 ), (, , , , 6.99 ), (, , , , 7.99 ) ])print( "a2dim Shape : " , a2dim.shape)tf_t = tf.convert_to_tensor(a2dim, dtype=tf.float64)print( 'tf_t : ' , tf_t)print( 'tf_t[0][0] : ' , tf_t[][])print( 'tf_t[1][2] : ' , tf_t[][])print( 'run(tf_t) : \n' , tfs.run(tf_t))a2dim Shape : (3, 5)tf_t : Tensor("Const_3:0", shape=(3, 5), dtype=float64)tf_t[0][0] : Tensor("strided_slice_3:0", shape=(), dtype=float64)tf_t[1][2] : Tensor("strided_slice_5:0", shape=(), dtype=float64)run(tf_t) : [[ 1. 2. 3. 4. 5.99] [ 2. 3. 4. 5. 6.99] [ 3. 4. 5. 6. 7.99]]a3dim = np.array([[[, ], [, ] ], [[, ], [, ] ] ])print( "a3dim Shape : " , a3dim.shape)tf_t = tf.convert_to_tensor(a3dim, dtype=tf.float64)print( 'tf_t : ' , tf_t)print( 'tf_t[0][0][0] : ' , tf_t[][][])print( 'tf_t[1][1][1] : ' , tf_t[][][])print( 'run(tf_t) : \n' , tfs.run(tf_t))a3dim Shape : (2, 2, 2)tf_t : Tensor("Const_4:0", shape=(2, 2, 2), dtype=float64)tf_t[0][0][0] : Tensor("strided_slice_8:0", shape=(), dtype=float64)tf_t[1][1][1] : Tensor("strided_slice_11:0", shape=(), dtype=float64)run(tf_t) : [[[ 1. 2.] [ 3. 4.]] [[ 5. 6.] [ 7. 8.]]]# Assume Linear Model y = w * x + b # Define model parameters w = tf.Variable([ .3 ], tf.float32)b = tf.Variable([ -.3 ], tf.float32) # Define model input and output x = tf.placeholder(tf.float32)y = w * x + bprint( "w:" , w)print( "x:" , x)print( "b:" , b)print( "y:" , y)w: x: Tensor("Placeholder_2:0", dtype=float32)b: y: Tensor("add:0", dtype=float32)# initialize and print the variable y tf.global_variables_initializer().run()print( 'run(y,{x:[1,2,3,4]}) : ' , tfs.run(y, {x: [, , , ]}))run(y,{x:[1,2,3,4]}) : [ 0. 0.30000001 0.60000002 0.90000004]a = tf.zeros((,))print(tfs.run(a))[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]tfs.close()# Assume Linear Model y = w * x + b # Define model parameters w = tf.Variable([ .3 ], tf.float32)b = tf.Variable([ -.3 ], tf.float32) # Define model input and output x = tf.placeholder(tf.float32)y = w * x + boutput = with tf.Session() as tfs: # initialize and print the variable y tf.global_variables_initializer().run() output = tfs.run(y, {x: [, , , ]})print( 'output : ' , output)output : [ 0. 0.30000001 0.60000002 0.90000004]from tensorflow.python.client import device_libprint(device_lib.list_local_devices())[name: "/cpu:0"device_type: "CPU"memory_limit: 268435456locality {}incarnation: 12445270569278384213, name: "/gpu:0"device_type: "GPU"memory_limit: 25628672locality { bus_id: 1}incarnation: 5284662930836416221physical_device_desc: "device: 0, name: GeForce GT 750M, pci bus id: 0000:01:00.0"]tf.reset_default_graph() # Define model parameters w = tf.get_variable(name= 'w' , initializer=[ .3 ], dtype=tf.float32)b = tf.get_variable(name= 'b' , initializer=[ -.3 ], dtype=tf.float32) # Define model input and output x = tf.placeholder(name= 'x' , dtype=tf.float32)y = w * x + bconfig = tf.ConfigProto()config.log_device_placement = True with tf.Session(config=config) as tfs: # initialize and print the variable y tfs.run(tf.global_variables_initializer()) print( 'output' , tfs.run(y, {x: [, , , ]}))output from GPU: [ 0. 0.30000001 0.60000002 0.90000004]tf.reset_default_graph() with tf.device( '/device:CPU:0' ): # Define model parameters w = tf.get_variable(name= 'w' , initializer=[ .3 ], dtype=tf.float32) b = tf.get_variable(name= 'b' , initializer=[ -.3 ], dtype=tf.float32) # Define model input and output x = tf.placeholder(name= 'x' , dtype=tf.float32) y = w * x + bconfig = tf.ConfigProto()config.log_device_placement = True with tf.Session(config=config) as tfs: # initialize and print the variable y tfs.run(tf.global_variables_initializer()) print( 'output' , tfs.run(y, {x: [, , , ]}))tf.reset_default_graph() with tf.device( '/device:CPU:0' ): # Define model parameters w = tf.get_variable(name= 'w' , initializer=[ .3 ], dtype=tf.float32) b = tf.get_variable(name= 'b' , initializer=[ -.3 ], dtype=tf.float32) # Define model input and output x = tf.placeholder(name= 'x' , dtype=tf.float32) with tf.device( '/device:GPU:0' ): y = w * x + bconfig = tf.ConfigProto()config.log_device_placement = True with tf.Session(config=config) as tfs: # initialize and print the variable y tfs.run(tf.global_variables_initializer()) print( 'output' , tfs.run(y, {x: [, , , ]}))Font size:
Interval:
Bookmark:
Similar books «Mastering TensorFlow 1.x Code Notes»
Look at similar books to Mastering TensorFlow 1.x Code Notes. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.
Discussion, reviews of the book Mastering TensorFlow 1.x Code Notes and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.