├── Procfile ├── README.md ├── __pycache__ └── config.cpython-34.pyc ├── app ├── __init__.py ├── __init__.pyc ├── __pycache__ │ ├── __init__.cpython-34.pyc │ ├── forms.cpython-34.pyc │ └── views.cpython-34.pyc ├── model │ ├── __init__.pyc │ ├── __pycache__ │ │ ├── __init__.cpython-34.pyc │ │ ├── alpha_cnn_predict.cpython-34.pyc │ │ └── preprocessor.cpython-34.pyc │ ├── alpha_cnn_predict.py │ ├── alpha_cnn_predict.pyc │ ├── alpha_weights.pkl │ ├── preprocessor.py │ └── preprocessor.pyc ├── static │ ├── analytics.js │ ├── bootstrap-theme.min.css │ ├── bootstrap.min.css │ ├── bootstrap.min.js │ ├── css │ ├── img │ │ ├── cnn_ocr.png │ │ ├── glyphicons-halflings-white.png │ │ ├── glyphicons-halflings.png │ │ ├── python.ico │ │ ├── python.png │ │ ├── recognized_o.png │ │ └── recognized_q.png │ ├── jquery.min.js │ ├── js │ │ ├── MathJax.js │ │ ├── bootstrap.min.js │ │ └── ocr_canvas.js │ └── style.css ├── templates │ ├── base.html │ └── index.html ├── views.py └── views.pyc ├── config.pyc ├── convolutional_network_tutorial.ipynb ├── dataset.txt ├── requirements.txt ├── run.py ├── runtime.txt └── temp.jpg /Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn --log-file - app:app 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Convolutional Neural Network 2 | 3 | ## Overview 4 | 5 | This is the code for [this](https://youtu.be/FTr3n7uBIuE) video on Youtube by Siraj Raval as part of The Math of Intelligence course. A convolutional neural network implemented in pure numpy. It uses a MNIST-like dataset with about 30 alphanumeric symbols. The author trained a deep convolutional network using Keras and saved the weights using python's pickle utility. Only the the forward propagation code is rewritten in pure numpy (as opposed to Theano or Tensorflow as in Keras). Which lets us run the network as a demo via heroku. For backpropagation in numpy for a convnet see [this](https://github.com/Kankroc/NaiveCNN) 6 | 7 | ![recognized_o.png](https://github.com/greydanus/pythonic_ocr/blob/master/app/static/img/recognized_o.png) ![recognized_q.png](https://github.com/greydanus/pythonic_ocr/blob/master/app/static/img/recognized_q.png) 8 | 9 | Live web app is here: 10 | [Website](https://pythonic-ocr.herokuapp.com/) 11 | 12 | 13 | ## Dependencies 14 | -------- 15 | 16 | Dependencies are packaged in the flask folder, so this app does not have any external depencies. Run `pip install -r requirements.txt` to install them. 17 | 18 | Install pip [here](https://pip.pypa.io/en/stable/). 19 | 20 | 21 | ## Usage 22 | 23 | to start the web app run `python run.py` . To start the notebook run `jupyter notebook` in terminal. 24 | 25 | Install jupyter [here](http://jupyter.readthedocs.io/en/latest/install.html). 26 | 27 | 28 | ## Credits 29 | 30 | Credits for this code go to [greydanus](https://github.com/greydanus/pythonic_ocr). I've merely created a wrapper to get people started. 31 | 32 | -------------------------------------------------------------------------------- /__pycache__/config.cpython-34.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/__pycache__/config.cpython-34.pyc -------------------------------------------------------------------------------- /app/__init__.py: -------------------------------------------------------------------------------- 1 | __all__ = ["model"] 2 | 3 | from flask import Flask 4 | 5 | app = Flask(__name__) 6 | app.config.from_object('config') 7 | 8 | from app import views -------------------------------------------------------------------------------- /app/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/__init__.pyc -------------------------------------------------------------------------------- /app/__pycache__/__init__.cpython-34.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/__pycache__/__init__.cpython-34.pyc -------------------------------------------------------------------------------- /app/__pycache__/forms.cpython-34.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/__pycache__/forms.cpython-34.pyc -------------------------------------------------------------------------------- /app/__pycache__/views.cpython-34.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/__pycache__/views.cpython-34.pyc -------------------------------------------------------------------------------- /app/model/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/model/__init__.pyc -------------------------------------------------------------------------------- /app/model/__pycache__/__init__.cpython-34.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/model/__pycache__/__init__.cpython-34.pyc -------------------------------------------------------------------------------- /app/model/__pycache__/alpha_cnn_predict.cpython-34.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/model/__pycache__/alpha_cnn_predict.cpython-34.pyc -------------------------------------------------------------------------------- /app/model/__pycache__/preprocessor.cpython-34.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/model/__pycache__/preprocessor.cpython-34.pyc -------------------------------------------------------------------------------- /app/model/alpha_cnn_predict.py: -------------------------------------------------------------------------------- 1 | import pickle 2 | import numpy as np 3 | from app.model.preprocessor import Preprocessor as img_prep 4 | 5 | class LiteOCR: 6 | def __init__(self, fn="alpha_weights.pkl", pool_size=2): 7 | [weights, meta] = pickle.load(open(fn, 'rb'), encoding='latin1') #currently, this class MUST be initialized from a pickle file 8 | self.vocab = meta["vocab"] 9 | 10 | self.img_rows = meta["img_side"] ; self.img_cols = meta["img_side"] 11 | 12 | self.CNN = LiteCNN() 13 | self.CNN.load_weights(weights) 14 | self.CNN.pool_size=int(pool_size) 15 | 16 | def predict(self, image): 17 | print(image.shape) 18 | X = np.reshape(image, (1, 1, self.img_rows, self.img_cols)) 19 | X = X.astype("float32") 20 | 21 | predicted_i = self.CNN.predict(X) 22 | return self.vocab[predicted_i] 23 | 24 | class LiteCNN: 25 | def __init__(self): 26 | self.layers = [] # a place to store the layers 27 | self.pool_size = None # size of pooling area for max pooling 28 | 29 | def load_weights(self, weights): 30 | assert not self.layers, "Weights can only be loaded once!" 31 | for k in range(len(weights.keys())): 32 | self.layers.append(weights['layer_{}'.format(k)]) 33 | 34 | def predict(self, X): 35 | assert not not self.layers, "Weights must be loaded before making a prediction!" 36 | h = self.cnn_layer(X, layer_i=0, border_mode="full") ; X = h 37 | h = self.relu_layer(X) ; X = h 38 | h = self.cnn_layer(X, layer_i=2, border_mode="valid") ; X = h 39 | h = self.relu_layer(X) ; X = h 40 | h = self.maxpooling_layer(X) ; X = h 41 | h = self.dropout_layer(X, .25) ; X = h 42 | h = self.flatten_layer(X) ; X = h 43 | h = self.dense_layer(X, layer_i=7) ; X = h 44 | h = self.relu_layer(X) ; X = h 45 | h = self.dropout_layer(X, .5) ; X = h 46 | h = self.dense_layer(X, layer_i=10) ; X = h 47 | h = self.softmax_layer2D(X) ; X = h 48 | max_i = self.classify(X) 49 | return max_i[0] 50 | 51 | def maxpooling_layer(self, convolved_features): 52 | nb_features = convolved_features.shape[0] 53 | nb_images = convolved_features.shape[1] 54 | conv_dim = convolved_features.shape[2] 55 | res_dim = int(conv_dim / self.pool_size) #assumed square shape 56 | 57 | pooled_features = np.zeros((nb_features, nb_images, res_dim, res_dim)) 58 | for image_i in range(nb_images): 59 | for feature_i in range(nb_features): 60 | for pool_row in range(res_dim): 61 | row_start = pool_row * self.pool_size 62 | row_end = row_start + self.pool_size 63 | 64 | for pool_col in range(res_dim): 65 | col_start = pool_col * self.pool_size 66 | col_end = col_start + self.pool_size 67 | 68 | patch = convolved_features[feature_i, image_i, row_start : row_end,col_start : col_end] 69 | pooled_features[feature_i, image_i, pool_row, pool_col] = np.max(patch) 70 | return pooled_features 71 | 72 | def cnn_layer(self, X, layer_i=0, border_mode = "full"): 73 | features = self.layers[layer_i]["param_0"] 74 | bias = self.layers[layer_i]["param_1"] 75 | 76 | patch_dim = features[0].shape[-1] 77 | nb_features = features.shape[0] 78 | image_dim = X.shape[2] #assume image square 79 | image_channels = X.shape[1] 80 | nb_images = X.shape[0] 81 | 82 | if border_mode == "full": 83 | conv_dim = image_dim + patch_dim - 1 84 | elif border_mode == "valid": 85 | conv_dim = image_dim - patch_dim + 1 86 | convolved_features = np.zeros((nb_images, nb_features, conv_dim, conv_dim)); 87 | for image_i in range(nb_images): 88 | for feature_i in range(nb_features): 89 | convolved_image = np.zeros((conv_dim, conv_dim)) 90 | for channel in range(image_channels): 91 | feature = features[feature_i, channel, :, :] 92 | 93 | image = X[image_i, channel, :, :] 94 | convolved_image += self.convolve2d(image, feature, border_mode); 95 | 96 | convolved_image = convolved_image + bias[feature_i] 97 | convolved_features[image_i, feature_i, :, :] = convolved_image 98 | return convolved_features 99 | 100 | def dense_layer(self, X, layer_i=0): 101 | W = self.layers[layer_i]["param_0"] 102 | b = self.layers[layer_i]["param_1"] 103 | output = np.dot(X, W) + b 104 | return output 105 | 106 | @staticmethod 107 | def convolve2d(image, feature, border_mode="full"): 108 | image_dim = np.array(image.shape) 109 | feature_dim = np.array(feature.shape) 110 | target_dim = image_dim + feature_dim - 1 111 | fft_result = np.fft.fft2(image, target_dim) * np.fft.fft2(feature, target_dim) 112 | target = np.fft.ifft2(fft_result).real 113 | 114 | if border_mode == "valid": 115 | # To compute a valid shape, either np.all(x_shape >= y_shape) or 116 | # np.all(y_shape >= x_shape). 117 | valid_dim = image_dim - feature_dim + 1 118 | if np.any(valid_dim < 1): 119 | valid_dim = feature_dim - image_dim + 1 120 | start_i = (target_dim - valid_dim) // 2 121 | end_i = start_i + valid_dim 122 | target = target[start_i[0]:end_i[0], start_i[1]:end_i[1]] 123 | return target 124 | 125 | @staticmethod 126 | def shuffle(X, y): 127 | assert X.shape[0] == y.shape[0], "X and y first dimensions must match" 128 | p = np.random.permutation(X.shape[0]) 129 | return X[p], y[p] 130 | 131 | @staticmethod 132 | def vectorize(y, vocab): 133 | nb_classes = len(vocab) 134 | Y = np.zeros((len(y), nb_classes)) 135 | for i in range(len(y)): 136 | index = np.where(np.char.find(vocab, y[i]) > -1)[0][0] 137 | Y[i, index] = 1. 138 | return Y 139 | 140 | @staticmethod 141 | def trtest_split(X,y,fraction): 142 | boundary=int(X.shape[0]*fraction) 143 | return (X[:boundary], y[:boundary]), (X[boundary:], y[boundary:]) 144 | 145 | @staticmethod 146 | def sigmoid(x): 147 | return 1.0/(1.0+np.exp(-x)) 148 | 149 | @staticmethod 150 | def hard_sigmoid(x): 151 | slope = 0.2 152 | shift = 0.5 153 | x = (x * slope) + shift 154 | x = np.clip(x, 0, 1) 155 | return x 156 | 157 | @staticmethod 158 | def relu_layer(x): 159 | z = np.zeros_like(x) 160 | return np.where(x>z,x,z) 161 | 162 | @staticmethod 163 | def softmax_layer2D(w): 164 | maxes = np.amax(w, axis=1) 165 | maxes = maxes.reshape(maxes.shape[0], 1) 166 | e = np.exp(w - maxes) 167 | dist = e / np.sum(e, axis=1, keepdims=True) 168 | return dist 169 | 170 | @staticmethod 171 | def repeat_vector(X, n): 172 | y = np.ones((X.shape[0], n, X.shape[2])) * X 173 | return y 174 | 175 | @staticmethod 176 | def dropout_layer(X, p): 177 | retain_prob = 1. - p 178 | X *= retain_prob 179 | return X 180 | 181 | @staticmethod 182 | def classify(X): 183 | return X.argmax(axis=-1) 184 | 185 | @staticmethod 186 | def flatten_layer(X): 187 | flatX = np.zeros((X.shape[0],np.prod(X.shape[1:]))) 188 | for i in range(X.shape[0]): 189 | flatX[i,:] = X[i].flatten(order='C') 190 | return flatX 191 | -------------------------------------------------------------------------------- /app/model/alpha_cnn_predict.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/model/alpha_cnn_predict.pyc -------------------------------------------------------------------------------- /app/model/preprocessor.py: -------------------------------------------------------------------------------- 1 | import pickle 2 | import numpy as np 3 | import os.path 4 | import base64 5 | from PIL import Image 6 | import sys 7 | 8 | class Preprocessor: 9 | def __init__(self, fn="dataset.txt"): 10 | self.datafile = fn 11 | self.target_shape = [20,20] 12 | self.splitchar = "&" 13 | 14 | if os.path.isfile(self.datafile): 15 | with open(fn, 'r') as f: 16 | self.vocab = eval(f.readline()) 17 | assert type(self.vocab) is list, "The first line of the file was not a vocab list." 18 | else: 19 | self.vocab = None 20 | 21 | def add_sample(self, data, index, vocab): 22 | assert self.vocab == vocab or self.vocab is None, "Vocab list has changed. You cannot append to this dataset" 23 | if self.vocab is None: 24 | with open(self.datafile, 'w') as f: 25 | v = str(vocab).replace("\n","") 26 | f.write(v) 27 | self.vocab = vocab 28 | n = self.preprocess(data) 29 | with open(self.datafile, "a") as f: 30 | n = str(n.tolist()).replace("\n","") 31 | f.write("\n" + str(self.vocab[index]) + self.splitchar + n) 32 | 33 | def load_sample(self, line_i = 0): 34 | X = None ; y = None 35 | if os.path.isfile(self.datafile): 36 | with open(self.datafile, 'r') as f: 37 | vocab = eval(f.readline()) 38 | assert vocab == self.vocab, "The vocab for this datafile does not match the current vocab" 39 | 40 | with open(self.datafile, "r") as f: 41 | lines = f.readlines() 42 | X = np.asarray(eval(lines[line_i + 1].split(self.splitchar)[1])) 43 | y = lines[line_i + 1].split(self.splitchar)[0] 44 | return [X, y] 45 | 46 | else: 47 | raise ValueError("Could not find the datafile") 48 | 49 | def load_all(self): 50 | X = None ; y = None 51 | if os.path.isfile(self.datafile): 52 | with open(self.datafile, 'r') as f: 53 | vocab = eval(f.readline()) 54 | assert vocab == self.vocab, "The vocab for this datafile does not match the current vocab" 55 | 56 | with open(self.datafile, "r") as f: 57 | lines = f.readlines() 58 | 59 | X = np.zeros((len(lines) - 1, self.target_shape[0]*self.target_shape[1])) 60 | y = [] 61 | 62 | for i in range (len(lines)-1): 63 | X[i,:] = np.asarray(eval(lines[i + 1].split(self.splitchar)[1])) 64 | y.append(lines[i + 1].split(self.splitchar)[0]) 65 | y = np.asarray(y) 66 | return [X, y] 67 | 68 | else: 69 | raise ValueError("Could not find the datafile") 70 | 71 | def preprocess(self, jpgtxt): 72 | # data = base64.decodestring(data) 73 | data = jpgtxt.split(',')[-1] 74 | data = base64.b64decode(data.encode('ascii')) 75 | 76 | g = open("temp.jpg", "wb") 77 | g.write(data) 78 | g.close() 79 | 80 | pic = Image.open("temp.jpg") 81 | M = np.array(pic) #now we have image data in numpy 82 | 83 | M = self.rgb2gray(M) 84 | M = self.squareTrim(M,threshold=0) 85 | M = self.naiveInterp2D(M,self.target_shape[0],self.target_shape[0]) 86 | [N, mean, sigma] = self.normalize(M) 87 | n = N.reshape(-1) 88 | if np.isnan(np.sum(n)): 89 | n = np.zeros(n.shape) 90 | return n 91 | 92 | def dataset_length(self): 93 | with open(self.datafile) as f: 94 | for i, l in enumerate(f): 95 | pass 96 | return i + 1 97 | 98 | @staticmethod 99 | def squareTrim(M, min_side=20, threshold=0): 100 | assert M.shape[0]==M.shape[1],"Input matrix must be a square" 101 | wsum = np.sum(M,axis=0) 102 | nonzero = np.where(wsum > threshold*M.shape[1])[0] 103 | if len(nonzero) >=1: 104 | wstart = nonzero[0] 105 | wend = nonzero[-1] 106 | else: 107 | wstart=0 ; wend = 0 108 | 109 | hsum = np.sum(M,axis=1) 110 | nonzero = np.where(hsum > threshold*M.shape[0])[0] 111 | if len(nonzero) >=1: 112 | hstart = nonzero[0] 113 | hend = nonzero[-1] 114 | else: 115 | hstart=0 ; hend = 0 116 | 117 | diff = abs((wend-wstart) - (hend-hstart)) 118 | if (wend-wstart > hend-hstart): 119 | side = max(wend-wstart+1, min_side) 120 | m = np.zeros((side, side)) 121 | cropped = M[hstart:hend+1,wstart:wend+1] 122 | shift = diff/2 123 | m[shift:cropped.shape[0]+shift,:cropped.shape[1]] = cropped 124 | else: 125 | side = max(hend-hstart+1, min_side) 126 | m = np.zeros((side, side)) 127 | cropped = M[hstart:hend+1,wstart:wend+1] 128 | shift=diff/2 129 | m[:cropped.shape[0],shift:cropped.shape[1]+shift] = cropped 130 | return m 131 | 132 | @staticmethod 133 | def rgb2gray(rgb): 134 | r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2] 135 | gray = 0.2989 * r + 0.5870 * g + 0.1140 * b 136 | return gray 137 | 138 | @staticmethod 139 | def naiveInterp2D(M, newx, newy): 140 | result = np.zeros((newx,newy)) 141 | for i in range(M.shape[0]): 142 | for j in range(M.shape[1]): 143 | indx = i*newx / M.shape[0] 144 | indy = j*newy / M.shape[1] 145 | result[indx,indy] +=M[i,j] 146 | return result 147 | 148 | @staticmethod 149 | def 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-------------------------------------------------------------------------------- /app/static/js/ocr_canvas.js: -------------------------------------------------------------------------------- 1 | var vocab = ["A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z","1","2","3","4","5","6","7","8","9",":)", ":(","triangle","5star","scribble"]; 2 | var index = -1; 3 | 4 | function clearDrawing() { 5 | var canvas = document.querySelector('#paint'); 6 | var ctx = canvas.getContext('2d'); 7 | ctx.clearRect(0, 0, canvas.width, canvas.height); 8 | } 9 | 10 | function submitDrawing() { 11 | var canvas = document.querySelector('#paint'); 12 | imgURI = canvas.toDataURL('image/jpeg', .5) 13 | 14 | // console.log("Submitting: " + imgStr); 15 | $.getJSON($SCRIPT_ROOT + '/_do_ocr', { 16 | imgURI: imgURI, 17 | index: index, 18 | vocab: JSON.stringify(vocab), 19 | 20 | }, function(data) { 21 | $('#result').text(data.result); 22 | $('input[name=a]').focus().select(); 23 | }); 24 | document.getElementById("result").innerHTML = "Working..."; 25 | return false; 26 | } 27 | 28 | //Here is the main code for the paint window 29 | (function() { 30 | 31 | var canvas = document.querySelector('#paint'); 32 | var ctx = canvas.getContext('2d'); 33 | 34 | var sketch = document.querySelector('#sketch'); 35 | var sketch_style = getComputedStyle(sketch); 36 | canvas.width = parseInt(sketch_style.getPropertyValue('width')); 37 | canvas.height = parseInt(sketch_style.getPropertyValue('height')); 38 | 39 | 40 | // Creating a tmp canvas 41 | var tmp_canvas = document.createElement('canvas'); 42 | var tmp_ctx = tmp_canvas.getContext('2d'); 43 | tmp_canvas.id = 'tmp_canvas'; 44 | tmp_canvas.width = canvas.width; 45 | tmp_canvas.height = canvas.height; 46 | 47 | sketch.appendChild(tmp_canvas); 48 | 49 | var mouse = {x: 0, y: 0}; 50 | var last_mouse = {x: 0, y: 0}; 51 | 52 | // Pencil Points 53 | var ppts = []; 54 | 55 | /* Mouse Capturing Work */ 56 | tmp_canvas.addEventListener('mousemove', function(e) { 57 | mouse.x = typeof e.offsetX !== 'undefined' ? e.offsetX : e.layerX; 58 | mouse.y = typeof e.offsetY !== 'undefined' ? e.offsetY : e.layerY; 59 | }, false); 60 | 61 | 62 | /* Drawing on Paint App */ 63 | tmp_ctx.lineWidth = 15; 64 | tmp_ctx.lineJoin = 'round'; 65 | tmp_ctx.lineCap = 'round'; 66 | tmp_ctx.strokeStyle = 'blue'; 67 | tmp_ctx.fillStyle = 'blue'; 68 | 69 | tmp_canvas.addEventListener('mousedown', function(e) { 70 | tmp_canvas.addEventListener('mousemove', onPaint, false); 71 | 72 | mouse.x = typeof e.offsetX !== 'undefined' ? e.offsetX : e.layerX; 73 | mouse.y = typeof e.offsetY !== 'undefined' ? e.offsetY : e.layerY; 74 | 75 | ppts.push({x: mouse.x, y: mouse.y}); 76 | 77 | onPaint(); 78 | }, false); 79 | 80 | tmp_canvas.addEventListener('mouseup', function() { 81 | tmp_canvas.removeEventListener('mousemove', onPaint, false); 82 | 83 | // Writing down to real canvas now 84 | ctx.drawImage(tmp_canvas, 0, 0); 85 | // Clearing tmp canvas 86 | tmp_ctx.clearRect(0, 0, tmp_canvas.width, tmp_canvas.height); 87 | 88 | // Emptying up Pencil Points 89 | ppts = []; 90 | }, false); 91 | 92 | var onPaint = function() { 93 | 94 | // Saving all the points in an array 95 | ppts.push({x: mouse.x, y: mouse.y}); 96 | 97 | if (ppts.length < 3) { 98 | var b = ppts[0]; 99 | tmp_ctx.beginPath(); 100 | //ctx.moveTo(b.x, b.y); 101 | //ctx.lineTo(b.x+50, b.y+50); 102 | tmp_ctx.arc(b.x, b.y, tmp_ctx.lineWidth / 2, 0, Math.PI * 2, !0); 103 | tmp_ctx.fill(); 104 | tmp_ctx.closePath(); 105 | 106 | return; 107 | } 108 | 109 | // Tmp canvas is always cleared up before drawing. 110 | tmp_ctx.clearRect(0, 0, tmp_canvas.width, tmp_canvas.height); 111 | 112 | tmp_ctx.beginPath(); 113 | tmp_ctx.moveTo(ppts[0].x, ppts[0].y); 114 | 115 | for (var i = 1; i < ppts.length - 2; i++) { 116 | var c = (ppts[i].x + ppts[i + 1].x) / 2; 117 | var d = (ppts[i].y + ppts[i + 1].y) / 2; 118 | 119 | tmp_ctx.quadraticCurveTo(ppts[i].x, ppts[i].y, c, d); 120 | } 121 | 122 | // For the last 2 points 123 | tmp_ctx.quadraticCurveTo( 124 | ppts[i].x, 125 | ppts[i].y, 126 | ppts[i + 1].x, 127 | ppts[i + 1].y 128 | ); 129 | tmp_ctx.stroke(); 130 | 131 | }; 132 | 133 | }()); -------------------------------------------------------------------------------- /app/static/style.css: -------------------------------------------------------------------------------- 1 | body { 2 | font-family: 'Roboto', sans-serif; 3 | font-weight: 300; 4 | padding: 0; 5 | margin: 0; 6 | font-size: 16px; 7 | } 8 | #header { 9 | margin-top: 20px; 10 | margin-bottom: 20px; 11 | } 12 | #teaser { 13 | height: 410px; 14 | padding-top: 20px; 15 | padding-bottom: 20px; 16 | padding-left: 3px; 17 | padding-right: 3px; 18 | background-color: #8C1515; 19 | position: relative; 20 | overflow: hidden; 21 | color: white; 22 | font-size: 16px; 23 | text-align: center; 24 | } 25 | h1 { 26 | font-size: 30px; 27 | font-weight: 300; 28 | text-align: center; 29 | } 30 | h2 { 31 | font-size: 25px; 32 | font-weight: 300; 33 | } 34 | .lhead { 35 | font-weight: normal; 36 | margin-top: 5px; 37 | } 38 | .maintext { 39 | width: 80%; 40 | margin: auto; 41 | } 42 | .litem { 43 | margin-top: 5px; 44 | } 45 | #footer { 46 | height: 250px; 47 | padding-top: 50px; 48 | text-align: center; 49 | position: relative; 50 | } 51 | .instructorphoto img { 52 | width: 100px; 53 | border-radius: 100px; 54 | margin-bottom: 10px; 55 | margin-top: 20px; 56 | } 57 | .instructor { 58 | display: inline-block; 59 | width: 120px; 60 | text-align: center; 61 | margin-right: 20px; 62 | } 63 | .sec { 64 | margin-bottom: 10px; 65 | padding-top: 10px; 66 | padding-bottom: 20px; 67 | width: 90%; 68 | } 69 | .tt { 70 | color: #060; 71 | display: inline-block; 72 | } 73 | 74 | #inputlayer { 75 | width: 65px; 76 | } 77 | .layer { 78 | width: 35px; 79 | display: inline-block; 80 | vertical-align: middle; 81 | margin-left: 10px; 82 | margin-bottom: 5px; 83 | } 84 | .actmap { 85 | display: block; 86 | border: 2px solid #FFF; 87 | } 88 | #convnetvis { 89 | text-align: center; 90 | } 91 | #probsdiv { 92 | 93 | } 94 | .pp { 95 | color: white; 96 | text-align: left; 97 | padding: 5px; 98 | height: 32px; 99 | margin: 2px; 100 | background-color: rgb(85,187,85); 101 | overflow: visible !important; 102 | } 103 | 104 | #convask { 105 | position: absolute; 106 | top: 360px; 107 | right: 16px; 108 | color: white; 109 | font-size: 25px; 110 | cursor: pointer; 111 | } 112 | 113 | #explain { 114 | display: none; 115 | background-color: #FF9; 116 | border-radius: 10px; 117 | margin: 10px; 118 | padding: 10px; 119 | } 120 | .qq { 121 | color: #060; 122 | } 123 | .qa { 124 | font-style: italic; 125 | } 126 | .qqa { 127 | margin-bottom: 20px; 128 | } 129 | .spanh { 130 | font-weight: normal; 131 | } 132 | .sechighlight { 133 | background-color: #f7f6f1; 134 | width: 90%; 135 | margin: auto; 136 | } 137 | #coursedesc { 138 | text-align: justify; 139 | } 140 | #footermsg { 141 | color: #333; 142 | margin: 20px; 143 | } 144 | #classicons { 145 | position: absolute; 146 | bottom: 20px; 147 | right: 20px; 148 | } 149 | .wimg { 150 | max-width: 100%; 151 | } 152 | .demoimg { 153 | max-width: 100%; 154 | margin: 0 auto; 155 | border-radius: 4px; 156 | margin-top: 20px; 157 | margin-bottom: 5px; 158 | } 159 | .rnndemo { 160 | font-size: 18px; 161 | text-align: center; 162 | } 163 | .demoimg2 { 164 | max-width: 100%; 165 | margin: 0 auto; 166 | } 167 | .demodiv2 { 168 | background-color: white; 169 | height: 380px; 170 | padding: 5px; 171 | border-radius: 4px; 172 | } 173 | .instructorphoto img { 174 | width: 120px; 175 | border-radius: 120px; 176 | margin-bottom: 10px; 177 | } 178 | .instructor { 179 | display: inline-block; 180 | width: 120px; 181 | text-align: center; 182 | margin-right: 20px; 183 | } 184 | #gallery { 185 | } 186 | .egimg { 187 | display: inline-block; 188 | vertical-align: top; 189 | } 190 | .egimg img { 191 | max-width: 370px; 192 | } 193 | .hh { 194 | color: #090; 195 | } -------------------------------------------------------------------------------- /app/templates/base.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Pythonic OCR 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 21 | 22 | 23 | 24 | 25 | 30 | 31 | 32 | 33 |
    34 |
    35 |
    36 | {% block content %}{% endblock %} 37 |
    38 |
    39 |
    40 | 41 | 42 | Fork me on GitHub 43 | 44 | -------------------------------------------------------------------------------- /app/templates/index.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | 3 | 4 | {% block content %} 5 | 26 | 27 |

