Regression using Tensorflow and partition of data for robust validation.

Again in the tutorial will use Gradient descent optimization algorithm. Additionally, we will divide our data set into three slices, Training, Testing, and validation. In our example, we have data in CSV format with columns “height weight age projects salary”. Assuming there is a correlation between projects and salary will try to predict the salary given projects completed. You download data using this link: “https://drive.google.com/file/d/1Gx0riTlJHt9o_VyokrKNbj384AhwXpAW/view?usp=sharing”

Importing essential libraries

from __future__ import print_function

import math ##For basic mathematical operations

from IPython import display ## Plot setup for Ipython
from matplotlib import cm ##  Colormap reference
from matplotlib import gridspec ##plot setups
from matplotlib import pyplot as plt ##plot setups
import numpy as np 
import pandas as pd
from sklearn import metrics
import tensorflow as tf
from tensorflow.python.data import Dataset

from google.colab import drive ## Loading data directly from Google Drive
drive.mount('/content/gdrive') ## Mounting drive

tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format

Step 1: loading data-set and data randomization

dataframe = pd.read_csv("/content/gdrive/My Drive/Colab Notebooks/TENSOR_FLOW/train_dataset.csv", sep=",")
#dataframe["height"] = dataframe["height"]*-1
dataframe = dataframe.reindex(
    np.random.permutation(dataframe.index))
dataframe.head()

height    weight  age projects    salary
 1623    117.2   33.1    7   2015    279600
 12851    121.8   37.4    37  1569    286200
 10236    119.9   38.9    24  235 136800
 2783    117.7   34.1    29  1216    134300
 16170    122.5   37.8    40  1675    330000

Step 2: Preprocess features

This step is optional and can be used to create synthetic features we will cover this in upcoming posts.

def preprocess_features(dataframe):
  
  selected_features = dataframe[
    ["height",
     "weight",
     "age",
     "projects"]]
  
  processed_features = selected_features.copy()
  # Create a synthetic feature.
  processed_features["height_weight"] = (
    dataframe["height"] /
    dataframe["weight"])
  return processed_features

def preprocess_targets(dataframe):

  output_targets = pd.DataFrame()
  # Scale the target to be in units of thousands of dollars.
  output_targets["salary"] = (
    dataframe["salary"] / 1000.0)
  return output_targets

Step 3: Split data-set

Choose the first 12000 (out of 17000) examples for training then choose the last 5000 (out of 17000) examples for validation.

training_examples = preprocess_features(dataframe.head(12000))
training_targets = preprocess_targets(dataframe.head(12000))

validation_examples = preprocess_features(dataframe.tail(5000))
validation_targets = preprocess_targets(dataframe.tail(5000))


print("Training examples summary:")
display.display(training_examples.describe())
print("Validation examples summary:")
display.display(validation_examples.describe())

print("Training targets summary:")
display.display(training_targets.describe())
print("Validation targets summary:")
display.display(validation_targets.describe())

Training examples summary:
 height    weight  age projects    height_weight
 count    12000.0 12000.0 12000.0 12000.0 12000.0
 mean    119.6   35.6    28.7    1423.6  3.4
 std    2.0 2.1 12.6    1168.4  0.1
 min    114.3   32.5    1.0 3.0 2.9
 25%    118.0   33.9    18.0    788.0   3.2
 50%    118.5   34.2    29.0    1160.0  3.5
 75%    121.8   37.7    37.0    1704.0  3.5
 max    124.3   41.9    52.0    35682.0 3.6
 Validation examples summary:
 height    weight  age projects    height_weight
 count    5000.0  5000.0  5000.0  5000.0  5000.0
 mean    119.6   35.6    28.3    1443.8  3.4
 std    2.0 2.1 12.6    1096.9  0.1
 min    114.6   32.5    2.0 6.0 2.9
 25%    118.0   33.9    18.0    792.0   3.2
 50%    118.5   34.3    28.0    1185.0  3.4
 75%    121.8   37.7    37.0    1756.0  3.5
 max    124.3   42.0    52.0    11956.0 3.6
 Training targets summary:
 salary
 count    12000.0
 mean    207.6
 std    115.9
 min    15.0
 25%    119.8
 50%    180.5
 75%    266.2
 max    500.0
 Validation targets summary:
 salary
 count    5000.0
 mean    206.5
 std    116.1
 min    15.0
 25%    119.1
 50%    179.9
 75%    262.5
 max    500.0

