updated to third week

SecondSaturday/Mnist10Digit.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

SecondSaturday/Notes.md

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+1. flowchart application -> ios mobile find symbol through gestures.
+2. matrimony portal profiles -> finding matching profiles
+3. swipe input keyboard for indian languages.
+4. mnist hand-written digit database -> build application for recognizing full phone numbers(10 digit).
+5. live cricket feed -> generating highlights of the match.
+6. designing a chatbot for getting cricket scores.
+
+
+# general approach to machine-learning
+1. model, objective and learning algo
+3. ml is technique for learning from examples
+    -> recommending smart-phone to a friend. price, branch, cam quality, screen size, processing speed. ==> model
+    -> objective ( why is he buying the phone) should be able to be boiled-down to a single number.
+    -> learning algo
+    binary features -> camera 0|1
+                    -> screen small|large
+                    -> battery good|bad
+                    -> memory high|low
+                    -> processing fast|slow
+                    -> audio good|bad
+
+prior probability -> probability of an event occuring without any knowledge of conditions
+
+P(A)*P(B|A) = P(B)*P(A/B) = P(A,B)
+
+P(+ve|[x1,x2,x3...xn]) = P([x1,x2...xn]|+ve)*P(+ve)/P([x1,x2...xn])
+                       = P(x1|+ve)*P(x2|+ve)*P(x3|+ve)*P(+ve)/P([x1,x2...xn])
+                       = Pi (i=1 to n) P(xi|+ve) * P(+ve)(Class variable)/ Sum (C=+ve to -ve) (P([x1,x2....xn],C))
+                       = Pi (i=1 to n) P(xi|+ve) * P(+ve)(Class variable) / (( Pi (i=1 to n ) P(xi|+ve)*P(+ve))+..+ Pi (i=1 to n) P(xi|-ve)*P(-ve))
+
+P(X,Y) = Sum (y=y1...yn) P(X,Y=y)
+
+W2 = P(+ve|xi=1) (human approach) (Naive bayes) (naively thinking that all features are independent)
+
+
+Regression : output contains real values -> (predicting the position of joints of a body given an image of a person)
+Classification : output classifies to discrete set of classes
+      -> predicting the posture of a person (sitting,walking,standing,running) given an image of a person
+(Numerical/Categorical)
+
+Representation Learning: embedding
+
+Deeplearning is all about hierarchical representation learning.
+
+Metric Learning: distance( of facial features)/similarity(of fashion apparels)/relevance( of search document)
+Structured Output(Models): auto-corrects dependent outputs based on output on the upper hierarchy outputs.
+
+Types of input:
+Bag of features,bag of words: ( finding whether a feature is present/not without caring where the feature occurs in the input)
+eg: Using unsupervized learning to convert the input to a given set of classes(clusters) and use them as bag of features.
+
+Spatial data(sequential data): if there local dependencies use CNN(convolutional nn) if there are near past dependencies in the data use RNN(Recurrent NN-LSTM)
+eg: stock market temporal data / speech data/ image data
+
+Non-Parametric models : k-NN(K-nearest neighbor), Decision Trees, Random Forests (independent of parameters)
+  -> very inaccurate because doesn't know much about the data
+Parametric Models: based on fixed set of parameters,SVM
+  -> more accurate coz the knows more about the parameters from the data
+
+
+Types of Learning:
+  supervized learning -> labeled data
+  unsupervized learning -> unlabeled data
+    exercise: *take 3s from mnist data *create a gmm model with them and *cluster them with 5/3/10 gaussians.
+              *take all images and cluster them to 10 gaussians.
+  semi-supervized learning -> combination of supervized and unsupervized models
+
+  Auto Encoder:
+    finding a low dimensional representation of a high dimensional data.
+    eg. image of 200x200 pixels create a fingerprint of image of 128 dimensions.
+       exercise: use the 128 dimensional data to reconstruct the 200x200 image(using inverse of the model).
+
+  Reinforcement learning:
+    eg:playing chess -> using the final result of the game to assign weights/score for moves that were made upto the final result. and training the model to predict based on those scores.

