shugert

# DRAW implementation
class draw_model():
    def __init__(self):
        
        # First we download the MNIST dataset into our local machine.
        self.mnist = input_data.read_data_sets("data/", one_hot=True)
        print "------------------------------------"
        print "MNIST Dataset Succesufully Imported"
        print "------------------------------------"
        self.n_samples = self.mnist.train.num_examples

shugert

# fully-conected layer
def dense(x, inputFeatures, outputFeatures, scope=None, with_w=False):
    with tf.variable_scope(scope or "Linear"):
        matrix = tf.get_variable("Matrix", [inputFeatures, outputFeatures], tf.float32, tf.random_normal_initializer(stddev=0.02))
        bias = tf.get_variable("bias", [outputFeatures], initializer=tf.constant_initializer(0.0))
        if with_w:
            return tf.matmul(x, matrix) + bias, matrix, bias
        else:
            return tf.matmul(x, matrix) + bias

shugert

# first we import our libraries
import tensorflow as tf
from tensorflow.examples.tutorials import mnist
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import scipy.misc
import os