    Pythonic OCR: Fun with Convolutional Neural Nets

    28 | 29 |
    30 | 31 |
    32 |
    33 |

    Introduction

    34 | 35 |
    36 | Convolutional neural networks attain state of the art performance in computer vision. They do this by analyzing the pixels in images in the same way as the human visual system. The network I built is made up of several simpler layers ‘stacked’ on top of each other: this means that it is a form of deep learning. I trained the network using Keras and then rewrote it in numpy so it could run as a demo. 37 |
    38 |
    39 |
    40 | 41 |
    42 |
    43 |

    Enter any alphanumeric character

    44 |
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    47 |
    48 | 49 | 50 |
    51 |

    52 |
    53 |
    54 | 55 |
    56 | 57 |

    Background

    58 |

    59 | To understand convolutional neural networks (CNNs), you must first understand convolution. Wikipedia gives a good mathematical definition here. 60 |

    61 |

    62 | To get a physical intuition of discrete 2D convolution (the kind I use in this project), imagine 63 |

      64 |
    1. an image
      2. 65 |
    2. a small patch of random pixels
      3. 66 |
    67 | When we convolve the two, we ‘smear’ the image’s pixels around using the patch. Different patch patterns will create different smears. Some of these smears will highlight particular features of the image such as horizontal lines or edges. 68 |

    69 |

    70 | In a convolutional neural network, each patch represents a different type of neuron in the human visual system. A convolutional neural net, in fact, is just like a regular neural net but each neuron acts on the entire image space (more and even more). These nets were invented in 1989 by Yann LeCun. 71 |