SecondSaturday/Workshop.md

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+# swipe input keyboard for indian languages.
+given a gesture made on the keybard,language chosen-> keyboard layout
+predict the word that matches closest.
+
+
+## Input
+gesture data -> polygon(shape,size,corners, path), (time, pauses)?, spatial data with word character correlation.
+weighted-vocabulary,corpus for the language,history of gesture-word mappings/corrections for the user.
+language, keyboard layout
+
+## Output
+  Predict the word
+
+## Model
+  Structured Output/HMM/CNN?
+
+# mnist hand-written digit database -> build application for recognizing full phone numbers(10 digit).
+
+## Input
+mnist digit database, generated 10 digit images with random positioning,orientation,scale of individual digit images sampled randomly from the mnist database.
+
+## Output
+  predict the phone number
+
+## Model
+  regression model to identify the points where the split for the images has to be made and pass the
+  split images to mnist digit recognizer to identify the digit.

SecondSunday/Notes.md

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+# Activation Function (AKA Transfer function)
+ In a neural network activation function adds non-linearity to it.
+ Types:
+ 1. Sigmoid(Logistic) (used mostly for output layer(looks like probability))
+ 2. RelU or Rectified Linear Unit (important discovery for NN - most-used for hidden layers)(not suitable for output layer if output is supposed to be probability) and leaky RelU with some slope on negative part
+ 3. tanH (Hyperbolic) (-1 - 1) or ArcTan (Tan Inverse -> maps to -Pi/2 - Pi/2)
+ 4. Linear(or Identity) layer (used for output layers(best for regression))
+ 5. Softmax (classification giving probability) (probability coz outputs add upto 1)
+ 6. SquareRoot
+ 7. Exponential
+ 8. Sine.
+ 9. Ramp
+ 10. Step (Binary)
+ 11. Unit Sum
+
+if the network computation is something that is multiplicative, use log as activation so that the sum becomes addition.
+
+Constraint Optimization: optimize in such a way that the output is constrained to some value.
+
+
+Steps => number of iteration of batches
+Epoch => number of iterations of going throught the entire dataset

SecondSunday/mnist_phone_number.py

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+%matplotlib inline
+from tensorflow.examples.tutorials.mnist import input_data
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+
+mnist = input_data.read_data_sets('./SecondSunday/mnist_data', one_hot=True)
+label_number = mnist.train.labels.argmax(axis=1)
+number_imgs = {str(i):mnist.train.images[np.argwhere(label_number == i).squeeze()] for i in range(10)}
+DATA_COUNT = 10240
+# phone_number_digits = np.random.randint(10**9,10**10,size=(DATA_COUNT,10))
+phone_number_digits = np.random.randint(10,size=(DATA_COUNT,10))
+phone_number_digits.astype(str)
+phone_number_strings = pd.DataFrame(phone_number_digits.astype(str).T).apply(lambda x: ''.join(x)).values
+
+def pick_img(num):
+    rand_idx = np.random.randint(number_imgs[num].shape[0])
+    img = number_imgs[num][rand_idx].reshape(28,28)
+    return img
+
+def create_phone_img(phon_no):
+    return np.hstack(tuple([pick_img(d) for d in phon_no]))
+
+def create_phone_images(phone_array):
+    phone_number_images = []
+    for phon_no in phone_array:
+        phone_number_images.append(create_phone_img(phon_no))
+    return np.array(phone_number_images).reshape(-1,28*280)
+
+phone_number_imgs = create_phone_images(phone_number_strings)
+train_imgs,test_imgs,train_digits,test_digits = train_test_split(phone_number_imgs,phone_number_digits)
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation
+
+# model = Sequential([
+#     Dense(32, input_shape=(7840,)),
+#     Activation('relu'),
+#     # Dense(24, input_shape=(32,)),
+#     # Activation('relu'),
+#     Dense(10),
+#     Activation('linear'),
+# ])
+
+# model.compile(optimizer='sgd',
+#               loss='mean_squared_error',
+#               metrics=['accuracy'])
+#
+# model.fit(train_imgs, train_digits,
+#           batch_size=128,
+#           epochs=100,
+#           validation_data=(test_imgs, test_digits))
+
+# img_idx = np.random.randint(phone_number_imgs.shape[0])
+# print(phone_number_strings[img_idx])
+# plt.imshow(phone_number_imgs[img_idx].reshape(28,280))

