Mohammed Innat innat

Viewing the graph

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

return n, r, m

Finding paths between specific nodes:*

Basic Graph Operations with CYPHER

Counting the number of nodes

match (n:MyNode)

return count(n)

Counting the number of edges

//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)

Five Nodes

N1 = Tom

N2 = Harry

N3 = Julian

N4 = Michele

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

Let's consider a folowing 4D matrix.

import numpy as np
W = np.random.randn(2,2,3,100)

print(W.shape)

output:

	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',

	# 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']

	'''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