parcmepperman

STEP: 0
Matches 0.1003
STEP: 100
Matches 0.1003
STEP: 200
Matches 0.1048

parcmepperman

import numpy as np
import re
import itertools
from collections import Counter


def clean_str(string):
    """
    Tokenization/string cleaning for all datasets except for SST.
    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py

parcmepperman

""" Auto Encoder Example.

Build a 2 layers auto-encoder with TensorFlow to compress images to a
lower latent space and then reconstruct them.

"""
from __future__ import division, print_function, absolute_import

import tensorflow as tf
import numpy as np

parcmepperman

#3
# Parameters
learning_rate = 0.0001 # 3.a (0.001 changed to .0001)
training_iters = 15000 # 3.a (10000 changed to 15000)
display_step = 1200    # 3.a (1000  changed to 1200)
n_input = 4           # 3.a (3     changed to 4)

Iter= 1200, Average Loss= 8.120618, Average Accuracy= 3.50%
[';', 'then', 'she', 'looked'] - [at] vs [was]
Iter= 2400, Average Loss= 5.403042, Average Accuracy= 4.83%

parcmepperman

First run of CNNmodel.py (nothing changed)
step 0, training accuracy 0.18
step 100, training accuracy 0.88
step 200, training accuracy 0.94
step 300, training accuracy 0.96
step 400, training accuracy 0.92
test accuracy 0.9433
Time for building convnet:
103588

parcmepperman

FIRST TEST (NO CHANGES)
2018-07-17T13:36:08.644717: step 1, loss 2.70243, acc 0.416667
2018-07-17T13:36:08.729154: step 2, loss 1.44836, acc 0.722222
2018-07-17T13:36:08.780371: step 3, loss 0.831631, acc 0.722222
2018-07-17T13:36:08.834600: step 4, loss 0.780276, acc 0.638889
2018-07-17T13:36:08.894865: step 5, loss 0.97322, acc 0.638889
2018-07-17T13:36:08.956489: step 6, loss 0.754949, acc 0.75
2018-07-17T13:36:09.007231: step 7, loss 0.949148, acc 0.75
2018-07-17T13:36:09.065482: step 8, loss 0.449501, acc 0.833333

parcmepperman

from mpl_toolkits.mplot3d import Axes3D  #required for 3d plotting mandatory
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import xlrd


# Improvement to the Linear Regression and new Smoking data set
DATA_FILE = 'Smoking.xls'
# Step 1: read in data from the .xls file

parcmepperman

import tensorflow as tf


# first approach is simple matrices using the tf.constant
# a,b,c are all 1x3 matrices, d is the calculated function
a = tf.constant([1, 2, 3, 1, 2, 3])
b = tf.constant([3, 2, 1, 1, 2, 3])
c = tf.constant([4, 5, 6, 1, 2, 3])
d = (a*a + b) * c

parcmepperman

<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
  <component name="NewModuleRootManager">
    <content url="file://$MODULE_DIR$" />
    <orderEntry type="jdk" jdkName="Python 3.6 (CS490)" jdkType="Python SDK" />
    <orderEntry type="sourceFolder" forTests="false" />
  </component>
</module>

parcmepperman

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