ThirdSaturday/Mnist_tf.py

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+
+# coding: utf-8
+
+# In[40]:
+
+from tensorflow.examples.tutorials.mnist import input_data
+
+
+# In[41]:
+
+mnist = input_data.read_data_sets('./mnist_data', one_hot=True)
+
+
+# In[42]:
+
+xtrain,xtest = mnist.train,mnist.test
+import tensorflow as tf
+# mnist.train.
+
+
+# In[43]:
+
+learning_rate = tf.constant(0.01,name='learning_rate')
+xtrain.images.shape[1]
+
+
+# In[44]:
+
+x = tf.placeholder(tf.float32, [None, 784])
+y = tf.placeholder(tf.float32, [None, 10])
+
+
+# In[45]:
+
+W1 = tf.Variable(tf.zeros([784, 512]),name='layer_1_weights')
+b1 = tf.Variable(tf.zeros([512]),name='bias_1_weights')
+W2 = tf.Variable(tf.zeros([512, 128]),name='layer_2_weights')
+b2 = tf.Variable(tf.zeros([128]),name='bias_2_weights')
+W_o = tf.Variable(tf.zeros([128, 10]),name='layer_output_weights')
+b_o = tf.Variable(tf.zeros([10]),name='bias_output_weights')
+
+
+# In[46]:
+
+layer_1 = tf.nn.relu(tf.add(tf.matmul(x,W1),b1))
+layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1,W2),b2))
+output_layer = tf.nn.softmax(tf.add(tf.matmul(layer_2,W_o),b_o))
+
+
+# In[47]:
+
+cross_entropy = tf.reduce_mean(-tf.reduce_sum(output_layer * tf.log(y), reduction_indices=[1]))
+
+
+# In[48]:
+
+train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
+
+
+# In[39]:
+
+with tf.Session() as s:
+    tf.global_variables_initializer()
+    [_,val] = s.run([train_step,cross_entropy],feed_dict={x:xtrain.images,y:xtrain.labels})
+    
+
+
+# In[ ]:
+
+
+

ThirdSaturday/Mnist_tf_relu.py

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+
+# coding: utf-8
+
+# In[1]:
+
+from tensorflow.examples.tutorials.mnist import input_data
+
+
+# In[2]:
+
+mnist = input_data.read_data_sets('./mnist_data', one_hot=True)
+
+
+# In[3]:
+
+xtrain,xtest = mnist.train,mnist.test
+import tensorflow as tf
+import math
+# mnist.train.
+
+
+# In[ ]:
+
+
+
+
+# In[28]:
+
+learning_rate = tf.constant(0.01,name='learning_rate')
+beta = tf.constant(0.01,name='regularization_beta')
+
+
+# In[5]:
+
+x = tf.placeholder(tf.float32, [None, xtrain.images.shape[1]])
+y = tf.placeholder(tf.float32, [None, 10])
+
+
+# In[6]:
+
+W1 = tf.Variable(tf.random_normal([784, 512],stddev=2.0/28.0),name='layer_1_weights')
+b1 = tf.Variable(tf.random_normal([512]),name='bias_1_weights')
+W2 = tf.Variable(tf.random_normal([512, 128],stddev=2.0/math.sqrt(512)),name='layer_2_weights')
+b2 = tf.Variable(tf.random_normal([128]),name='bias_2_weights')
+W_o = tf.Variable(tf.random_normal([128, 10],stddev=2.0/math.sqrt(128)),name='layer_output_weights')
+b_o = tf.Variable(tf.random_normal([10]),name='bias_output_weights')
+
+
+# In[20]:
+
+layer_1 = tf.nn.relu(tf.add(tf.matmul(x,W1),b1))
+layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1,W2),b2))
+#y_ = tf.nn.softmax(tf.add(tf.matmul(layer_2,W_o),b_o))+1e-6
+y_ = tf.add(tf.matmul(layer_2,W_o),b_o)
+
+
+# In[38]:
+
+#cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_)))
+cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=y_))
+total_loss  = cross_entropy+beta*(tf.nn.l2_loss(W1)+tf.nn.l2_loss(W2)+tf.nn.l2_loss(W_o)+tf.nn.l2_loss(b1)+tf.nn.l2_loss(b2)+tf.nn.l2_loss(b_o))
+correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(tf.nn.softmax(y_),1))
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+
+
+# In[39]:
+
+train_step = tf.train.GradientDescentOptimizer(0.1).minimize(total_loss)
+
+
+# In[40]:
+
+with tf.Session() as s:
+    tf.global_variables_initializer().run()
+    for i in range(20000):
+        batch_xs, batch_ys = xtrain.next_batch(100)
+        [_] = s.run([train_step],feed_dict={x:batch_xs,y:batch_ys})
+        if i%1000 == 0:
+            print(s.run(accuracy, feed_dict={x: xtest.images, y: xtest.labels}))
+
+
+# In[ ]:
+
+
+

