LitArk » Books » Romance novel

it-ebooks - Keras Tutorials (tgjeon)

Here you can read online it-ebooks - Keras Tutorials (tgjeon) full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2019, publisher: iBooker it-ebooks, genre: Romance novel. 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.

Book:
Keras Tutorials (tgjeon)
Author:
itebooks
Publisher:
iBooker it-ebooks
Genre:
Books / Romance novel
Year:
2019
Rating:
3 / 5
Favourites:
Add to favourites
Your mark:
- 60
- 1
- 2
- 3
- 4
- 5

Description
Author's other books
Similar books

Keras Tutorials (tgjeon): summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Keras Tutorials (tgjeon)" 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 Keras Tutorials (tgjeon)? Find out the surname, the name of the author of the book and a list of all author's works by series.

Keras Tutorials (tgjeon) — 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 "Keras Tutorials (tgjeon)" 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.

Light

Font size:

↓

↑

Reset

Interval:

↓

↑

Bookmark:

Make

Linear Regression

from keras.models import Sequential from keras.layers import Dense import numpy as np import matplotlib.pyplot as plt%matplotlib inline

Using TensorFlow backend.

# Generate dataset trX = np.linspace( -1 , , )trY = * trX + np.random.randn(*trX.shape) * 0.33 # create a y value which is approximately linear but with some random noise

# Linear regression model model = Sequential()model.add(Dense(input_dim=, output_dim=, init= 'uniform' , activation= 'linear' ))model.compile(optimizer= 'sgd' , loss= 'mse' )

# Print initial weights weights = model.layers[].get_weights()w_init = weights[][][]b_init = weights[][]print( 'Linear regression model is initialized with weight w: %.2f, b: %.2f' % (w_init, b_init))

Linear regression model is initialized with weight w: -0.02, b: 0.00# Train model.fit(trX, trY, nb_epoch=, verbose=)

