Alphabet recognition by using LogisticRegression model from sklearn.linear_model. (Udacity Assignment 1)

Continued from previous assignment, we saved pickle data from some sample alphabet images.

Now we are going to extract the data and train the data, so that we can recognize the alphabet.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
import random
import hashlib
from IPython.display import display, Image
from sklearn.linear_model import LogisticRegression
from six.moves.urllib.request import urlretrieve
from six.moves import cPickle as pickle

test_filename = "notMNIST.pickle"

def read_data(picklename,data_name):
  if os.path.exists(picklename):
 with open(picklename, 'rb') as f:
   letter_set = pickle.load(f)
  return letter_set[data_name]

train_dataset = read_data(test_filename,'train_dataset')
train_labels = read_data(test_filename,'train_labels')
valid_dataset = read_data(test_filename,'valid_dataset')
valid_labels = read_data(test_filename,'valid_labels')
test_dataset = read_data(test_filename,'test_dataset')
test_labels = read_data(test_filename,'test_labels')

Let's train this data using 50, 100, 1000, and 5000 training samples and see how's the result outcome.
#Display multiple sample set
pretty_labels = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'J'}

def disp_sample_dataset(dataset, labels):
  items = random.sample(range(len(labels)), 8)
  for i, item in enumerate(items):
    plt.subplot(2, 4, i+1)
    plt.axis('off')
    plt.title(pretty_labels[labels[item]])
    plt.imshow(dataset[item])
  plt.show()
#end

#Train data by using Logistic Regression
regr = LogisticRegression()
X_test = test_dataset.reshape(test_dataset.shape[0], 28 * 28)
y_test = test_labels

sample_size = 50
X_train = train_dataset[:sample_size].reshape(sample_size, 784)
y_train = train_labels[:sample_size]
print('Sample size: ', sample_size)
%time regr.fit(X_train, y_train)
print('Score: ',regr.score(X_test, y_test))
pred_labels = regr.predict(X_test)
disp_sample_dataset(test_dataset, pred_labels)

sample_size = 100
X_train = train_dataset[:sample_size].reshape(sample_size, 784)
y_train = train_labels[:sample_size]
print('Sample size = ', sample_size)
%time regr.fit(X_train, y_train)
print('Score: ',regr.score(X_test, y_test))
pred_labels = regr.predict(X_test)
disp_sample_dataset(test_dataset, pred_labels)

sample_size = 1000
X_train = train_dataset[:sample_size].reshape(sample_size, 784)
y_train = train_labels[:sample_size]
print('Sample size = ', sample_size)
%time regr.fit(X_train, y_train)
print('Score: ',regr.score(X_test, y_test))
pred_labels = regr.predict(X_test)
disp_sample_dataset(test_dataset, pred_labels)

X_valid = valid_dataset[:sample_size].reshape(sample_size, 784)
y_valid = valid_labels[:sample_size]
%time regr.score(X_valid, y_valid)
pred_labels = regr.predict(X_valid)
disp_sample_dataset(valid_dataset, pred_labels)

sample_size = 5000
X_train = train_dataset[:sample_size].reshape(sample_size, 784)
y_train = train_labels[:sample_size]
print('Sample size = ', sample_size)
%time regr.fit(X_train, y_train)
print('Score: ',regr.score(X_test, y_test))
pred_labels = regr.predict(X_test)
disp_sample_dataset(test_dataset, pred_labels)








Outcomes:


Sample size:  50
Wall time: 78 ms
Score:  0.509



























Sample size =  100
Wall time: 156 ms
Score:  0.6966



























Sample size =  1000
Wall time: 3.19 s
Score:  0.8333



























Wall time: 0 ns



























Sample size =  5000
Wall time: 27.2 s
Score:  0.851
























Based on the outcomes, the result is getting more accurate with increasing of training samples.







But in order to train all the data, this method will be too slow. So, can use the fastest method by using SAG solver.







Outcomes:








Wall time: 14min 35s






















Hmm, seem likes the accuracy is still not 100%.