ThirdSaturday/Notes.md

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+Parameters:
+        variables that are learnt by the model through training.
+
+
+HyperParameters:
+        variables that are empirical and have to be assigned manually.
+
+
+Protocol:
+  Train,Test,Validation/Dev Set
+  update HyperParameters and try training with the devset accuracy.
+  pick the best params.
+  Depending on the datasize and the nature of the problem(no of classes to be classified to) decide the test datasize
+
+  Error rate : (Bayes Error rate) lower possible error rate for any classifier of a random outcome.
+  (accuracy of the model shouldn't be more than this,
+
+Regularization:
+   if it is it means the model is overfitting to the training datas)
+   if the model is overfitting, use regularization to control it.
+   It is a technique to limit the expressiveness of the model.
+   eg.
+   1. L2 regularizer -> Loss' = Loss + lambda*Sum(wi^2) // lambda is the regularization param
+   makes |wi| =~= 0.
+   controls the degree of non-linearity of the model, without having to redesign the model
+   2. Dropout regularizer -> switching off some neurons
+      forcing the model learn from other features(neurons)

ThirdSaturday/keras_mnist.py

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+from keras.layers import Dense, Activation
+from keras.optimizers import RMSprop
+from keras.models import Sequential
+from keras import losses
+
+from keras.models import Sequential
+model = Sequential([Dense(units=64, input_dim=784),
+                    Activation('relu'),
+                    Dense(units=10),
+                    Activation('softmax')])
+
+model = Sequential([Dense(units=64, input_dim=784),
+                    Activation('relu'),
+                    Dense(units=10),
+                    Activation('softmax')])
+model.compile(optimizer=RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0), loss=losses.,metrics=['accuracy'])
+model.fit(xtrain.images,xtrain.labels,batch_size=10,epochs=10,validation_data=(xtest.images,xtest.labes))

