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Linear Regression Machine Learning with TensorFlow and Oracle JET UI Explained
import numpy as np
import tensorflow as tf
from flask import Flask, jsonify, request
from flask_cors import CORS, cross_origin
app = Flask(__name__)
CORS(app)
@app.route("/redsam/api/v0.1/points", methods=['GET', 'POST'])
def linear_regression_train():
learning_rate = 0.01
training_epochs = 100
# reading training parameters from REST request
if request.method == 'POST':
learning_rate = request.json['learningrate']
training_epochs = request.json['trainingepochs']
# training data
x_train = np.linspace(-1, 1, 101)
y_train = 2 * x_train + np.random.randn(*x_train.shape) * 0.33
# placeholders, initialized from training data
X = tf.placeholder("float")
Y = tf.placeholder("float")
# model function, defined as y = W * x
def model(X, w):
return tf.multiply(X, w)
# weights variable for equation
w = tf.Variable(0.0, name="weights")
# cost function for linear regression, defined as the mean squared error
y_model = model(X, w)
cost = tf.reduce_mean(tf.square(Y-y_model))
# This operation will be called on each iteration of the learning algorithm
# GradientDescentOptimizer - TensorFlow implementation
# Learn with given rate and try to minimize learning cost
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# TensorFlow initialization
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
# repeating training multiple times
for epoch in range(training_epochs):
# iterating through training data set and calling optimizer, for each training entry
for (x, y) in zip(x_train, y_train):
sess.run(train_op, feed_dict={X: x, Y: y})
# calculated weights variable
w_val = sess.run(w)
# calculated cost variable
cost_val = sess.run(cost, feed_dict={X: x_train, Y: y_train})
all_points = [];
eq_vals = [];
# populating array of x/y training data for REST response
for (x, y) in zip(x_train, y_train):
all_points.append({"x": x, "y": y})
# populating weights and cost variables for REST response
eq_vals.append({"w": str(w_val), "cost": str(cost_val)})
# constructing JSON response
response = jsonify(results=[eq_vals, all_points])
sess.close()
return response
# running REST interface
if __name__ == "__main__":
app.run(host='0.0.0.0')
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