evaluate_mlp.py

import tensorflow as tf
import urllib.request
import requests
import numpy as np
from datetime import datetime
import cv2
import json

sess = tf.InteractiveSession(config=tf.ConfigProto())

def abc():
    pass

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')

x = tf.placeholder(tf.float32, shape=[None, 240, 320, 3])
y_ = tf.placeholder(tf.float32, shape=[None, 3])

x_shaped = tf.reshape(x, [-1, 240 * 320 * 3])

W1 = weight_variable([240 * 320 * 3, 32])
b1 = bias_variable([32])
h1 = tf.sigmoid(tf.matmul(x_shaped, W1) + b1)

W2 = weight_variable([32, 3])
b2 = bias_variable([3])
y=tf.nn.softmax(tf.matmul(h1, W2) + b2)

saver = tf.train.Saver()
saver.restore(sess, "/Users/ryanzotti/Documents/repos/Self_Driving_RC_Car/tf_visual_data/runs/1/trained_model/model.ckpt")

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

npzfile = np.load("/Users/ryanzotti/Documents/repos/Self_Driving_RC_Car/final_processed_data_3_channels.npz")

# training data
train_predictors = npzfile['train_predictors']
train_targets = npzfile['train_targets']

# validation/test data
validation_predictors = npzfile['validation_predictors']
validation_targets = npzfile['validation_targets']
#validation_predictors, validation_targets = shuffle_dataset(validation_predictors, validation_targets)

print("validation accuracy %g" % accuracy.eval(feed_dict={x: validation_predictors/255, y_: validation_targets}))

print("Finished")