ThirdSunday/FaceEyes.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import tensorflow as tf\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import matplotlib.pyplot as plt\n",
+    "import msgpack as msg\n",
+    "import msgpack_numpy as m\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from skimage.transform import resize\n",
+    "# from sklearn.color import rgb2gray\n",
+    "m.patch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def load_face_files():\n",
+    "    all_data = [msg.load(open('./face_images/face_images{}.bin'.format(i),'rb')) for i in range(1,6)]\n",
+    "    images = np.vstack([i[b'images'] for i in all_data])\n",
+    "    gray_images = np.dot(images,np.array([0.2125,0.7154,0.0721]))/255.0\n",
+    "#     print(gray_images.shape)\n",
+    "#     scaled_gray_images = resize(,(32,32))/255.0\n",
+    "#     import pdb;pdb.set_trace()\n",
+    "    coords = np.vstack([i[b'co-ords'] for i in all_data])\n",
+    "    coords_norm = coords/255.0\n",
+    "    return gray_images,coords_norm\n",
+    "\n",
+    "images,coords = load_face_files()\n",
+    "\n",
+    "def plot_image(idx,n=1):\n",
+    "    im = images[idx]\n",
+    "    part_coords = np.split(coords[idx],3)\n",
+    "    plt.figure(figsize=(16,16),dpi=80)\n",
+    "    plt.subplot(n,4,1)\n",
+    "    plt.imshow(im,'gray')\n",
+    "    for i in range(2,5):\n",
+    "        [x,y,w,h] = part_coords[i-2]#[:4]\n",
+    "        # print([x,y,w,h],all([i<0 for i in [x,y,w,h]]))\n",
+    "        if not all([j<0 for j in [x,y,w,h]]):\n",
+    "            plt.subplot(n,4,i)\n",
+    "            plt.imshow(im[y:y+h,x:x+w],'gray')\n",
+    "\n",
+    "\n",
+    "def get_head_images(c):\n",
+    "    h_idx = []\n",
+    "    for (idx,i) in enumerate(c):\n",
+    "        head_coords = np.split(i,3)[0]\n",
+    "        if not any([j<0 for j in head_coords]):\n",
+    "            h_idx.append(idx)\n",
+    "    return h_idx\n",
+    "\n",
+    "# plot_image(958)\n",
+    "\n",
+    "head_idxs = get_head_images(coords)\n",
+    "head_images = images[head_idxs].reshape(-1,images.shape[1]*images.shape[2])\n",
+    "head_coords = coords[head_idxs,:4].astype(np.float32)\n",
+    "tr_head_imgs,te_head_imgs,tr_head_crds,te_head_crds = train_test_split(head_images,head_coords,test_size=0.33)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def create_model(input_dim,output_dim):\n",
+    "    g = tf.Graph()\n",
+    "    with g.as_default():\n",
+    "        learning_rate = tf.constant(0.1,name='learning_rate')\n",
+    "        beta = tf.constant(0.00001,name='regularization_beta')\n",
+    "        error_upper_bound = tf.constant(1.1,name='upper_bound')\n",
+    "        error_lower_bound = tf.constant(0.9,name='lower_bound')\n",
+    "        x = tf.placeholder(tf.float32, [None,input_dim])\n",
+    "        y = tf.placeholder(tf.float32, [None,output_dim])\n",
+    "        W1 = tf.Variable(tf.random_normal([input_dim, 512],stddev=2.0/math.sqrt(input_dim)),name='layer_1_weights')\n",
+    "        b1 = tf.Variable(tf.random_normal([512]),name='bias_1_weights')\n",
+    "        W2 = tf.Variable(tf.random_normal([512, 128],stddev=2.0/math.sqrt(512)),name='layer_2_weights')\n",
+    "        b2 = tf.Variable(tf.random_normal([128]),name='bias_2_weights')\n",
+    "        W_o = tf.Variable(tf.random_normal([128, output_dim],stddev=2.0/math.sqrt(128)),name='layer_output_weights')\n",
+    "        b_o = tf.Variable(tf.random_normal([output_dim]),name='bias_output_weights')\n",
+    "        layer_1 = tf.nn.relu(tf.add(tf.matmul(x,W1),b1))\n",
+    "        layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1,W2),b2))\n",
+    "        y_ = tf.nn.sigmoid(tf.add(tf.matmul(layer_2,W_o),b_o))\n",
+    "        distance = tf.losses.mean_squared_error(labels = y,predictions = y_)\n",
+    "        regularized_loss  = distance+beta*(tf.nn.l2_loss(W1)+tf.nn.l2_loss(W2)+tf.nn.l2_loss(W_o)+tf.nn.l2_loss(b1)+tf.nn.l2_loss(b2)+tf.nn.l2_loss(b_o))\n",
+    "        ratio = tf.div(y,y_)\n",
+    "        accuracy = distance#tf.reduce_mean(tf.cast((ratio < error_upper_bound) & (ratio > error_lower_bound), tf.float32))\n",
+    "        tf.summary.scalar('distance', distance)\n",
+    "        tf.summary.histogram('Weights1', W1)\n",
+    "        tf.summary.histogram('Bias1', b1)\n",
+    "        tf.summary.histogram('Weights2', W2)\n",
+    "        tf.summary.histogram('Bias2', b2)\n",
+    "        tf.summary.histogram('Weights_output', W_o)\n",
+    "        tf.summary.histogram('Bias_output', b_o)\n",
+    "        merged_summary = tf.summary.merge_all()\n",
+    "        train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(regularized_loss)\n",
+    "        return (g,x,y,y_,train_step,accuracy,merged_summary)\n",
+    "\n",
+    "(g,x,y,y_,train_step,accuracy,merged_summary) = create_model(tr_head_imgs.shape[1],tr_head_crds.shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "distance on validation set 0.0457288585603\n",
+      "distance on validation set 0.0384670570493\n",
+      "distance on validation set 0.0463402196765\n",
+      "distance on validation set 0.0418722033501\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-56-ee62be87709e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m                     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"distance on validation set {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#,'saved to ',save_path)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmerged_summary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-56-ee62be87709e>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(g, x, y, y_, train_step, accuracy, merged_summary)\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0mbatch_xs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_ys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_head_imgs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtr_head_crds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m                 \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbatch_xs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbatch_ys\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;36m100\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m                     \u001b[0mt_batch_xs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt_batch_ys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_head_imgs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtr_head_crds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "def batch_data(data_x,data_y,size=128):\n",
+    "    batch_idxs = np.random.randint(0,data_x.shape[0],size=size)\n",
+    "    return (data_x[batch_idxs],data_y[batch_idxs])\n",
+    "\n",
+    "def train_model(g,x,y,y_,train_step,accuracy,merged_summary):\n",
+    "    with g.as_default():\n",
+    "        with tf.Session() as s:\n",
+    "            train_writer = tf.summary.FileWriter('./tensor_log',s.graph)\n",
+    "            tf.global_variables_initializer().run()\n",
+    "#             saver = tf.train.Saver()\n",
+    "            # saver.restore(s, \"/tmp/model.ckpt\")\n",
+    "            for i in range(20000):\n",
+    "                batch_xs,batch_ys = batch_data(tr_head_imgs,tr_head_crds,128)\n",
+    "                s.run([train_step],feed_dict={x:batch_xs,y:batch_ys})\n",
+    "                if i%100 == 0:\n",
+    "                    t_batch_xs,t_batch_ys = batch_data(tr_head_imgs,tr_head_crds,32)\n",
+    "                    [acc]= s.run([accuracy], feed_dict={x: t_batch_xs,y: t_batch_ys})\n",
+    "                   # train_writer.add_summary(summary,i)\n",
+    "#                     save_path = saver.save(s, \"/tmp/model.ckpt\")\n",
+    "#                     print(y_vals,t_batch_ys)\n",
+    "                    print(\"distance on validation set {}\".format(acc))#,'saved to ',save_path)\n",
+    "\n",
+    "train_model(g,x,y,y_,train_step,accuracy,merged_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