    72 |

    73 | In the past few years, computers and training methods have become powerful enough to enable anyone (even me) to build and train these networks; this has resulted in a massive resurgence of interest. I started with the Keras MNIST convolutional neural net example and made modifications to create this project. 74 | 75 |

    76 | 77 |

    How it works

    78 |

    79 | Let’s take a closer look. Convolution is defined as 80 | 81 | $$ (f * g )(t) = \int_{0}^{t} f(\tau)\, g(t - \tau)\, d\tau\ \mathrm{for} \ \ f, g : [0, \infty) \to \mathbb{R} $$ 82 | 83 | where \( (f * g )(t) \) represents the convolution of \(f\) on \(g\). The discrete analog is 84 | 85 | $$ (f * g)[n]\ \stackrel{\mathrm{def}}{=}\ \sum_{m=-\infty}^\infty f[m]\, g[n - m] $$ 86 | 87 | At first, I implemented convolution using a \(\mathrm{for}\) loop, but for many convolutions, this approach is too slow. Fortunately, convolution can be performed much more efficiently if you first take a fast fourier transform (fft). Python’s numpy library has a built-in fft function, so we can actually perform convolution in just two lines: 88 |

    89 | 
    freq_result = np.fft.fft2(image, target_dim) * np.fft.fft2(patch, target_dim)   #frequency domain 
 90 | spatial_result = np.fft.ifft2(fft_result).real   #go back to spatial domain
    91 | 92 |

    93 | This method is at least an order of magnitude faster for my project! 94 |

    95 | 96 |

    97 | The 2D convolution is only half of the picture. For every convolution layer, there is a pooling layer. The responsibility of the pooling layer is to combine different convolutions to create a 'image' of features that can be fed into the next convolution layer. I used max pooling for the demo. 98 |

    99 | 100 |

    101 | The first part of my model has two convolution layers and two pooling layers. These layers extract high-level image features. On top of these layers are two regular old dense neural networks (with dropout) to handle classification. I used Keras to build and train the entire model. 102 |

    103 |

    104 | Keras's backends compile everything in C++. I did not want my demo to do this, so I rewrote the network in numpy. While not especially scalable or modular, my reimplementation allows me to run my CNN as a demo on this page! 105 |

    106 |

    107 | A non-trivial part of machine learning is building a good database. I created my own miniature dataset of 3,000 samples. I resized, desaturated, cropped, and centered every input around 0 with stdev \(\sigma=1\). I made a separate preprocessor class to handle these processes. 108 |

    109 |

    110 | With a train=0.9 and valid+test=0.1 I was getting about 99% accuracy. There are a few extras: try drawing a 5-pointed star or a smiley face! Another of these extras is a category called ‘scribble.’ Basically, anything that did not fit well into the other categories is supposed to be classified as a scribble. 111 |

    112 | 113 |

    Discussion

    114 |

    115 | Convolutional neural nets are very powerful. This network performed well on a dataset without any hand-engineered features. Furthermore, the small ‘scribble detector’ innovation I added to the dataset worked quite well. 116 |

    117 |

    118 | One problem is that I used an existing deep architecture along with the preset hyperparameters. Choosing a model and setting correct hyperparameters is half the challenge in deep learning, so in the future this is something I will focus on. 119 |

    120 |
    121 | 122 |
    123 | 126 |
    127 | 128 | 129 | 130 | {% endblock %} -------------------------------------------------------------------------------- /app/views.py: -------------------------------------------------------------------------------- 1 | from flask import render_template, flash, redirect, request, jsonify 2 | from app import app 3 | from app.model.preprocessor import Preprocessor as img_prep 4 | from app.model.alpha_cnn_predict import LiteOCR 5 | import json 6 | import sys 7 | 8 | @app.route('/', methods=['GET', 'POST']) 9 | @app.route('/index', methods=['GET', 'POST']) 10 | def index(): 11 | app.logger.debug("Went to OCR page") 12 | return render_template('index.html', title='Optical Character Recognition', prediction=None) 13 | 14 | 15 | @app.route('/_do_ocr', methods=['GET', 'POST']) 16 | def do_ocr(): 17 | """Add two numbers server side, ridiculous but well...""" 18 | app.logger.debug("Accessed _do_ocr page with image data") 19 | # flash('Just hit the _add_numbers function') 20 | # a = json.loads(request.args.get('a', 0, type=str)) 21 | data = request.args.get('imgURI', 0, type=str) 22 | app.logger.debug("Data looks like " + data) 23 | index = request.args.get('index', 0, type=int) 24 | vocab = json.loads(request.args.get('vocab',0,type=str)) 25 | 26 | pp = img_prep(fn="dataset.txt") 27 | ocr = LiteOCR(fn="app/model/alpha_weights.pkl") 28 | char_prediction= ocr.predict(pp.preprocess(data)) 29 | 30 | result = "You entered a: " + char_prediction 31 | 32 | app.logger.debug("Recognized a character") 33 | return jsonify(result=result) 34 | -------------------------------------------------------------------------------- /app/views.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/app/views.pyc -------------------------------------------------------------------------------- /config.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/config.pyc -------------------------------------------------------------------------------- /convolutional_network_tutorial.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Convolutional Network \n", 8 | "\n", 9 | "## Demo\n", 10 | "\n", 11 | "We're going to build a Convolutional Neural Network using just numpy capable of detecting any alphanumeric character that a user draws. It will all be wrapped into a Flask web app so you can play with it in the browser!\n", 12 | "\n", 13 | "![alt text](http://i.imgur.com/8ysCoB5.png \"Logo Title Text 1\")\n", 14 | "\n", 15 | "## What inspired Convolutional Networks?\n", 16 | "\n", 17 | "CNNs are biologically-inspired models inspired by research by D. H. Hubel and T. N. Wiesel. They proposed an explanation for the way in which mammals visually perceive the world around them using a layered architecture of neurons in the brain, and this in turn inspired engineers to attempt to develop similar pattern recognition mechanisms in computer vision.\n", 18 | "\n", 19 | "![alt text](https://qph.ec.quoracdn.net/main-qimg-235acb60a481423eaf70c39b17bc914b.webp \"Logo Title Text 1\")\n", 20 | "\n", 21 | "\n", 22 | "In their hypothesis, within the visual cortex, complex functional responses generated by \"complex cells\" are constructed from more simplistic responses from \"simple cells'. \n", 23 | "\n", 24 | "For instances, simple cells would respond to oriented edges etc, while complex cells will also respond to oriented edges but with a degree of spatial invariance.\n", 25 | "\n", 26 | "Receptive fields exist for cells, where a cell responds to a summation of inputs from other local cells.\n", 27 | "\n", 28 | "The architecture of deep convolutional neural networks was inspired by the ideas mentioned above \n", 29 | "- local connections \n", 30 | "- layering \n", 31 | "- spatial invariance (shifting the input signal results in an equally shifted output signal. , most of us are able to recognize specific faces under a variety of conditions because we learn abstraction These abstractions are thus invariant to size, contrast, rotation, orientation\n", 32 | " \n", 33 | "However, it remains to be seen if these computational mechanisms of convolutional neural networks are similar to the computation mechanisms occurring in the primate visual system\n", 34 | "\n", 35 | "- convolution operation\n", 36 | "- shared weights\n", 37 | "- pooling/subsampling \n", 38 | "\n", 39 | "## How does it work? \n", 40 | "\n", 41 | "![alt text](https://images.nature.com/w926/nature-assets/srep/2016/160610/srep27755/images_hires/srep27755-f1.jpg \"Logo Title Text 1\")\n", 42 | "![alt text](https://www.mathworks.com/content/mathworks/www/en/discovery/convolutional-neural-network/jcr:content/mainParsys/image_copy.adapt.full.high.jpg/1497876372993.jpg \"Logo Title Text 1\")\n", 43 | "\n", 44 | "### Step 1 - Prepare a dataset of images\n", 45 | "\n", 46 | "![alt text](http://xrds.acm.org/blog/wp-content/uploads/2016/06/Figure1.png \"Logo Title Text 1\")\n", 47 | "\n", 48 | "- Every image is a matrix of pixel values. \n", 49 | "- The range of values that can be encoded in each pixel depends upon its bit size. \n", 50 | "- Most commonly, we have 8 bit or 1 Byte-sized pixels. Thus the possible range of values a single pixel can represent is [0, 255]. \n", 51 | "- However, with coloured images, particularly RGB (Red, Green, Blue)-based images, the presence of separate colour channels (3 in the case of RGB images) introduces an additional ‘depth’ field to the data, making the input 3-dimensional. \n", 52 | "- Hence, for a given RGB image of size, say 255×255 (Width x Height) pixels, we’ll have 3 matrices associated with each image, one for each of the colour channels. \n", 53 | "- Thus the image in it’s entirety, constitutes a 3-dimensional structure called the Input Volume (255x255x3).\n", 54 | "\n", 55 | "Great training datasets are [CIFAR](https://www.cs.toronto.edu/~kriz/cifar.html) and [CoCo](http://mscoco.org/). We'll use CIFAR.\n", 56 | "\n", 57 | "### Step 2 - Convolution \n", 58 | "\n", 59 | "![alt text](https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/more_images/Convolution_schematic.gif \"Logo Title Text 1\")\n", 60 | "\n", 61 | "![alt text](http://xrds.acm.org/blog/wp-content/uploads/2016/06/Figure_2.png \"Logo Title Text 1\")\n", 62 | "\n", 63 | "- A convolution is an orderly procedure where two sources of information are intertwined.\n", 64 | "\n", 65 | "- A kernel (also called a filter) is a smaller-sized matrix in comparison to the input dimensions of the image, that consists of real valued entries.\n", 66 | "\n", 67 | "- Kernels are then convolved with the input volume to obtain so-called ‘activation maps’ (also called feature maps). \n", 68 | "- Activation maps indicate ‘activated’ regions, i.e. regions where features specific to the kernel have been detected in the input. \n", 69 | "\n", 70 | "- The real values of the kernel matrix change with each learning iteration over the training set, indicating that the network is learning to identify which regions are of significance for extracting features from the data.\n", 71 | "\n", 72 | "- We compute the dot product between the kernel and the input matrix. -The convolved value obtained by summing the resultant terms from the dot product forms a single entry in the activation matrix. \n", 73 | "\n", 74 | "- The patch selection is then slided (towards the right, or downwards when the boundary of the matrix is reached) by a certain amount called the ‘stride’ value, and the process is repeated till the entire input image has been processed. - The process is carried out for all colour channels.\n", 75 | "\n", 76 | "- instead of connecting each neuron to all possible pixels, we specify a 2 dimensional region called the ‘receptive field[14]’ (say of size 5×5 units) extending to the entire depth of the input (5x5x3 for a 3 colour channel input), within which the encompassed pixels are fully connected to the neural network’s input layer. It’s over these small regions that the network layer cross-sections (each consisting of several neurons (called ‘depth columns’)) operate and produce the activation map. (reduces computational complexity)\n", 77 | "\n", 78 | "![alt text](http://i.imgur.com/g4hRI6Z.png \"Logo Title Text 1\")\n", 79 | "![alt text](http://i.imgur.com/tpQvMps.jpg \"Logo Title Text 1\")\n", 80 | "![alt text](http://i.imgur.com/oyXkhHi.jpg \"Logo Title Text 1\")\n", 81 | "![alt text](http://xrds.acm.org/blog/wp-content/uploads/2016/06/Figure_5.png \"Logo Title Text 1\")\n", 82 | "\n", 83 | "Great resource on description of convolution (discrete vs continous) & the fourier transform\n", 84 | "\n", 85 | "http://timdettmers.com/2015/03/26/convolution-deep-learning/\n", 86 | "\n", 87 | "\n", 88 | "### Step 3 - Pooling\n", 89 | "![alt text](http://xrds.acm.org/blog/wp-content/uploads/2016/06/Figure_6.png \"Logo Title Text 1\")\n", 90 | "\n", 91 | "- Pooling reducing the spatial dimensions (Width x Height) of the Input Volume for the next Convolutional Layer. It does not affect the depth dimension of the Volume. \n", 92 | "- The transformation is either performed by taking the maximum value from the values observable in the window (called ‘max pooling’), or by taking the average of the values. Max pooling has been favoured over others due to its better performance characteristics.