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December 15, 2017 20:01
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TensorFlow Linear Regression Model Access with Custom REST API using Flask Raw
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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): | |
if i % 10 != 0: | |
# Create fake data for x and y | |
xs = np.array([[i]]) | |
ys = np.array([[2*i - 10]]) | |
if i % 2 == 0: | |
ys = np.array([[2*i + 10]]) | |
# 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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