custom_visualization.py

import os
import tensorflow as tf
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# Turn off TensorFlow warning messages in program output
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# Load training data set from CSV file
training_data_df = pd.read_csv("sales_data_training.csv", dtype=float)

# Pull out columns for X (data to train with) and Y (value to predict)
X_training = training_data_df.drop('total_earnings', axis=1).values
Y_training = training_data_df[['total_earnings']].values

# Load testing data set from CSV file
test_data_df = pd.read_csv("sales_data_test.csv", dtype=float)

# Pull out columns for X (data to train with) and Y (value to predict)
X_testing = test_data_df.drop('total_earnings', axis=1).values
Y_testing = test_data_df[['total_earnings']].values

# All data needs to be scaled to a small range like 0 to 1 for the neural
# network to work well. Create scalers for the inputs and outputs.
X_scaler = MinMaxScaler(feature_range=(0, 1))
Y_scaler = MinMaxScaler(feature_range=(0, 1))

# Scale both the training inputs and outputs
X_scaled_training = X_scaler.fit_transform(X_training)
Y_scaled_training = Y_scaler.fit_transform(Y_training)

# It's very important that the training and test data are scaled with the same scaler.
X_scaled_testing = X_scaler.transform(X_testing)
Y_scaled_testing = Y_scaler.transform(Y_testing)

# Define model parameters
RUN_NAME = "histogram_visualization"
learning_rate = 0.001
training_epochs = 100

# Define how many inputs and outputs are in our neural network
number_of_inputs = 9
number_of_outputs = 1

# Define how many neurons we want in each layer of our neural network
layer_1_nodes = 50
layer_2_nodes = 100
layer_3_nodes = 50

# Section One: Define the layers of the neural network itself

# Input Layer
with tf.variable_scope('input'):
    X = tf.placeholder(tf.float32, shape=(None, number_of_inputs), name="X")

# Layer 1
with tf.variable_scope('layer_1'):
    weights = tf.get_variable("weights1", shape=[number_of_inputs, layer_1_nodes], initializer=tf.contrib.layers.xavier_initializer())
    biases = tf.get_variable(name="biases1", shape=[layer_1_nodes], initializer=tf.zeros_initializer())
    layer_1_output = tf.nn.relu(tf.matmul(X, weights) + biases)

# Layer 2
with tf.variable_scope('layer_2'):
    weights = tf.get_variable("weights2", shape=[layer_1_nodes, layer_2_nodes], initializer=tf.contrib.layers.xavier_initializer())
    biases = tf.get_variable(name="biases2", shape=[layer_2_nodes], initializer=tf.zeros_initializer())
    layer_2_output = tf.nn.relu(tf.matmul(layer_1_output, weights) + biases)

# Layer 3
with tf.variable_scope('layer_3'):
    weights = tf.get_variable("weights3", shape=[layer_2_nodes, layer_3_nodes], initializer=tf.contrib.layers.xavier_initializer())
    biases = tf.get_variable(name="biases3", shape=[layer_3_nodes], initializer=tf.zeros_initializer())
    layer_3_output = tf.nn.relu(tf.matmul(layer_2_output, weights) + biases)

# Output Layer
with tf.variable_scope('output'):
    weights = tf.get_variable("weights4", shape=[layer_3_nodes, number_of_outputs], initializer=tf.contrib.layers.xavier_initializer())
    biases = tf.get_variable(name="biases4", shape=[number_of_outputs], initializer=tf.zeros_initializer())
    prediction = tf.matmul(layer_3_output, weights) + biases

# Section Two: Define the cost function of the neural network that will be optimized during training

with tf.variable_scope('cost'):
    Y = tf.placeholder(tf.float32, shape=(None, 1), name="Y")
    cost = tf.reduce_mean(tf.squared_difference(prediction, Y))

# Section Three: Define the optimizer function that will be run to optimize the neural network

with tf.variable_scope('train'):
    optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)

# Create a summary operation to log the progress of the network
with tf.variable_scope('logging'):
    tf.summary.scalar('current_cost', cost)
    tf.summary.histogram('predicted_value', prediction)
    summary = tf.summary.merge_all()

# Initialize a session so that we can run TensorFlow operations
with tf.Session() as session:

    # Run the global variable initializer to initialize all variables and layers of the neural network
    session.run(tf.global_variables_initializer())

    # Create log file writers to record training progress.
    # We'll store training and testing log data separately.
    training_writer = tf.summary.FileWriter("./logs/{}/training".format(RUN_NAME), session.graph)
    testing_writer = tf.summary.FileWriter("./logs/{}/testing".format(RUN_NAME), session.graph)

    # Run the optimizer over and over to train the network.
    # One epoch is one full run through the training data set.
    for epoch in range(training_epochs):

        # Feed in the training data and do one step of neural network training
        session.run(optimizer, feed_dict={X: X_scaled_training, Y: Y_scaled_training})

        # Every few training steps, log our progress
        if epoch % 5 == 0:
            # Get the current accuracy scores by running the "cost" operation on the training and test data sets
            training_cost, training_summary = session.run([cost, summary], feed_dict={X: X_scaled_training, Y:Y_scaled_training})
            testing_cost, testing_summary = session.run([cost, summary], feed_dict={X: X_scaled_testing, Y:Y_scaled_testing})

            # Write the current training status to the log files (Which we can view with TensorBoard)
            training_writer.add_summary(training_summary, epoch)
            testing_writer.add_summary(testing_summary, epoch)

            # Print the current training status to the screen
            print("Epoch: {} - Training Cost: {}  Testing Cost: {}".format(epoch, training_cost, testing_cost))

    # Training is now complete!

    # Get the final accuracy scores by running the "cost" operation on the training and test data sets
    final_training_cost = session.run(cost, feed_dict={X: X_scaled_training, Y: Y_scaled_training})
    final_testing_cost = session.run(cost, feed_dict={X: X_scaled_testing, Y: Y_scaled_testing})

    print("Final Training cost: {}".format(final_training_cost))
    print("Final Testing cost: {}".format(final_testing_cost))