human_obj = super(Human, cls).__new__(cls)

For MacOS, you can do brew install git-lfs.

Let's also make a requirements.txt file which will allow us to install the packages that we need in whatever environment we need:

builtin = { 'settings' : ('settings', ), 'train' : ('train',),# 'training'), 'validate' : ('validate',),# 'validation'), 'test' : ('test', ), #'testing'), 'evaluate' : ('evaluate', ), #'evaluation'), 'templates' : ('templates', ), 'model' : ('model', ), 'loss' : ('loss', )

num_entries = len(batch[keys[0]]) for entry in range(num_entries): print('Entry {}/{}:'.format(entry+1, num_entries)) for k in keys: print(' {}: {}'.format(k, batch[k][entry]))

#### What is the shape of each key's values batch_shape = {} for k in keys: batch_shape[k] = batch[k].shape logger.info("A single batch consists of: %s", batch_shape) ### count inside the first element of batch dictionary, assign to num_entries num_entries = len(batch[keys[0]]) logger.info("There are total %s samples in a single batch.", num_entries) logger.info("Let's have a look a single sample") ### print out the whole batch, but one sample pair at a time, each sample pair like (key1:value, key2: value) # for entry in range(0, num_entries) ### Here only print out the first and last sample of a batch # for entry in range(0, num_entries, num_entries-1): for entry in range(1): ## print Entry entry_idx/total_entries print('Entry {}/{}:'.format(entry+1, num_entries)) ## for every batch_key for k in keys: # print: batch_key: this batch's first item print(' {}: {}'.format(k, batch[k][entry]))

Question: all models built successfully, but failed to train with train_network function. r2rt uses from tensorflow.models.rnn.ptb import reader which is not available anymore as I couldn't find it. I used reader.pyfrom https://github.com/tensorflow/models/tree/master/tutorials/rnn/ptb instead. I suspect the error lies on inappropriate use of functions in reader.py. Could anyone help me resolve it? Thanks! error message is here my guess on source of error Question: In order to upgrade CustomCell class to work, I need to upgrade the TF functions used by r2rt, e.g., tf.nn.rnn_cell._linear. However, I have difficulties to find the equivalent functions in TF docs. Could anyone help me update this class? Thanks codes that needs upgrade

return (x, y)

# tf.nn.rnn_cell._linear can't be found??? ru0 = tf.nn.rnn_cell._linear([inputs, state]

TypeError

Batch size set to: 2 [INFO 2017-02-28 22:29:05,910 kur.providers.batch_provider:60] Maximum number of batches set to: 1

Batch size set to: 32 [INFO 2017-02-28 22:29:02,413 kur.providers.batch_provider:60] Maximum number of batches set to: 50

# Where to get the data cifar: &cifar url: "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz" checksum: "6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce" path: "~/kur" # download and save dataset in kur directory, if so why not see it ######

images

by convolving with few 1x1 kernels, you are reducing the dimensions by collapsing many feature maps into few feature maps while maintaining the size of each feature map

Hour 1: True transcript: these vast buildings what were they DNN prediction: he s ma tol ln wt r hett jzxzjxzjqzjqjxzq

Copy either setup_p2.sh or setup_t2.sh to setup.sh (I did cp setup_t2.sh setup.sh on linux) Open setup.sh and change two lines -- change ami to the new ami that @vshets created, and change the VolumeSize to 30.

distribution of mean 0 and variance 1 (if any of the are floats, they are first converted to integers by truncation)

nthisbook,weexplore

Intuitively, an explanation is a local linear approximation of the model's behaviour

[1],[[i*.0001,1-(i*.0001)]]

[0,1 if i > 0 else 0],[[0.0001, 0.9999],[1-(i*.0001), i*.0001]]

Previous link won't take us to the highlighted text automatically because I shared the whole page link (top right) not the specific annotation link (bottom right).

annotation with jupyter notebook