\n", 93 | "- also called downsampling\n", 94 | "\n", 95 | "### Step 4 - Normalization (ReLU in our case)\n", 96 | "\n", 97 | "![alt text](http://xrds.acm.org/blog/wp-content/uploads/2016/06/CodeCogsEqn-3.png \"Logo Title Text 1\")\n", 98 | "\n", 99 | "Normalization (keep the math from breaking by turning all negative numbers to 0) (RELU) a stack of images becomes a stack of images with no negative values. \n", 100 | "\n", 101 | "Repeat Steps 2-4 several times. More, smaller images (feature maps created at every layer)\n", 102 | "\n", 103 | "### Step 5 - Regularization \n", 104 | "\n", 105 | "- Dropout forces an artificial neural network to learn multiple independent representations of the same data by alternately randomly disabling neurons in the learning phase.\n", 106 | "- Dropout is a vital feature in almost every state-of-the-art neural network implementation.\n", 107 | "- To perform dropout on a layer, you randomly set some of the layer's values to 0 during forward propagation.\n", 108 | "\n", 109 | "See [this](http://iamtrask.github.io/2015/07/28/dropout/)\n", 110 | "\n", 111 | "![alt text](https://i.stack.imgur.com/CewjH.png \"Logo Title Text 1\")\n", 112 | "\n", 113 | "### Step 6 - Probability Conversion\n", 114 | "\n", 115 | "At the very end of our network (the tail), we'll apply a softmax function to convert the outputs to probability values for each class. \n", 116 | "\n", 117 | "![alt text](https://1.bp.blogspot.com/-FHDU505euic/Vs1iJjXHG0I/AAAAAAABVKg/x4g0FHuz7_A/s1600/softmax.JPG \"Logo Title Text 1\")\n", 118 | "\n", 119 | "\n", 120 | "### Step 7 - Choose most likely label (max probability value) \n", 121 | "\n", 122 | "argmax(softmax_outputs)\n", 123 | "\n", 124 | "These 7 steps are one forward pass through the network.\n", 125 | "\n", 126 | "## So how do we learn the magic numbers? \n", 127 | "\n", 128 | "- We can learn features and weight values through backpropagation\n", 129 | "\n", 130 | "![alt text](http://www.robots.ox.ac.uk/~vgg/practicals/cnn/images/cover.png \"Logo Title Text 1\")\n", 131 | "\n", 132 | "![alt text](https://image.slidesharecdn.com/cnn-toupload-final-151117124948-lva1-app6892/95/convolutional-neural-networks-cnn-52-638.jpg?cb=1455889178 \"Logo Title Text 1\")\n", 133 | "\n", 134 | "The other hyperparameters are set by humans and they are an active field of research (finding the optimal ones)\n", 135 | "\n", 136 | "i.e - number of neurons, number of features, size of features, poooling window size, window stride\n", 137 | "\n", 138 | "\n", 139 | "## When is a good time to use it?\n", 140 | "\n", 141 | "- To classify images\n", 142 | "- To generate images (more on that later..)\n", 143 | "\n", 144 | "![alt text](https://nlml.github.io/images/convnet_diagram.png \"Logo Title Text 1\")\n", 145 | "\n", 146 | "But can also be applied to any any spatial 2D or 3D data. Images. Even sound and text. A rule of thumb is if you data is just as useful if you swap out the rows and columns, like customer data, then you can't use a CNN.\n", 147 | "\n", 148 | "\n", 149 | "## Good examples\n", 150 | "\n", 151 | "Robot learns to grasp (combining CNNs)\n", 152 | "\n", 153 | "![alt text](https://img.newatlas.com/youtube-robot-6.jpg?auto=format%2Ccompress&fit=max&h=670&q=60&w=1000&s=d003e42afa7e462fd711c6a99f21b51f \"Logo Title Text 1\")\n", 154 | "\n", 155 | "Tensorflow! https://github.com/upul/CarND-TensorFlow-Lab\n", 156 | "\n", 157 | "Adversarial CNNs https://github.com/michbad/adversarial-mnist\n" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "metadata": { 164 | "collapsed": true 165 | }, 166 | "outputs": [], 167 | "source": [ 168 | "import pickle #saving and loading our serialized model \n", 169 | "import numpy as np #matrix math\n", 170 | "from app.model.preprocessor import Preprocessor as img_prep #image preprocessing\n", 171 | "\n", 172 | "#class for loading our saved model and classifying new images\n", 173 | "class LiteOCR:\n", 174 | " \n", 175 | "\tdef __init__(self, fn=\"alpha_weights.pkl\", pool_size=2):\n", 176 | " #load the weights from the pickle file and the meta data\n", 177 | "\t\t[weights, meta] = pickle.load(open(fn, 'rb'), encoding='latin1') #currently, this class MUST be initialized from a pickle file\n", 178 | "\t\t#list to store labels\n", 179 | " self.vocab = meta[\"vocab\"]\n", 180 | " \n", 181 | " #how many rows and columns in an image\n", 182 | "\t\tself.img_rows = meta[\"img_side\"] ; self.img_cols = meta[\"img_side\"]\n", 183 | " \n", 184 | " #load our CNN\n", 185 | "\t\tself.CNN = LiteCNN()\n", 186 | " #with our saved weights\n", 187 | "\t\tself.CNN.load_weights(weights)\n", 188 | " #define the pooling layers size\n", 189 | "\t\tself.CNN.pool_size=int(pool_size)\n", 190 | " \n", 191 | " #classify new image\n", 192 | "\tdef predict(self, image):\n", 193 | "\t\tprint(image.shape)\n", 194 | " #vectorize the image into the right shape for our network\n", 195 | "\t\tX = np.reshape(image, (1, 1, self.img_rows, self.img_cols))\n", 196 | "\t\tX = X.astype(\"float32\")\n", 197 | " \n", 198 | " #make the prediction\n", 199 | "\t\tpredicted_i = self.CNN.predict(X)\n", 200 | " #return the predicted label\n", 201 | "\t\treturn self.vocab[predicted_i]\n", 202 | "\n", 203 | "class LiteCNN:\n", 204 | "\tdef __init__(self):\n", 205 | " # a place to store the layers\n", 206 | "\t\tself.layers = [] \n", 207 | " # size of pooling area for max pooling\n", 208 | "\t\tself.pool_size = None \n", 209 | "\n", 210 | "\tdef load_weights(self, weights):\n", 211 | "\t\tassert not self.layers, \"Weights can only be loaded once!\"\n", 212 | " #add the saved matrix values to the convolutional network\n", 213 | "\t\tfor k in range(len(weights.keys())):\n", 214 | "\t\t\tself.layers.append(weights['layer_{}'.format(k)])\n", 215 | "\n", 216 | "\tdef predict(self, X): \n", 217 | " #here is where the network magic happens at a high level\n", 218 | " h = self.cnn_layer(X, layer_i=0, border_mode=\"full\"); X= h\n", 219 | " h = self.relu_layer(X); X = h;\n", 220 | " h = self.cnn_layer(X, layer_i=2, border_mode=\"valid\"); X = h\n", 221 | " h = self.relu_layer(X); X = h;\n", 222 | " h = self.maxpooling_layer(X); X = h\n", 223 | " h = self.dropout_layer(X, .25); X = h\n", 224 | " h = self.flatten_layer(X, layer_i=7); X = h;\n", 225 | " h = self.dense_layer(X, fully, layer_i=10); x = H\n", 226 | " h = self.softmax_layer2D(X); x = h\n", 227 | " max_i = self.classify(X)\n", 228 | " return max_i[0]\n", 229 | " \n", 230 | " #given our feature map we've learned from convolving around the image\n", 231 | " #lets make it more dense by performing pooling, specifically max pooling\n", 232 | " #we'll select the max values from the image matrix and use that as our new feature map\n", 233 | "\tdef maxpooling_layer(self, convolved_features):\n", 234 | " #given our learned features and images\n", 235 | "\t\tnb_features = convolved_features.shape[0]\n", 236 | "\t\tnb_images = convolved_features.shape[1]\n", 237 | "\t\tconv_dim = convolved_features.shape[2]\n", 238 | "\t\tres_dim = int(conv_dim / self.pool_size) #assumed square shape\n", 239 | "\n", 240 | " #initialize our more dense feature list as empty\n", 241 | "\t\tpooled_features = np.zeros((nb_features, nb_images, res_dim, res_dim))\n", 242 | " #for each image\n", 243 | "\t\tfor image_i in range(nb_images):\n", 244 | " #and each feature map\n", 245 | "\t\t\tfor feature_i in range(nb_features):\n", 246 | " #begin by the row\n", 247 | "\t\t\t\tfor pool_row in range(res_dim):\n", 248 | " #define start and end points\n", 249 | "\t\t\t\t\trow_start = pool_row * self.pool_size\n", 250 | "\t\t\t\t\trow_end = row_start + self.pool_size\n", 251 | "\n", 252 | " #for each column (so its a 2D iteration)\n", 253 | "\t\t\t\t\tfor pool_col in range(res_dim):\n", 254 | " #define start and end points\n", 255 | "\t\t\t\t\t\tcol_start = pool_col * self.pool_size\n", 256 | "\t\t\t\t\t\tcol_end = col_start + self.pool_size\n", 257 | " \n", 258 | " #define a patch given our defined starting ending points\n", 259 | "\t\t\t\t\t\tpatch = convolved_features[feature_i, image_i, row_start : row_end,col_start : col_end]\n", 260 | " #then take the max value from that patch\n", 261 | " #store it. this is our new learned feature/filter\n", 262 | "\t\t\t\t\t\tpooled_features[feature_i, image_i, pool_row, pool_col] = np.max(patch)\n", 263 | "\t\treturn pooled_features\n", 264 | "\n", 265 | " #convolution is the most important of the matrix operations here\n", 266 | " #well define our input, lauyer number, and a border mode (explained below)\n", 267 | "\tdef cnn_layer(self, X, layer_i=0, border_mode = \"full\"):\n", 268 | " #we'll store our feature maps and bias value in these 2 vars\n", 269 | "\t\tfeatures = self.layers[layer_i][\"param_0\"]\n", 270 | "\t\tbias = self.layers[layer_i][\"param_1\"]\n", 271 | " #how big is our filter/patch?