Regression using Tensorflow and multiple distinctive attributes

As we did in the previous 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”. In this tutorial, we will use multiple features to train our model. 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

Step1: loading dataset 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

Step2: 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()
  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

Step3: Split data

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
 count    12000.0 12000.0 12000.0 12000.0
 mean    119.6   35.6    28.6    1425.3
 std    2.0 2.1 12.6    1112.5
 min    114.5   32.5    1.0 3.0
 25%    118.0   33.9    18.0    793.0
 50%    118.5   34.2    29.0    1170.0
 75%    121.8   37.7    37.0    1726.0
 max    124.3   42.0    52.0    35682.0
 Validation examples summary:
 height    weight  age projects
 count    5000.0  5000.0  5000.0  5000.0
 mean    119.5   35.6    28.5    1439.9
 std    2.0 2.1 12.5    1228.5
 min    114.3   32.6    1.0 11.0
 25%    118.0   33.9    18.0    779.0
 50%    118.5   34.2    29.0    1159.0
 75%    121.8   37.7    37.0    1713.0
 max    124.3   42.0    52.0    28566.0
 Training targets summary:
 salary
 count    12000.0
 mean    207.2
 std    115.7
 min    15.0
 25%    119.7
 50%    180.4
 75%    264.6
 max    500.0
 Validation targets summary:
 salary
 count    5000.0
 mean    207.5
 std    116.6
 min    15.0
 25%    119.0
 50%    179.9
 75%    265.7
 max    500.0

Step4: 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):

    
    # Convert pandas data into a dict of np arrays.
    features = {key:np.array(value) for key,value in dict(features).items()}                                           
    
    # Construct a dataset, and configure batching/repeating.
    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit
    ds = ds.batch(batch_size).repeat(num_epochs)

    # Shuffle the data, if specified.
    if shuffle:
      ds = ds.shuffle(10000)
    
    # Return the next batch of data.
    features, labels = ds.make_one_shot_iterator().get_next()
    return features, labels

Step5: Training the model

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


  periods = 10
  steps_per_period = steps / periods

  # Create a 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
  )
    
  # Create 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
    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.")

  
  # Output a graph of loss metrics over periods.
  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 features and train model

This is the step where we will supply multiple features that is “height”, “weight”, “age”, “projects” all at once.

minimal_features = ["height","weight","age","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]


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)

WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
 For more information, please see:
 https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
 https://github.com/tensorflow/addons
 If you depend on functionality not listed there, please file an issue. 
 Training model…
 RMSE (on training data):
   period 00 : 173.38
   period 01 : 173.80
   period 02 : 187.86
   period 03 : 167.92
   period 04 : 186.66
   period 05 : 165.39
   period 06 : 165.32
   period 07 : 159.16
   period 08 : 166.23
   period 09 : 157.86
 Model training finished.
 h

Tensors: The building block of TensorFlow

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4. 

   Very nice thanks for post

5. 

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