Epoch 1/100101/101 [==============================] - 0s - loss: 1.6034 Epoch 2/100101/101 [==============================] - 0s - loss: 1.5246 Epoch 3/100101/101 [==============================] - 0s - loss: 1.4446 Epoch 4/100101/101 [==============================] - 0s - loss: 1.3690 Epoch 5/100101/101 [==============================] - 0s - loss: 1.2975 Epoch 6/100101/101 [==============================] - 0s - loss: 1.2362 Epoch 7/100101/101 [==============================] - 0s - loss: 1.1691 Epoch 8/100101/101 [==============================] - 0s - loss: 1.1179 Epoch 9/100101/101 [==============================] - 0s - loss: 1.0717 Epoch 10/100101/101 [==============================] - 0s - loss: 1.0224 Epoch 11/100101/101 [==============================] - 0s - loss: 0.9743 Epoch 12/100101/101 [==============================] - 0s - loss: 0.9290 Epoch 13/100101/101 [==============================] - 0s - loss: 0.8886 Epoch 14/100101/101 [==============================] - 0s - loss: 0.8452 Epoch 15/100101/101 [==============================] - 0s - loss: 0.8061 Epoch 16/100101/101 [==============================] - 0s - loss: 0.7721 Epoch 17/100101/101 [==============================] - 0s - loss: 0.7335 Epoch 18/100101/101 [==============================] - 0s - loss: 0.6992 Epoch 19/100101/101 [==============================] - 0s - loss: 0.6663 Epoch 20/100101/101 [==============================] - 0s - loss: 0.6388 Epoch 21/100101/101 [==============================] - 0s - loss: 0.6106 Epoch 22/100101/101 [==============================] - 0s - loss: 0.5842 Epoch 23/100101/101 [==============================] - 0s - loss: 0.5584 Epoch 24/100101/101 [==============================] - 0s - loss: 0.5357 Epoch 25/100101/101 [==============================] - 0s - loss: 0.5126 Epoch 26/100101/101 [==============================] - 0s - loss: 0.4931 Epoch 27/100101/101 [==============================] - 0s - loss: 0.4715 Epoch 28/100101/101 [==============================] - 0s - loss: 0.4544 Epoch 29/100101/101 [==============================] - 0s - loss: 0.4364 Epoch 30/100101/101 [==============================] - 0s - loss: 0.4194 Epoch 31/100101/101 [==============================] - 0s - loss: 0.4032 Epoch 32/100101/101 [==============================] - 0s - loss: 0.3883 Epoch 33/100101/101 [==============================] - 0s - loss: 0.3703 Epoch 34/100101/101 [==============================] - 0s - loss: 0.3565 Epoch 35/100101/101 [==============================] - 0s - loss: 0.3433 Epoch 36/100101/101 [==============================] - 0s - loss: 0.3312 Epoch 37/100101/101 [==============================] - 0s - loss: 0.3179 Epoch 38/100101/101 [==============================] - 0s - loss: 0.3067 Epoch 39/100101/101 [==============================] - 0s - loss: 0.2956 Epoch 40/100101/101 [==============================] - 0s - loss: 0.2856 Epoch 41/100101/101 [==============================] - 0s - loss: 0.2767 Epoch 42/100101/101 [==============================] - 0s - loss: 0.2660 Epoch 43/100101/101 [==============================] - 0s - loss: 0.2579 Epoch 44/100101/101 [==============================] - 0s - loss: 0.2498 Epoch 45/100101/101 [==============================] - 0s - loss: 0.2428 Epoch 46/100101/101 [==============================] - 0s - loss: 0.2355 Epoch 47/100101/101 [==============================] - 0s - loss: 0.2281 Epoch 48/100101/101 [==============================] - 0s - loss: 0.2222 Epoch 49/100101/101 [==============================] - 0s - loss: 0.2158 Epoch 50/100101/101 [==============================] - 0s - loss: 0.2101 Epoch 51/100101/101 [==============================] - 0s - loss: 0.2054 Epoch 52/100101/101 [==============================] - 0s - loss: 0.2013 Epoch 53/100101/101 [==============================] - 0s - loss: 0.1963 Epoch 54/100101/101 [==============================] - 0s - loss: 0.1918 Epoch 55/100101/101 [==============================] - 0s - loss: 0.1870 Epoch 56/100101/101 [==============================] - 0s - loss: 0.1836 Epoch 57/100101/101 [==============================] - 0s - loss: 0.1800 Epoch 58/100101/101 [==============================] - 0s - loss: 0.1760 Epoch 59/100101/101 [==============================] - 0s - loss: 0.1721 Epoch 60/100101/101 [==============================] - 0s - loss: 0.1691 Epoch 61/100101/101 [==============================] - 0s - loss: 0.1664 Epoch 62/100101/101 [==============================] - 0s - loss: 0.1631 Epoch 63/100101/101 [==============================] - 0s - loss: 0.1602 Epoch 64/100101/101 [==============================] - 0s - loss: 0.1570 Epoch 65/100101/101 [==============================] - 0s - loss: 0.1542 Epoch 66/100101/101 [==============================] - 0s - loss: 0.1519 Epoch 67/100101/101 [==============================] - 0s - loss: 0.1496 Epoch 68/100101/101 [==============================] - 0s - loss: 0.1470 Epoch 69/100101/101 [==============================] - 0s - loss: 0.1446 Epoch 70/100101/101 [==============================] - 0s - loss: 0.1430 Epoch 71/100101/101 [==============================] - 0s - loss: 0.1414 Epoch 72/100101/101 [==============================] - 0s - loss: 0.1397 Epoch 73/100101/101 [==============================] - 0s - loss: 0.1381 Epoch 74/100101/101 [==============================] - 0s - loss: 0.1364 Epoch 75/100101/101 [==============================] - 0s - loss: 0.1352 Epoch 76/100101/101 [==============================] - 0s - loss: 0.1338 Epoch 77/100101/101 [==============================] - 0s - loss: 0.1321 Epoch 78/100101/101 [==============================] - 0s - loss: 0.1312 Epoch 79/100101/101 [==============================] - 0s - loss: 0.1299 Epoch 80/100101/101 [==============================] - 0s - loss: 0.1286 Epoch 81/100101/101 [==============================] - 0s - loss: 0.1270 Epoch 82/100101/101 [==============================] - 0s - loss: 0.1260 Epoch 83/100101/101 [==============================] - 0s - loss: 0.1252 Epoch 84/100101/101 [==============================] - 0s - loss: 0.1247 Epoch 85/100101/101 [==============================] - 0s - loss: 0.1239 Epoch 86/100101/101 [==============================] - 0s - loss: 0.1228 Epoch 87/100101/101 [==============================] - 0s - loss: 0.1222 Epoch 88/100101/101 [==============================] - 0s - loss: 0.1214 Epoch 89/100101/101 [==============================] - 0s - loss: 0.1210 Epoch 90/100101/101 [==============================] - 0s - loss: 0.1201 Epoch 91/100101/101 [==============================] - 0s - loss: 0.1194 Epoch 92/100101/101 [==============================] - 0s - loss: 0.1189 Epoch 93/100101/101 [==============================] - 0s - loss: 0.1184 Epoch 94/100101/101 [==============================] - 0s - loss: 0.1176 Epoch 95/100101/101 [==============================] - 0s - loss: 0.1168 Epoch 96/100101/101 [==============================] - 0s - loss: 0.1162 Epoch 97/100101/101 [==============================] - 0s - loss: 0.1157 Epoch 98/100101/101 [==============================] - 0s - loss: 0.1150 Epoch 99/100101/101 [==============================] - 0s - loss: 0.1144 Epoch 100/100101/101 [==============================] - 0s - loss: 0.1140

Light

Font size:

↓

↑

Reset

Interval:

↓

↑

Bookmark:

Make

Similar books «Keras Tutorials (tgjeon)»

Look at similar books to Keras Tutorials (tgjeon). 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.

Anshik

AI for Healthcare with Keras and Tensorflow 2.0: Design, Develop, and Deploy Machine Learning Models Using Healthcare Data

it-ebooks

Programming Practice Tutorials (KAIST CS109)

it-ebooks

Tensorflow Tutorials (pkmital)

it-ebooks

TensorFlow Tutorials (nlintz)

it-ebooks

it-ebooks

it-ebooks

it-ebooks

it-ebooks

computer_networks_a_systems_approach_5th_ed

it-ebooks

Deep Learning with Keras and Tensorflow

it-ebooks

Ruby中的元编程

John Suresh

Learn Keras for Deep Neural Networks

Reviews about «Keras Tutorials (tgjeon)»

Discussion, reviews of the book Keras Tutorials (tgjeon) 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.