innat

import numpy as np
import random, math
from scipy.stats import beta

def binarise_mask(mask, lam, in_shape, max_soft=0.0):
    """ Binarises a given low frequency image such that it has mean lambda.
    :param mask: Low frequency image, usually the result of `make_low_freq_image`
    :param lam: Mean value of final mask
    :param in_shape: Shape of inputs
    :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask.

innat

class SpatialAttentionModule(tf.keras.layers.Layer):
    def __init__(self, kernel_size=3):
        '''
        paper: https://arxiv.org/abs/1807.06521
        code: https://gist.github.com/innat/99888fa8065ecbf3ae2b297e5c10db70
        '''
        super(SpatialAttentionModule, self).__init__()
        self.conv1 = tf.keras.layers.Conv2D(64, kernel_size=kernel_size, 
                                            use_bias=False, 
                                            kernel_initializer='he_normal',

innat

# set plot figure size
fig, c_ax = plt.subplots(1,1, figsize = (12, 8))

def multiclass_roc_auc_score(y_test, y_pred, average="macro"):
    lb = LabelBinarizer()
    lb.fit(y_test)
    y_test = lb.transform(y_test)
    y_pred = lb.transform(y_pred)

    for (idx, c_label) in enumerate(all_labels): # all_labels: no of the labels, for ex. ['cat', 'dog', 'rat']

innat

1. Download
2. Postpressing

check

innat

Viewing the graph

match (n:MyNode)-[r]->(m)

return n, r, m

Finding paths between specific nodes:*

innat

Basic Graph Operations with CYPHER

Counting the number of nodes

match (n:MyNode)

return count(n)

Counting the number of edges

innat

//One way to "clean the slate" in Neo4j before importing (run both lines):

match (a)-[r]->() delete a,r

match (a) delete a

//Script to Import Data Set: test.csv (simple road network)

innat

Five Nodes

N1 = Tom

N2 = Harry

N3 = Julian

N4 = Michele

innat

Step by step tuts to setup apache spark ( pyspark ) on linux and setup environment for deep learning with Apache Spark using Deep-Learning-Pipelines.

Step 1 : Install Python 3 and Jupyter Notebook

Run following command. Someone may need to install pip first or any missing packages may need to download.

sudo apt install python3-pip sudo pip3 install jupyter

innat

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats