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updated to third week

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Malar Kannan 2017-10-23 11:05:35 +05:30
parent 6c62795d02
commit 2c91fa6eb5
8 changed files with 643 additions and 14 deletions
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39
SecondSunday/mnist_phone_number.py
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@ -1,16 +1,18 @@
%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 = 100
phone_number_digits = np.random.randint(10**9,10**10,size=(DATA_COUNT,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 = 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])
@ -27,20 +29,29 @@ def create_phone_images(phone_array):
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(10),
Activation('linear'),
])
# model = Sequential([
# Dense(32, input_shape=(7840,)),
# Activation('relu'),
# # Dense(24, input_shape=(32,)),
# # Activation('relu'),
# Dense(10),
# Activation('linear'),
# ])
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
# 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))
model.fit()
# plt.imshow(phone_number_imgs[np.random.randint(phone_number_imgs.shape[0])])
# 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))

71
ThirdSaturday/Mnist_tf.py Normal file
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@ -0,0 +1,71 @@
# 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[ ]:

85
ThirdSaturday/Mnist_tf_relu.py Normal file
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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[ ]:

27
ThirdSaturday/Notes.md Normal file
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@ -0,0 +1,27 @@
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)

17
ThirdSaturday/keras_mnist.py Normal file
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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))

224
ThirdSunday/FaceEyes.ipynb Normal file
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@ -0,0 +1,224 @@
{
"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",
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"\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)"
]
},
{
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121
ThirdSunday/Faces.py Normal file
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#%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)

73
ThirdSunday/Mnist.py Normal file
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# 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[ ]: