├── LICENSE ├── README.md ├── simdata ├── linear_data_eval.csv ├── linear_data_train.csv └── linear_data_train.jpg └── softmax.py /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. 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Real examples do [exist](https://openfmri.org/dataset/) but, too many features to sift through for a short video. 6 | 7 | ##Dependencies 8 | 9 | * tensorflow (https://www.tensorflow.org/get_started/os_setup) 10 | * numpy (pip install numpy) 11 | 12 | 13 | ##Demo 14 | 15 | Run the following in terminal 16 | ``` 17 | $ python softmax.py --train simdata/linear_data_train.csv --test simdata/linear_data_eval.csv --num_epochs 5 --verbose True 18 | ``` 19 | 20 | Add your own test data to test the model out. 21 | 22 | ##Challenge 23 | The challenge for this video is to create a pokemon classifier by their type 1 (i.e fire, water, grass, etc.) using [this](https://www.kaggle.com/abcsds/pokemon) pokemon dataset on Kaggle. It will be great practice in data preparation (feature selection, cleaning, etc.) Post your github link in the comments and i'll announce the winner in the next video. Due date is December 22nd at Noon PST. 24 | 25 | ##Credits 26 | 27 | Credits go to [Jason Baldridge](https://github.com/jasonbaldridge). I've merely created a wrapper to get people started. 28 | -------------------------------------------------------------------------------- /simdata/linear_data_eval.csv: -------------------------------------------------------------------------------- 1 | 0,0.147562141324833,0.243518270820358 2 | 0,0.179868989766322,0.0922537025547999 3 | 1,0.754244045840797,0.52387485552728 4 | 0,0.248663780798734,0.175587276306351 5 | 0,0.397217489998824,0.0342134948381493 6 | 0,0.45098160780959,0.0328858982745571 7 | 0,0.335532917252522,0.16654442982869 8 | 0,0.371372049777255,0.167201755443297 9 | 0,0.280985655458144,0.214982885821991 10 | 0,0.313304894476342,0.00521976760984659 11 | 1,0.638465839375771,0.59044662666132 12 | 0,0.431591142988843,0.0470830726734468 13 | 0,0.207774186228136,0.0819718701225306 14 | 1,0.74160948940248,0.48471276227826 15 | 1,0.953514363329195,0.639625829881579 16 | 1,0.642944532413742,0.561453314573865 17 | 0,0.226494744243105,0.054182457565834 18 | 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-------------------------------------------------------------------------------- 1 | import tensorflow.python.platform 2 | 3 | import numpy as np 4 | import tensorflow as tf 5 | 6 | # Global variables. 7 | NUM_LABELS = 2 # The number of labels. 8 | BATCH_SIZE = 100 # The number of training examples to use per training step. 9 | 10 | # Define the flags useable from the command line. 11 | tf.app.flags.DEFINE_string('train', None, 12 | 'File containing the training data (labels & features).') 13 | tf.app.flags.DEFINE_string('test', None, 14 | 'File containing the test data (labels & features).') 15 | tf.app.flags.DEFINE_integer('num_epochs', 1, 16 | 'Number of examples to separate from the training ' 17 | 'data for the validation set.') 18 | tf.app.flags.DEFINE_boolean('verbose', False, 'Produce verbose output.') 19 | FLAGS = tf.app.flags.FLAGS 20 | 21 | # Extract numpy representations of the labels and features given rows consisting of: 22 | # label, feat_0, feat_1, ..., feat_n 23 | def extract_data(filename): 24 | 25 | # Arrays to hold the labels and feature vectors. 26 | labels = [] 27 | fvecs = [] 28 | 29 | # Iterate over the rows, splitting the label from the features. Convert labels 30 | # to integers and features to floats. 31 | for line in file(filename): 32 | row = line.split(",") 33 | labels.append(int(row[0])) 34 | fvecs.append([float(x) for x in row[1:]]) 35 | 36 | # Convert the array of float arrays into a numpy float matrix. 37 | fvecs_np = np.matrix(fvecs).astype(np.float32) 38 | 39 | # Convert the array of int labels into a numpy array. 40 | labels_np = np.array(labels).astype(dtype=np.uint8) 41 | 42 | # Convert the int numpy array into a one-hot matrix. 43 | labels_onehot = (np.arange(NUM_LABELS) == labels_np[:, None]).astype(np.float32) 44 | 45 | # Return a pair of the feature matrix and the one-hot label matrix. 46 | return fvecs_np,labels_onehot 47 | 48 | def main(argv=None): 49 | # Be verbose? 50 | verbose = FLAGS.verbose 51 | 52 | # Get the data. 53 | train_data_filename = FLAGS.train 54 | test_data_filename = FLAGS.test 55 | 56 | # Extract it into numpy matrices. 57 | train_data,train_labels = extract_data(train_data_filename) 58 | test_data, test_labels = extract_data(test_data_filename) 59 | 60 | # Get the shape of the training data. 61 | train_size,num_features = train_data.shape 62 | 63 | # Get the number of epochs for training. 64 | num_epochs = FLAGS.num_epochs 65 | 66 | # This is where training samples and labels are fed to the graph. 67 | # These placeholder nodes will be fed a batch of training data at each 68 | # training step using the {feed_dict} argument to the Run() call below. 69 | x = tf.placeholder("float", shape=[None, num_features]) 70 | y_ = tf.placeholder("float", shape=[None, NUM_LABELS]) 71 | 72 | # For the test data, hold the entire dataset in one constant node. 73 | test_data_node = tf.constant(test_data) 74 | 75 | # Define and initialize the network. 76 | 77 | # These are the weights that inform how much each feature contributes to 78 | # the classification. 79 | W = tf.Variable(tf.zeros([num_features,NUM_LABELS])) 80 | b = tf.Variable(tf.zeros([NUM_LABELS])) 81 | y = tf.nn.softmax(tf.matmul(x,W) + b) 82 | 83 | # Optimization. 84 | cross_entropy = -tf.reduce_sum(y_*tf.log(y)) 85 | train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) 86 | 87 | # Evaluation. 88 | correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 89 | accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 90 | 91 | # Create a local session to run this computation. 92 | with tf.Session() as s: 93 | # Run all the initializers to prepare the trainable parameters. 94 | tf.initialize_all_variables().run() 95 | if verbose: 96 | print 'Initialized!' 97 | print 98 | print 'Training.' 99 | 100 | # Iterate and train. 101 | for step in xrange(num_epochs * train_size // BATCH_SIZE): 102 | if verbose: 103 | print step, 104 | 105 | offset = (step * BATCH_SIZE) % train_size 106 | batch_data = train_data[offset:(offset + BATCH_SIZE), :] 107 | batch_labels = train_labels[offset:(offset + BATCH_SIZE)] 108 | train_step.run(feed_dict={x: batch_data, y_: batch_labels}) 109 | 110 | if verbose and offset >= train_size-BATCH_SIZE: 111 | print 112 | 113 | # Give very detailed output. 114 | if verbose: 115 | print 116 | print 'Weight matrix.' 117 | print s.run(W) 118 | print 119 | print 'Bias vector.' 120 | print s.run(b) 121 | print 122 | print "Applying model to first test instance." 123 | first = test_data[:1] 124 | print "Point =", first 125 | print "Wx+b = ", s.run(tf.matmul(first,W)+b) 126 | print "softmax(Wx+b) = ", s.run(tf.nn.softmax(tf.matmul(first,W)+b)) 127 | print 128 | 129 | print "Accuracy:", accuracy.eval(feed_dict={x: test_data, y_: test_labels}) 130 | 131 | 132 | if __name__ == '__main__': 133 | tf.app.run() 134 | --------------------------------------------------------------------------------