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TensorFlow Linear Regression Model Access with Custom REST API using Flask
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():
# CUSTOMIZABLE: Collect/Prepare data
steps = 100
learn_rate = 0.0001
if request.method == 'POST':
steps = request.json['steps']
learn_rate = request.json['learnrate']
# Model linear regression y = Wx + b
x = tf.placeholder(tf.float32, [None, 1])
W = tf.Variable(tf.zeros([1,1]))
b = tf.Variable(tf.zeros([1]))
product = tf.matmul(x,W)
y = product + b
y_ = tf.placeholder(tf.float32, [None, 1])
# Cost function sum((y_-y)**2)
cost = tf.reduce_mean(tf.square(y_-y))
# Training using Gradient Descent to minimize cost
train_step = tf.train.GradientDescentOptimizer(learn_rate).minimize(cost)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
all_points = [];
eq_vals = [];
for i in range(steps):
# Create fake data for y = W.x + b where W = 2, b = 0
xs = np.array([[i]])
ys = np.array([[2*i]])
# Train
feed = { x: xs, y_: ys }
sess.run(train_step, feed_dict=feed)
all_points.append({"x": xs[0][0], "y": ys[0][0]});
# NOTE: W should be close to 2, and b should be close to 0
eq_vals.append({'w': str(sess.run(W)[0][0]), "b": str(sess.run(b)[0]),
"cost": '{0:f}'.format(sess.run(cost, feed_dict=feed))});
response = jsonify(results=[eq_vals, all_points])
return response
if __name__ == "__main__":
app.run(host='0.0.0.0')
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