ThirdSunday/Faces.py

@@ -0,0 +1,121 @@
+#%matplotlib inline
+import tensorflow as tf
+import pandas as pd
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import msgpack as msg
+import msgpack_numpy as m
+from skimage.transform import resize
+from sklearn.model_selection import train_test_split
+# from sklearn.color import rgb2gray
+m.patch()
+
+
+
+def load_face_files():
+    all_data = [msg.load(open('./face_images/face_images{}.bin'.format(i),'rb')) for i in range(1,6)]
+    images = np.vstack([i[b'images'] for i in all_data])
+    gray_images = np.dot(images,np.array([0.2125,0.7154,0.0721]))
+    coords = np.vstack([i[b'co-ords'] for i in all_data])
+    return gray_images,coords
+
+images,coords = load_face_files()
+
+def plot_image(idx,n=1):
+    im = images[idx]
+    part_coords = np.split(coords[idx],3)
+    plt.figure(figsize=(16,16),dpi=80)
+    plt.subplot(n,4,1)
+    plt.imshow(im,'gray')
+    for i in range(2,5):
+        [x,y,w,h] = part_coords[i-2]#[:4]
+        # print([x,y,w,h],all([i<0 for i in [x,y,w,h]]))
+        if not all([j<0 for j in [x,y,w,h]]):
+            plt.subplot(n,4,i)
+            plt.imshow(im[y:y+h,x:x+w],'gray')
+
+
+def get_head_images(c):
+    h_idx = []
+    for (idx,i) in enumerate(c):
+        head_coords = np.split(i,3)[0]
+        if not any([j<0 for j in head_coords]):
+            h_idx.append(idx)
+    return h_idx
+
+# plot_image(958)
+
+head_idxs = get_head_images(coords)
+head_images = images[head_idxs].reshape(-1,images.shape[1]*images.shape[2])
+head_coords = coords[head_idxs,:4].astype(np.float32)
+tr_head_imgs,te_head_imgs,tr_head_crds,te_head_crds = train_test_split(head_images,head_coords,test_size=0.33)
+
+
+def create_model(input_dim,output_dim):
+    g = tf.Graph()
+    with g.as_default():
+        learning_rate = tf.constant(0.01,name='learning_rate')
+        beta = tf.constant(0.001,name='regularization_beta')
+        error_upper_bound = tf.constant(1.1,name='upper_bound')
+        error_lower_bound = tf.constant(0.9,name='lower_bound')
+        x = tf.placeholder(tf.float32, [None,input_dim])
+        y = tf.placeholder(tf.float32, [None,output_dim])
+        W1 = tf.Variable(tf.random_normal([input_dim, 512],stddev=2.0/math.sqrt(input_dim)),name='layer_1_weights')
+        b1 = tf.Variable(tf.random_normal([512]),name='bias_1_weights')
+        W2 = tf.Variable(tf.random_normal([512, 128],stddev=2.0/math.sqrt(512)),name='layer_2_weights')
+        b2 = tf.Variable(tf.random_normal([128]),name='bias_2_weights')
+        W_o = tf.Variable(tf.random_normal([128, output_dim],stddev=2.0/math.sqrt(128)),name='layer_output_weights')
+        b_o = tf.Variable(tf.random_normal([output_dim]),name='bias_output_weights')
+        layer_1 = tf.nn.relu(tf.add(tf.matmul(x,W1),b1))
+        layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,W2),b2))
+        #y_ = tf.nn.softmax(tf.add(tf.matmul(layer_2,W_o),b_o))+1e-6
+        y_ = tf.add(tf.matmul(layer_2,W_o),b_o)#tf.nn.relu()
+
+        #cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_)))
+        # cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=y_))
+        # distance = tf.reduce_sum(tf.square(tf.subtract(y,y_)))
+        distance = tf.losses.mean_squared_error(labels = y,predictions = y_)
+        regularized_loss  = distance+beta*(tf.nn.l2_loss(W1)+tf.nn.l2_loss(W2)+tf.nn.l2_loss(W_o)+tf.nn.l2_loss(b1)+tf.nn.l2_loss(b2)+tf.nn.l2_loss(b_o))
+        # correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(tf.nn.softmax(y_),1))
+        ratio = tf.div(y,y_)
+        accuracy = tf.reduce_mean(tf.cast((ratio < error_upper_bound) & (ratio > error_lower_bound), tf.float32))
+        tf.summary.scalar('distance', distance)
+        tf.summary.histogram('Weights1', W1)
+        tf.summary.histogram('Bias1', b1)
+        tf.summary.histogram('Weights2', W2)
+        tf.summary.histogram('Bias2', b2)
+        tf.summary.histogram('Weights_output', W_o)
+        tf.summary.histogram('Bias_output', b_o)
+        merged_summary = tf.summary.merge_all()
+        train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(regularized_loss)
+        return (g,x,y,y_,train_step,accuracy,merged_summary)
+
+(g,x,y,y_,train_step,accuracy,merged_summary) = create_model(tr_head_imgs.shape[1],tr_head_crds.shape[1])
+
+def batch_data(data_x,data_y,size=128):
+    batch_idxs = np.random.randint(0,data_x.shape[0],size=size)
+    return (data_x[batch_idxs],data_y[batch_idxs])
+
+def train_model(g,x,y,y_,train_step,accuracy,merged_summary):
+    with g.as_default():
+        with tf.Session() as s:
+            train_writer = tf.summary.FileWriter('./tensor_log',s.graph)
+            tf.global_variables_initializer().run()
+            saver = tf.train.Saver()
+            # saver.restore(s, "/tmp/model.ckpt")
+            for i in range(20000):
+                batch_xs,batch_ys = batch_data(tr_head_imgs,tr_head_crds,10)
+                s.run([train_step],feed_dict={x:batch_xs,y:batch_ys})
+                if i%100 == 0:
+                    t_batch_xs,t_batch_ys = batch_data(tr_head_imgs,tr_head_crds,5)
+                    [summary,acc,y_vals]= s.run([merged_summary,accuracy,y_], feed_dict={x: t_batch_xs,y: t_batch_ys})
+                    train_writer.add_summary(summary,i)
+                    save_path = saver.save(s, "/tmp/model.ckpt")
+                    print(y_vals,t_batch_ys)
+                    print("Accuracy on validation set {}".format(acc))#,'saved to ',save_path)
+
+
+bb = np.array([[100,200,300,400], [100,200,300,400], [100,200,300,400], [100,200,300,400]])
+bb[:,1]
+train_model(g,x,y,y_,train_step,accuracy,merged_summary)

