m4-LinearRegressionWithCustomModel.py

import numpy as np
from sklearn import datasets, linear_model

from returns_data import read_goog_sp500_data
xData, yData = read_goog_sp500_data()

googModel = linear_model.LinearRegression()

googModel.fit(xData.reshape(-1, 1), yData.reshape(-1, 1))

print(googModel.coef_)
print(googModel.intercept_)


### Custom Linear Regression Model

import tensorflow as tf

# Declare list of features, we only have one real-valued feature
def model(features, labels, mode):

    # Build a linear model and predict values
    W = tf.get_variable('W', [1], dtype=tf.float64)
    b = tf.get_variable('b', [1], dtype=tf.float64)

    y = W * features['x'] + b

    # Loss sub-graph
    loss = tf.reduce_sum(tf.square(y - labels))

    # Training sub-graph
    global_step = tf.train.get_global_step()

    optimizer = tf.train.FtrlOptimizer(1)

    # groups steps so they are completed together
    train = tf.group(optimizer.minimize(loss), tf.assign_add(global_step, 1))

    # ModelFnOps connects subgraphs we built to the appropriate functionality
    return tf.contrib.learn.ModelFnOps(mode=mode, predictions=y, loss=loss, train_op=train)

estimator = tf.contrib.learn.Estimator(model_fn=model)

input_fn = tf.contrib.learn.io.numpy_input_fn({"x": xData}, yData, batch_size=len(xData), num_epochs=10000)

fit = estimator.fit(input_fn=input_fn, steps=10000)

for variable_name in fit.get_variable_names():
    print(variable_name, '--->', fit.get_variable_value(variable_name))