train_with_neural_network.py

""" Neural Network.

A N-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron)
implementation with TensorFlow.

This example is using TensorFlow layers, see 'neural_network_raw' example for
a raw implementation with variables.

Author: Gui Yuanmiao
Project: https://github.com/smalltalkman/hppi-tensorflow/
"""

from __future__ import print_function

# Import HPPI data
import os, hppi
hppids = hppi.read_data_sets(os.getcwd()+"/data/09-hppids", one_hot=False)

import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)

# Parameters
learning_rate = 0.01
num_steps = 10000
batch_size = 128
display_step = 100

# Network Parameters
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
num_input = 1106 # HPPI data input
num_classes =  2 # HPPI total classes


# Define the neural network
def neural_net(x_dict):
    # TF Estimator input is a dict, in case of multiple inputs
    x = x_dict['datas']
    # Hidden fully connected layer with 256 neurons
    layer_1 = tf.layers.dense(x, n_hidden_1, activation=tf.nn.relu)
    # Hidden fully connected layer with 256 neurons
    layer_2 = tf.layers.dense(layer_1, n_hidden_2, activation=tf.nn.relu)
    # Output fully connected layer with a neuron for each class
    out_layer = tf.layers.dense(layer_2, num_classes)
    return out_layer


# Define the model function (following TF Estimator Template)
def model_fn(features, labels, mode):
    # Build the neural network
    logits = neural_net(features)

    # Predictions
    pred_classes = tf.argmax(logits, axis=1)
    pred_probas = tf.nn.softmax(logits)

    # If prediction mode, early return
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode, predictions=pred_classes)

        # Define loss and optimizer
    loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=logits, labels=tf.cast(labels, dtype=tf.int32)))
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
    train_op = optimizer.minimize(loss_op,
                                  global_step=tf.train.get_global_step())

    # Evaluate the accuracy of the model
    acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)

    # TF Estimators requires to return a EstimatorSpec, that specify
    # the different ops for training, evaluating, ...
    estim_specs = tf.estimator.EstimatorSpec(
        mode=mode,
        predictions=pred_classes,
        loss=loss_op,
        train_op=train_op,
        eval_metric_ops={'accuracy': acc_op})

    return estim_specs

# Build the Estimator
model = tf.estimator.Estimator(model_fn,
                               model_dir=os.getcwd()+"/model/train_with_neural_network")

# Define the input function for training
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'datas': hppids.train.datas}, y=hppids.train.labels,
    batch_size=batch_size, num_epochs=None, shuffle=True, queue_capacity=60000)
# Train the Model
model.train(input_fn, steps=num_steps)

# Evaluate the Model
# Define the input function for evaluating
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'datas': hppids.test.datas}, y=hppids.test.labels,
    batch_size=batch_size, num_epochs=1, shuffle=False)
# Use the Estimator 'evaluate' method
e = model.evaluate(input_fn)

print("Testing Accuracy:", e['accuracy'])