Anyway, thanks to sample solutions provided by khanhnamle1994 which allows us to refer and learn.Github: https://github.com/khanhnamle1994














































Popular posts from this blog









Accuracy for Logistic Regression model is always lower than using Convolutional neural network




















Image







Here's the coding for a logistic regression model with 100k samples. The accuracy is only 86.7% compared than 96% for ConvNet. ROC Curve for logistic is 0.97 compared to 0.99 for ConvNet.   from __future__ import print_function import numpy as np import matplotlib.pyplot as plt import os  from sklearn.metrics import roc_curve, auc from sklearn.linear_model import LogisticRegression from six.moves import cPickle as pickle  from sklearn.metrics import confusion_matrix  def load_data():     test_filename = "notMNIST.pickle"     if os.path.exists(test_filename):         with open(test_filename, 'rb') as f:             letter_dataset = pickle.load(f)     return (letter_dataset)  sample_size = 10000 print('Sample size: ', sample_size)  lt_dt = load_data() x_train = lt_dt['train_dataset'][:sample_size] y_train = lt_dt['train_labels'][:sample_size] x_test = lt_dt['test_dataset'] y_test = lt_dt['test_labels']  regr = LogisticRegressi...








Read more










Simple data curation practices using Numpy (Udacity Deep Learning Assignment 1)





















First, download the compressed dataset "notMNIST_large.tar.gz" and "notMNIST_small.tar.gz" from  http://yaroslavvb.com/upload/notMNIST/   OR download from others site using full code provided below.   url  =  'https://commondatastorage.googleapis.com/books1000/'  last_percent_reported  =  None  data_root  =  os.getcwd() #path to save file   def  download_progress_hook ( count ,  blockSize ,  totalSize ):    """A hook to report the progress of a download. This is mostly intended for users with    slow internet connections. Reports every 5% change in download progress.    """    global  last_percent_reported    percent  =  int ( count  *  blockSize  *  100  /  totalSize )     if  last_percent_reported  !=  percent :      if  percent  %  5  ==  0 :        sys . stdout . write ( " %s%% "  %  percent )        sys . stdout . flush ()      else :        sys . stdout . write ( "." )        sys . stdout . flush ()    ...








Read more










96.24% accuracy with higher epoch numbers for Convolutional Neural Network




















Image







    For  Convolutional Neural Network , there are a lots of factors affect the model accuracy. Factors included the structure of  Convolutional Neural Network , hyper parameters  values, over-fitting  and etc.   Only correct or suitable network structure can produce high model accuracy. If wrong network structure is used, whatever you tune the hyper parameters, the accuracy will still worse. So, after we found the right network structure, only we can start to tune the hyper parameters. However, over-fitting may happened on your model accuracy. There are several methods to prevent that. One of the over-fitting prevention is by using the dropout function. Dropout give the model a way to find alternative way to train the model by removed some characteristic according to the ratio. Another way to prevent over-fitting is by increasing the number of samples. Besides, we can also simplify the network structure. Some complicated network structure sometimes can cause over-fitting too.    Fi...








Read more










Accuracy 96% with Simple Deep NN using Keras Theano backend for nonMNist alphabet recognition




















Image







    Here's first try to build a simple simple deep neurons network by using Keras with Theano backend.   By using the previous data pickles sample   nonMNist dataset  we have saved, here's the completed coding.   The summarized deep filter layers can be described as combination of common dense layer with 512 units, relu activation, dropout ratio of 0.2, softmax activation, and RMSprop optimizer.   from __future__ import print_function  import keras from keras.models import Sequential from keras.layers import Dense, Dropout from keras.optimizers import RMSprop from keras.utils import np_utils import os from six.moves import cPickle as pickle  batch_size = 128 num_classes = 10 epochs = 20  def load_data():     test_filename = "notMNIST.pickle"     if os.path.exists(test_filename):         with open(test_filename, 'rb') as f:             letter_dataset = pickle.load(f)     return (letter_dataset)          lt_dt = load_data()      train_dataset = lt_dt['train_...








Read more










Python to read and record SQM-LE data, search saved data and find IP address..




