\n", 272 | "\t\tpatch_dim = features[0].shape[-1]\n", 273 | " #how many features do we have?\n", 274 | "\t\tnb_features = features.shape[0]\n", 275 | " #How big is our image?\n", 276 | "\t\timage_dim = X.shape[2] #assume image square\n", 277 | " #R G B values\n", 278 | "\t\timage_channels = X.shape[1]\n", 279 | " #how many images do we have?\n", 280 | "\t\tnb_images = X.shape[0]\n", 281 | " \n", 282 | " #With border mode \"full\" you get an output that is the \"full\" size as the input. \n", 283 | " #That means that the filter has to go outside the bounds of the input by \"filter size / 2\" - \n", 284 | " #the area outside of the input is normally padded with zeros.\n", 285 | "\t\tif border_mode == \"full\":\n", 286 | "\t\t\tconv_dim = image_dim + patch_dim - 1\n", 287 | " #With border mode \"valid\" you get an output that is smaller than the input because \n", 288 | " #the convolution is only computed where the input and the filter fully overlap.\n", 289 | "\t\telif border_mode == \"valid\":\n", 290 | "\t\t\tconv_dim = image_dim - patch_dim + 1\n", 291 | " \n", 292 | " #we'll initialize our feature matrix\n", 293 | "\t\tconvolved_features = np.zeros((nb_images, nb_features, conv_dim, conv_dim));\n", 294 | " #then we'll iterate through each image that we have\n", 295 | "\t\tfor image_i in range(nb_images):\n", 296 | " #for each feature \n", 297 | "\t\t\tfor feature_i in range(nb_features):\n", 298 | " #lets initialize a convolved image as empty\n", 299 | "\t\t\t\tconvolved_image = np.zeros((conv_dim, conv_dim))\n", 300 | " #then for each channel (r g b )\n", 301 | "\t\t\t\tfor channel in range(image_channels):\n", 302 | " #lets extract a feature from our feature map\n", 303 | "\t\t\t\t\tfeature = features[feature_i, channel, :, :]\n", 304 | " #then define a channel specific part of our image\n", 305 | "\t\t\t\t\timage = X[image_i, channel, :, :]\n", 306 | " #perform convolution on our image, using a given feature filter\n", 307 | "\t\t\t\t\tconvolved_image += self.convolve2d(image, feature, border_mode);\n", 308 | "\n", 309 | " #add a bias to our convoved image\n", 310 | "\t\t\t\tconvolved_image = convolved_image + bias[feature_i]\n", 311 | " #add it to our list of convolved features (learnings)\n", 312 | "\t\t\t\tconvolved_features[image_i, feature_i, :, :] = convolved_image\n", 313 | "\t\treturn convolved_features\n", 314 | "\n", 315 | " #In a dense layer, every node in the layer is connected to every node in the preceding layer.\n", 316 | "\tdef dense_layer(self, X, layer_i=0):\n", 317 | " #so we'll initialize our weight and bias for this layer\n", 318 | "\t\tW = self.layers[layer_i][\"param_0\"]\n", 319 | "\t\tb = self.layers[layer_i][\"param_1\"]\n", 320 | " #and multiply it by our input (dot product)\n", 321 | "\t\toutput = np.dot(X, W) + b\n", 322 | "\t\treturn output\n", 323 | "\n", 324 | "\t@staticmethod\n", 325 | " \n", 326 | " #so what does the convolution operation look like?, given an image and a feature map (filter)\n", 327 | "\tdef convolve2d(image, feature, border_mode=\"full\"):\n", 328 | " #we'll define the tensor dimensions of the image and the feature\n", 329 | "\t\timage_dim = np.array(image.shape)\n", 330 | "\t\tfeature_dim = np.array(feature.shape)\n", 331 | " #as well as a target dimension\n", 332 | "\t\ttarget_dim = image_dim + feature_dim - 1\n", 333 | " #then we'll perform a fast fourier transform on both the input and the filter\n", 334 | " #performing a convolution can be written as a for loop but for many convolutions\n", 335 | " #this approach is too comp. expensive/slow. it can be performed orders of magnitude\n", 336 | " #faster using a fast fourier transform. \n", 337 | "\t\tfft_result = np.fft.fft2(image, target_dim) * np.fft.fft2(feature, target_dim)\n", 338 | " #and set the result to our target \n", 339 | "\t\ttarget = np.fft.ifft2(fft_result).real\n", 340 | "\n", 341 | "\t\tif border_mode == \"valid\":\n", 342 | "\t\t\t# To compute a valid shape, either np.all(x_shape >= y_shape) or\n", 343 | "\t\t\t# np.all(y_shape >= x_shape).\n", 344 | " #decide a target dimension to convolve around\n", 345 | "\t\t\tvalid_dim = image_dim - feature_dim + 1\n", 346 | "\t\t\tif np.any(valid_dim < 1):\n", 347 | "\t\t\t\tvalid_dim = feature_dim - image_dim + 1\n", 348 | "\t\t\tstart_i = (target_dim - valid_dim) // 2\n", 349 | "\t\t\tend_i = start_i + valid_dim\n", 350 | "\t\t\ttarget = target[start_i[0]:end_i[0], start_i[1]:end_i[1]]\n", 351 | "\t\treturn target\n", 352 | "\n", 353 | "\tdef relu_layer(x):\n", 354 | " #turn all negative values in a matrix into zeros\n", 355 | "\t\tz = np.zeros_like(x)\n", 356 | "\t\treturn np.where(x>z,x,z)\n", 357 | "\n", 358 | "\tdef softmax_layer2D(w):\n", 359 | " #this function will calculate the probabilities of each\n", 360 | " #target class over all possible target classes. \n", 361 | "\t\tmaxes = np.amax(w, axis=1)\n", 362 | "\t\tmaxes = maxes.reshape(maxes.shape[0], 1)\n", 363 | "\t\te = np.exp(w - maxes)\n", 364 | "\t\tdist = e / np.sum(e, axis=1, keepdims=True)\n", 365 | "\t\treturn dist\n", 366 | "\n", 367 | " #affect the probability a node will be turned off by multiplying it\n", 368 | " #by a p values (.25 we define)\n", 369 | "\tdef dropout_layer(X, p):\n", 370 | "\t\tretain_prob = 1. - p\n", 371 | "\t\tX *= retain_prob\n", 372 | "\t\treturn X\n", 373 | "\n", 374 | " #get the largest probabililty value from the list\n", 375 | "\tdef classify(X):\n", 376 | "\t\treturn X.argmax(axis=-1)\n", 377 | "\n", 378 | " #tensor transformation, less dimensions\n", 379 | "\tdef flatten_layer(X):\n", 380 | "\t\tflatX = np.zeros((X.shape[0],np.prod(X.shape[1:])))\n", 381 | "\t\tfor i in range(X.shape[0]):\n", 382 | "\t\t\tflatX[i,:] = X[i].flatten(order='C')\n", 383 | "\t\treturn flatX" 384 | ] 385 | } 386 | ], 387 | "metadata": { 388 | "kernelspec": { 389 | "display_name": "Python 2", 390 | "language": "python", 391 | "name": "python2" 392 | }, 393 | "language_info": { 394 | "codemirror_mode": { 395 | "name": "ipython", 396 | "version": 2 397 | }, 398 | "file_extension": ".py", 399 | "mimetype": "text/x-python", 400 | "name": "python", 401 | "nbconvert_exporter": "python", 402 | "pygments_lexer": "ipython2", 403 | "version": "2.7.12" 404 | } 405 | }, 406 | "nbformat": 4, 407 | "nbformat_minor": 2 408 | } 409 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | Babel==2.1.1 2 | Flask==0.10.1 3 | Flask-Babel==0.9 4 | Flask-WTF==0.12 5 | Jinja2==2.8 6 | MarkupSafe==0.23 7 | Pillow==3.0.0 8 | WTForms==2.0.2 9 | Werkzeug==0.10.4 10 | coverage==4.0.1 11 | flipflop==1.0 12 | gunicorn==19.4.3 13 | itsdangerous==0.24 14 | numpy==1.10.1 15 | pytz==2015.6 16 | speaklater==1.3 17 | -------------------------------------------------------------------------------- /run.py: -------------------------------------------------------------------------------- 1 | #!flask/bin/python 2 | from app import app 3 | app.run(debug=True) -------------------------------------------------------------------------------- /runtime.txt: -------------------------------------------------------------------------------- 1 | python-3.5.1 -------------------------------------------------------------------------------- /temp.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/Convolutional_neural_network/28fe10899301fc3cb18b1e629e7fe57780bcaca4/temp.jpg --------------------------------------------------------------------------------