dipanjanS

Get the processor core affinity for a process ( cores on which it is allowed to run )

taskset -cp <PID>

Example,

[root@user]# taskset -cp 74515
pid 74515's current affinity list: 0-7

dipanjanS

# Correlation Matrix Heatmap
f, ax = plt.subplots(figsize=(10, 6))
corr = wines.corr()
hm = sns.heatmap(round(corr,2), annot=True, ax=ax, cmap="coolwarm",fmt='.2f',
                 linewidths=.05)
f.subplots_adjust(top=0.93)
t= f.suptitle('Wine Attributes Correlation Heatmap', fontsize=14)

dipanjanS

train_dir = 'training_data'
val_dir = 'validation_data'
test_dir = 'test_data'

train_files = np.concatenate([cat_train, dog_train])
validate_files = np.concatenate([cat_val, dog_val])
test_files = np.concatenate([cat_test, dog_test])

os.mkdir(train_dir) if not os.path.isdir(train_dir) else None
os.mkdir(val_dir) if not os.path.isdir(val_dir) else None

dipanjanS

from nltk.parse.stanford import StanfordDependencyParser
sdp = StanfordDependencyParser(path_to_jar='E:/stanford/stanford-parser-full-2015-04-20/stanford-parser.jar',
                               path_to_models_jar='E:/stanford/stanford-parser-full-2015-04-20/stanford-parser-3.5.2-models.jar')    

result = list(sdp.raw_parse(sentence))  

# print the dependency tree
dep_tree = [parse.tree() for parse in result][0]
print(dep_tree)

dipanjanS

tf.logging.set_verbosity(tf.logging.ERROR)

results = {}

results["nnlm-en-dim128"] = train_and_evaluate_with_sentence_encoder(
    "https://tfhub.dev/google/nnlm-en-dim128/1", path='/storage/models/nnlm-en-dim128_f/')

results["nnlm-en-dim128-with-training"] = train_and_evaluate_with_sentence_encoder(
    "https://tfhub.dev/google/nnlm-en-dim128/1", train_module=True, path='/storage/models/nnlm-en-dim128_t/')

dipanjanS

from keras.preprocessing import text
from keras.utils import np_utils
from keras.preprocessing import sequence

tokenizer = text.Tokenizer()
tokenizer.fit_on_texts(norm_bible)
word2id = tokenizer.word_index

# build vocabulary of unique words
word2id['PAD'] = 0

dipanjanS

from keras.preprocessing import text

tokenizer = text.Tokenizer()
tokenizer.fit_on_texts(norm_bible)

word2id = tokenizer.word_index
id2word = {v:k for k, v in word2id.items()}

vocab_size = len(word2id) + 1 
embed_size = 100

dipanjanS

from keras.layers import Merge
from keras.layers.core import Dense, Reshape
from keras.layers.embeddings import Embedding
from keras.models import Sequential

# build skip-gram architecture
word_model = Sequential()
word_model.add(Embedding(vocab_size, embed_size,
                         embeddings_initializer="glorot_uniform",
                         input_length=1))

dipanjanS

%%time

sample_test_data = test_images
sample_test_labels = test_labels
IMG_DIMS = (32, 32)
sample_test_data_processed = (np.array([resize_image_array(img, 
                                                           img_size_dims=IMG_DIMS) 
                                            for img in np.stack([sample_test_data]*3, 
                                                                axis=-1)])) / 255.
data = json.dumps({"signature_name": "serving_default", 

dipanjanS

# set java path
import os
java_path = r'C:\Program Files\Java\jdk1.8.0_102\bin\java.exe'
os.environ['JAVAHOME'] = java_path

from nltk.parse.stanford import StanfordParser

scp = StanfordParser(path_to_jar='E:/stanford/stanford-parser-full-2015-04-20/stanford-parser.jar',
                     path_to_models_jar='E:/stanford/stanford-parser-full-2015-04-20/stanford-parser-3.5.2-models.jar')