<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script>

// Define a model for linear regression.
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));

model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

// Generate some synthetic data for training.
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

// Train the model using the data.
model.fit(xs, ys, {epochs: 10}).then(() => {
  // Use the model to do inference on a data point the model hasn't seen before:
  model.predict(tf.tensor2d([5], [1, 1])).print();
  // Open the browser devtools to see the output
});

yarn add @tensorflow/tfjs

npm install @tensorflow/tfjs

import * as tf from '@tensorflow/tfjs';

// Define a model for linear regression.
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));

model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

// Generate some synthetic data for training.
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

// Train the model using the data.
model.fit(xs, ys, {epochs: 10}).then(() => {
  // Use the model to do inference on a data point the model hasn't seen before:
  model.predict(tf.tensor2d([5], [1, 1])).print();
  // Open the browser devtools to see the output
});

yarn add @tensorflow/tfjs-node

npm install @tensorflow/tfjs-node

yarn add @tensorflow/tfjs-node-gpu

npm install @tensorflow/tfjs-node-gpu

yarn add @tensorflow/tfjs

npm install @tensorflow/tfjs

const tf = require('@tensorflow/tfjs');

// Optional Load the binding:
// Use '@tensorflow/tfjs-node-gpu' if running with GPU.
require('@tensorflow/tfjs-node');

// Train a simple model:
const model = tf.sequential();
model.add(tf.layers.dense({units: 100, activation: 'relu', inputShape: [10]}));
model.add(tf.layers.dense({units: 1, activation: 'linear'}));
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});

const xs = tf.randomNormal([100, 10]);
const ys = tf.randomNormal([100, 1]);

model.fit(xs, ys, {
  epochs: 100,
  callbacks: {
    onEpochEnd: (epoch, log) => console.log(`Epoch ${epoch}: loss = ${log.loss}`)
  }
});

Source : Tensorflow

tf.constant(value, dtype=None, shape=None, name='Const', verify_shape=False)

#include <manager.h>  

data = tf.nn.batch_norm_with_global_normalization()

./deepspeech.py  

tf.Variable(initial_value=None, trainable=None, validate_shape=True, 
caching_device=None, name=None, variable_def=None, dtype=None, import_scope=None,
constraint=None,synchronization=tf.VariableSynchronization.AUTO, 
aggregation=tf.compat.v1.VariableAggregation.NONE, shape=None)​

tensor = tf.Variable([3,4])

<tf.Variable ‘Variable:0’ shape=(2,) dtype=int32, numpy=array([3, 4], dtype=int32)>

# import packages
import tensorflow as tf
 
# create variable
tensor1 = tf.Variable([3, 4])
 
# The shape of the variable
print("The shape of the variable: ",
      tensor1.shape)
 
# The number of dimensions in the variable
print("The number of dimensions in the variable:",
      tf.rank(tensor1).numpy())
 
# The size of the variable
print("The size of the tensorflow variable:",
      tf.size(tensor1).numpy())
 
# checking the datatype of the variable
print("The datatype of the tensorflow variable is:",
      tensor1.dtype)

The shape of the variable:  (2,)
The number of dimensions in the variable: 1
The size of the tensorflow variable: 2
The datatype of the tensorflow variable is: <dtype: 'int32'>

import tensorflow as tf
 
tensor1 = tf.Variable([3, 4])
tensor1[1].assign(5)
tensor1

<tf.Variable ‘Variable:0’ shape=(2,) dtype=int32, numpy=array([3, 5], dtype=int32)>

Example :

Syntax : assign_add(delta, use_locking=False, name=None, read_value=True)

parameters :

* delta : The value to be added to the variable(Tensor).
* use_locking : During the operation, if True, utilise locking.
* name :  name of the operation.
* read_value : If True, anything that evaluates to the modified value of the variable will be returned; if False, the assign op will be returned.

# import packages
import tensorflow as tf

# create variable
tensor1 = tf.Variable([3, 4])

# using assign_add() function
tensor1.assign_add([1, 1])

tensor1

Output :

<tf.Variable ‘Variable:0’ shape=(2,) dtype=int32, numpy=array([4, 5], dtype=int32)>

Example :

Syntax : assign_sub(  delta, use_locking=False, name=None, read_value=True)

parameters :

* delta : The value to be subtracted from the variable
* use_locking : During the operation, if True, utilise locking.
* name : name of the operation.
* read_value : If True, anything that evaluates to the modified value of the variable will be returned; if False, the assign op will be returned.

# import packages
import tensorflow as tf

# create variable
tensor1 = tf.Variable([3, 4])

# using assign_sub() function
tensor1.assign_sub([1, 1])

tensor1

Output :

<tf.Variable ‘Variable:0’ shape=(2,) dtype=int32, numpy=array([2, 3], dtype=int32)>

python -c 'import tensor flow as tf; print(tf.__version__)'

python3 -c 'import tensor flow as tf; print(tf.__version__)'

tf.Session()  

tf.get_default_session().run(values)  ​

weighted_r2 = WeightedR2()
regression = regression(net, metric=weighted_r2)

tf.backend()

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis"></script>

<!DOCTYPE html>
<html>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis"></script>
<body>

<h2>TensorFlow Visor</h2>

<script>

const series = ['First', 'Second'];

const serie1 = []; 
const serie2 = [];
for (let i = 0; i < 100; i++) {
  serie1[i] = {x:i, y:Math.random() * 100};
  serie2[i] = {x:i, y:Math.random() * 100};
}

const data = {values: [serie1, serie2], series}

tfvis.render.scatterplot({name: "my Plots"}, data);

</script>
</body>
</html>

Output : 

Import tensorflow as tf
# Creating variable for parameter slope (W) with initial value as 0.4
W = tf.Variable([.4], tf.float32)
#Creating variable for parameter bias (b) with initial value as -0.4
b = tf.Variable([-0.4], tf.float32)
# Creating placeholders for providing input or independent variable, denoted by x
x = tf.placeholder(tf.float32)
# Equation of Linear Regression
linear_model = W * x + b
# Initializing all the variables
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
# Running regression model to calculate the output w.r.t. to provided x values
print(sess.run(linear_model {x: [1, 2, 3, 4]})) 

import numpy as np
import tensorflow as tf
# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# In this example, we limit mnist data
Xtrain, Ytrain = mnist.train.next_batch(5000) #5000 for training (nn candidates)
Xtest, Ytest = mnist.test.next_batch(200) #200 for testing
# tf Graph Input
xtrain = tf.placeholder("float", [None, 784])
xtest = tf.placeholder("float", [784])
# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtrain, tf.negative(xtest))), reduction_indices=1)
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.argmin(distance, 0)
accuracy = 0.
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Start training
with tf.Session() as sess:
   sess.run(init)
   # loop over test data
   for i in range(len(Xtest)):
       # Get nearest neighbor
       nn_index = sess.run(pred, feed_dict={xtrain: Xtrain, xtest: Xtest[i, :]})
     # Get nearest neighbor class label and compare it to its true label
       print "Test", i, "Prediction:", np.argmax(Ytrain[nn_index]), \
           "True Class:", np.argmax(Ytest[i])
       # Calculate accuracy
       if np.argmax(Ytrain[nn_index]) == np.argmax(Ytest[i]):
           accuracy += 1./len(Xtest)
   print "Accuracy:", accuracy