Image







  Unihedron Sky Quality Meter with lens and Ethernet (SQM-LE) is used to monitor sky brightness for light pollution analysis.     Here's some codes used to read and record SQM-LE data, search saved data and find IP address.      Google Drive: https://drive.google.com/open?id=1Dfc1mvKKRsWB12AAYpjOnvCekrGP85xh     Below is the example of recorded format in txt files.   15-01-17-Time:22:40 r, 15.54m,0000000052Hz,0000008592c,0000000.019s, 030.9C   15-01-17-Time:22:41 r, 16.21m,0000000050Hz,0000015911c,0000000.035s, 031.5C   15-01-17-Time:22:42 r, 16.41m,0000000025Hz,0000019128c,0000000.042s, 031.9C   15-01-17-Time:22:43 r, 16.30m,0000000027Hz,0000017304c,0000000.038s, 032.2C   15-01-17-Time:22:44 r, 16.37m,0000000025Hz,0000018388c,0000000.040s, 032.5C   15-01-17-Time:22:45 r, 16.43m,0000000024Hz,0000019481c,0000000.042s, 032.8C            Sample picture for Unihedron SQM-LE        








Read more










Using mini batch SGD Neural Network in Alphabet recognition!





















Here we are using mini-batch stochastic gradient descent neural network method in Alphabet recognition with previous data set mentioned in  nonMNIST data set .   First, we are going to build the neural network part.  Here we have adjustable number of hidden neurons, layers, epochs, mini batch sizes that allow us to tune the accuracy later.   #network part   import random import numpy as np  class Network(object):          def __init__(self, sizes):         #sizes: number of neurons (eg: (2,3,1))         #biases and weights are initialized randomly                  self.num_layers = len(sizes)         self.sizes = sizes         self.biases = [np.random.randn(y, 1) for y in sizes[1:]]         self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]              def feedforward(self, a):         #return output of network f a is input                  for b, w in zip(self.biases, self.weights):             a = sigmoid(np.dot(w, a) + b)         return a        ...








Read more










Tower Defense (Endless TD Beta)




















Image







Hi All, this is my third game written by myself.  This game is built with Visual Studio 2005 in C#.   ** If game cannot be opened, please run as administrator.   Screenshot:          Software Link:  https://drive.google.com/open?id=0B7_726VmtxyoUmk3aTdEZFBXT0k   Software coding Link:  https://drive.google.com/open?id=0B7_726VmtxyoLWJpdm9pVzRLZ2M








Read more










First try to solve Polynomial equations by Tensorflow





















First try to solve polynomial equations y=3x^2+2x-5 using Tensorflow Output with 100000 looptimes:  A: [ 2.99184537] B: [ 2.0156281] C: [-5.00763798] D: [ 2.00114322] loss: 4.62821e-07    from __future__ import print_function import tensorflow as tf  sess = tf.Session();  #Model Parameters A = tf.Variable([ 1. ], dtype =tf.float32) B = tf.Variable([ 1. ], dtype =tf.float32) C = tf.Variable([- 1. ], dtype =tf.float32) D = tf.Variable([ 1. ], dtype =tf.float32)  #Model Input and Output x = tf.placeholder(tf.float32) y = tf.placeholder(tf.float32) unknown_model = A * x ** D + B * x + C  #Loss loss = tf.reduce_sum(tf.square(unknown_model - y))  #Optimizer optimizer = tf.train.AdamOptimizer( 0.01 ) train = optimizer.minimize(loss)  #Training Data x_train = [ 1 , 2 , 3 , 4 ] y_train = [ 0 , 11 , 28 , 51 ]  #Training Loop init = tf.global_variables_initializer() sess.run(init) for i in range ( 100000 ):     sess.run(train, {x:x_train, y:y_train})  #evaluate training accuracy curr_A, curr_B, c...








Read more










Endless Falling




















Image







Hi all, please get this mini game called Endless Falling.   ** If game cannot be opened, please run as administrator.   Screenshot:     Game Link:  https://drive.google.com/open?id=0B7_726VmtxyoZ3dTREE3c3Q4Tnc   Software coding Link:  https://drive.google.com/open?id=0B7_726VmtxyodXlVdVdtY0I5R3c








Read more