Step 4: Feature construction

def construct_feature_columns(input_features):
  return set([tf.feature_column.numeric_column(my_feature)
              for my_feature in input_features])

def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
    features = {key:np.array(value) for key,value in dict(features).items()}                                           
    
    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit
    ds = ds.batch(batch_size).repeat(num_epochs)

    if shuffle:
      ds = ds.shuffle(10000)
    
    features, labels = ds.make_one_shot_iterator().get_next()
    return features, labels

Step 5: Training model

def train_model(
    learning_rate,
    steps,
    batch_size,
    training_examples,
    training_targets,
    validation_examples,
    validation_targets):


  periods = 10
  steps_per_period = steps / periods

  # linear regressor object.
  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)
  linear_regressor = tf.estimator.LinearRegressor(
      feature_columns=construct_feature_columns(training_examples),
      optimizer=my_optimizer
  )
    
  # input functions.
  training_input_fn = lambda: my_input_fn(training_examples, 
                                          training_targets["salary"], 
                                          batch_size=batch_size)
  predict_training_input_fn = lambda: my_input_fn(training_examples, 
                                                  training_targets["salary"], 
                                                  num_epochs=1, 
                                                  shuffle=False)
  predict_validation_input_fn = lambda: my_input_fn(validation_examples, 
                                                    validation_targets["salary"], 
                                                    num_epochs=1, 
                                                    shuffle=False)

  # Train the model
  print("Training model...")
  print("RMSE (on training data):")
  training_rmse = []
  validation_rmse = []
  for period in range (0, periods):
    # Train the model, starting from the prior state.
    linear_regressor.train(
        input_fn=training_input_fn,
        steps=steps_per_period,
    )
    
    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)
    training_predictions = np.array([item['predictions'][0] for item in training_predictions])
    
    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)
    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])
    
    .
    training_root_mean_squared_error = math.sqrt(
        metrics.mean_squared_error(training_predictions, training_targets))
    validation_root_mean_squared_error = math.sqrt(
        metrics.mean_squared_error(validation_predictions, validation_targets))
    
    print("  period %02d : %0.2f" % (period, training_root_mean_squared_error))
    
    training_rmse.append(training_root_mean_squared_error)
    validation_rmse.append(validation_root_mean_squared_error)
  print("Model training finished.")

  
  # Plots.
  plt.ylabel("RMSE")
  plt.xlabel("Periods")
  plt.title("Root Mean Squared Error vs. Periods")
  plt.tight_layout()
  plt.plot(training_rmse, label="training")
  plt.plot(validation_rmse, label="validation")
  plt.legend()

  return linear_regressor

Supply feature and train model

minimal_features = ["height_weight","projects"]

assert minimal_features, "You must select at least one feature!"

minimal_training_examples = training_examples[minimal_features]
minimal_validation_examples = validation_examples[minimal_features]

#
# Don't forget to adjust these parameters.
#
train_model(
    learning_rate=0.001,
    steps=500,
    batch_size=5,
    training_examples=minimal_training_examples,
    training_targets=training_targets,
    validation_examples=minimal_validation_examples,
    validation_targets=validation_targets)

Training model…
 RMSE (on training data):
   period 00 : 188.03
   period 01 : 203.27
   period 02 : 203.27
   period 03 : 188.01
   period 04 : 187.99
   period 05 : 194.65
   period 06 : 194.66
   period 07 : 183.70
   period 08 : 183.70
   period 09 : 196.58
 Model training finished.

---

Tensors: The building block of TensorFlow

• This AI Paper Explores the Fusion of Cognitive Science and Machine Learning in Pursuit of Superhuman Mathematical Systems
• Generate Information-Rich Text for a Strong Cross-Modal Interface in LLMs with De-Diffusion
• This AI Paper Proposes a Novel Pre-Training Strategy Called Privacy-Preserving MAE-Align’ to Effectively Combine Synthetic Data and Human-Removed Real Data
• Google and MIT Researchers Introduce StableRep: Revolutionizing AI Training with Synthetic Imagery for Enhanced Machine Learning
• Meet One-2-3-45++: An Innovative Artificial Intelligence Method that Transforms a Single Image into a Detailed 3D Textured Mesh in Approximately One Minute

1. 

   Very nice thanks for post

2. 

   Hi Nilesh, Nice work with the computer generated Bible. I had a similar thought with another piece of art. Wanted…

3. 

   Very nice

4. 

   Thank you

5. 

   Good work