ThirdSunday/Mnist.py

@@ -0,0 +1,73 @@
+
+# coding: utf-8
+
+# In[1]:
+
+from tensorflow.examples.tutorials.mnist import input_data
+import tensorflow as tf
+import math
+
+
+# In[2]:
+
+mnist = input_data.read_data_sets('./mnist_data', one_hot=True)
+
+
+# In[3]:
+
+learning_rate = tf.constant(0.01,name='learning_rate')
+beta = tf.constant(0.001,name='regularization_beta')
+x = tf.placeholder(tf.float32, [None, mnist.train.images.shape[1]])
+y = tf.placeholder(tf.float32, [None, 10])
+W1 = tf.Variable(tf.random_normal([784, 512],stddev=2.0/28.0),name='layer_1_weights')
+b1 = tf.Variable(tf.random_normal([512]),name='bias_1_weights')
+W2 = tf.Variable(tf.random_normal([512, 128],stddev=2.0/math.sqrt(512)),name='layer_2_weights')
+b2 = tf.Variable(tf.random_normal([128]),name='bias_2_weights')
+W_o = tf.Variable(tf.random_normal([128, 10],stddev=2.0/math.sqrt(128)),name='layer_output_weights')
+b_o = tf.Variable(tf.random_normal([10]),name='bias_output_weights')
+layer_1 = tf.nn.relu(tf.add(tf.matmul(x,W1),b1))
+layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1,W2),b2))
+#y_ = tf.nn.softmax(tf.add(tf.matmul(layer_2,W_o),b_o))+1e-6
+y_ = tf.add(tf.matmul(layer_2,W_o),b_o)
+
+
+# In[4]:
+
+#cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_)))
+cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=y_))
+total_loss  = cross_entropy+beta*(tf.nn.l2_loss(W1)+tf.nn.l2_loss(W2)+tf.nn.l2_loss(W_o)+tf.nn.l2_loss(b1)+tf.nn.l2_loss(b2)+tf.nn.l2_loss(b_o))
+correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(tf.nn.softmax(y_),1))
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+tf.summary.scalar('cross_entropy', cross_entropy)
+tf.summary.histogram('Weights1', W1)
+tf.summary.histogram('Bias1', b1)
+tf.summary.histogram('Weights2', W2)
+tf.summary.histogram('Bias2', b2)
+tf.summary.histogram('Weights_output', W_o)
+tf.summary.histogram('Bias_output', b_o)
+train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)
+#saver = tf.train.Saver()
+
+
+# In[6]:
+
+with tf.Session() as s:
+#     merged = tf.summary.merge_all()
+#     train_writer = tf.summary.FileWriter('./train',s.graph)
+    tf.global_variables_initializer().run()
+#     saver.restore(s, "/tmp/model.ckpt")
+#     print("Model restored.")
+    for i in range(20000):
+        batch_xs, batch_ys = mnist.train.next_batch(128)
+        s.run([train_step],feed_dict={x:batch_xs,y:batch_ys})
+        if i%1000 == 0:
+            [acc]= s.run([accuracy], feed_dict={x: mnist.test.images, y: mnist.test.labels})
+            train_writer.add_summary(summary,i)
+#             save_path = saver.save(s, "/tmp/model.ckpt")
+            print("Accuracy on validation set {}".format(acc))#,'saved to ',save_path)
+
+
+# In[ ]:
+
+
+